Sistemas de negociação alternativos com formatos

Sistemas de negociação alternativos com formatos

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Sistemas de negociação de corridas de cavalos
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Regulamento SCI (Conformidade e Integridade dos Sistemas de Regulação) Obtenha informações detalhadas sobre o desempenho do servidor com a infraestrutura convergente - Dell EMC facilita a conformidade com o gerenciamento do ciclo de vida de dados & ndash; Iron Mountain Veja Mais. Conformidade e integridade dos sistemas de regulamentação (Regulation SCI) é um conjunto de regras criadas pela Comissão de Valores Mobiliários dos Estados Unidos para monitorar a segurança e as capacidades da infra-estrutura tecnológica dos mercados de valores dos EUA. Download: Principais perguntas frequentes sobre o impacto de quatro regulamentações comuns de conformidade. Às vezes, as operações de TI são inesperadamente afetadas pelos principais regulamentos de auditoria - sua equipe de TI está preparada? Explore o papel fundamental que sua equipe de TI desempenha na garantia da conformidade e analise as penalidades para a não conformidade baixando este guia eletrônico GRATUITO, que abrange todas as dúvidas que você possa ter em relação a quatro importantes regulamentações legislativas. Ao enviar suas informações pessoais, você concorda que a TechTarget e seus parceiros podem entrar em contato com você sobre conteúdo relevante, produtos e ofertas especiais. Você também concorda que suas informações pessoais podem ser transferidas e processadas nos Estados Unidos, e que você leu e concorda com os Termos de Uso e a Política de Privacidade. A SEC projetou a Regulation SCI em resposta aos mercados de títulos sendo cada vez mais dependentes de tecnologia e sistemas automatizados. Regulamentação A SCI se esforça para reduzir o número de distúrbios de mercado decorrentes dessa dependência de tecnologia, bem como acelerar a recuperação quando ocorrem perturbações. Estas perturba�es, conhecidas como "eventos SCI" sob o regulamento, incluem interrupções de sistemas, problemas de conformidade e intrusões de segurança. O regulamento SCI é obrigatório para o que o SEC se refere como “entidades de SCI.” Entidades da SCI incluem organizações de auto-regulação, processadores de planos, agências de compensação e alguns sistemas alternativos de negociação (ATSes). Sob a regra, as entidades da SCI devem projetar, implementar, testar e manter políticas e procedimentos de TI para a capacidade, integridade, resiliência, disponibilidade e segurança de seus sistemas. Se ocorrer um evento SCI, a entidade SCI deve tomar imediatamente uma ação corretiva, bem como notificar a SEC da ocorrência. As entidades da SCI também devem notificar a SEC quando planejarem fazer alterações em seus sistemas de TI. Para ajudar a garantir a conformidade, as entidades da SCI devem realizar revisões anuais de seus processos de regulamentação da SCI e enviar o relatório à SEC. A Regulation SCI também exige que as entidades da SCI mantenham registros de conformidade de TI para comprovar a aderência às regras. O regulamento SCI foi aprovado em fevereiro de 2015. O regulamento entrou oficialmente em vigor no início de novembro de 2015. Nota do editor: Em outubro de 2015, a SEC atualizou sua página de Perguntas frequentes sobre a Regulamentação SCI, abordando dois pontos. Primeiro, aborda se os ATS podem ter sistemas de vigilância de regulação de mercado. Segundo a definição da Reg SCI de sistemas SCI, as ATS que atendem ao limite de volume da regulamentação são consideradas entidades SCI. No entanto, no contexto da Reg SCI, a SEC disse que os sistemas de regulação do mercado se referem apenas àqueles usados ​​para realizar responsabilidades auto-regulatórias, que as ATSs não possuem. Assim, a SEC acredita que é improvável que um ATS tenha sistemas que se qualifiquem como sistemas de regulação do mercado. Em segundo lugar, o FAQ foi atualizado para esclarecer quais os sistemas SCI que se relacionam com a comunicação de & quot; interrupções comerciais & quot; s� considerados "sistema SCI cr�ico". Em primeiro lugar, a SEC define as paradas de negociação à medida que as paradas de mercado (por exemplo, paralisações regulatórias), em vez de as paradas de negociação em um mercado individual. Dada esta definição, os sistemas críticos de SCI são definidos pela Regulation SCI como qualquer sistema SCI que seja operado por ou em nome de uma entidade SCI que suporte diretamente a funcionalidade relacionada a interrupções comerciais e que dissemine comunicações relacionadas a interrupções comerciais em todo o mercado entre mercados . Continue lendo sobre o Regulamento SCI (Conformidade e Integridade dos Sistemas de Regulação) Termos relacionados. Saiba mais sobre os requisitos específicos do setor para conformidade. Os regulamentos dos drones evoluem à medida que a adoção da empresa se aquece. A indústria de drones encontra aliado, cão de guarda na FAA. Os regulamentos de segurança da informação podem ter como alvo o IoT, drones. FTC (Federal Trade Commission) A indústria de drones encontra aliado, cão de guarda na FAA. O Snapchat observa o mercado de hardware com novos óculos de gravação de vídeo. A supervisão da SEC atinge novos níveis sob o Regulamento SCI. Os novos regulamentos de neutralidade da rede estimularão o investimento e a inovação? Os regulamentos de segurança da informação podem ter como alvo o IoT, drones. FTC (Federal Trade Commission) As propostas da FCC continuam a estimular o debate sobre a neutralidade da rede. Como o FTC buscou melhorias na privacidade e segurança de dados? Os regulamentos dos drones evoluem à medida que a adoção da empresa se aquece. A indústria de drones encontra aliado, cão de guarda na FAA. Os regulamentos de segurança da informação podem ter como alvo o IoT, drones. FAQ: O projeto de lei exigirá o acesso a informações criptografadas? FTC: Analisar big data cria risco de discriminação. A influência do dispositivo móvel nos mandatos de conformidade regulamentar. FAQ: As empresas podem combater pedidos de informações de vigilância secreta? Novas e não tão novas mudanças na segurança no Ato de Segurança Cibernética de 2012. Como o FTC objetivou melhorias na privacidade e segurança de dados? A influência do dispositivo móvel nos mandatos de conformidade regulamentar. FAQ: As empresas podem combater pedidos de informações de vigilância secreta? Novas e não tão novas mudanças na segurança no Ato de Segurança Cibernética de 2012. Quais serão as maiores mudanças para as empresas que têm que cumprir o Regulamento SCI? Participe da conversa. Sua senha foi enviada para: Ao enviar você concorda em receber e-mails da TechTarget e seus parceiros. Se você reside fora dos Estados Unidos, você consente que seus dados pessoais sejam transferidos e processados ​​nos Estados Unidos. Privacidade. Por favor, crie um nome de usuário para comentar. -ANÚNCIOS DO GOOGLE. Extensões de arquivos e formatos de arquivo. Recursos mais recentes do TechTarget. Pesquisar CIO. CISOs, dê ao seu programa de segurança cibernética um senso de propósito. "Derrote o inimigo que você pode ver ... então prepare-se para o próximo compromisso." Phillip Miller, da Brooks Brothers, dá aos colegas CISOs uma novidade. Quem está falando? Agente conversacional vs. chatbot vs. assistente virtual. Pense um agente de conversação, chatbot e assistente virtual são os mesmos? Pense de novo. O Vice-Presidente e CTO da IBM Watson, Rob High, explica. Neurala afirma que “redes neurais profundas ao longo da vida” não esquecem. A startup de Boston Neurala diz que desenvolveu redes neurais profundas que podem aprender na hora. O COO de Neurala, Heather Ames, explica. Pesquise em TI de saúde. Ao implementar o VDI, observe os aprimoramentos de armazenamento. Hospitais que analisam a infraestrutura de desktops virtuais podem obter um aliado em recursos aprimorados de armazenamento. Armazenamento Flash, em. AI um grande benefício de dados para mudar para a nuvem para cuidados de saúde. AI um grande benefício de dados para mudar para a nuvem para cuidados de saúde. Os provedores e grandes fornecedores são cautelosos, mas os cuidados de saúde na nuvem e os benefícios que a inteligência artificial proporciona estimularão um. Pesquisar Cloud Computing. A aquisição da VMware continua em direção à segurança na nuvem. As ferramentas de segurança em nuvem da VMware serão impulsionadas pela aquisição da CloudCoreo, uma startup de segurança e gerenciamento da empresa. A automação da liberação de aplicativos é transferida para a nuvem. As iniciativas de CI / CD estimularão o aumento da adoção de ferramentas de automação de lançamento de aplicativos este ano, incluindo aquelas hospedadas na nuvem,. Os desafios de autoatendimento do usuário são montados em computação em várias nuvens. O provisionamento de autoatendimento apresenta desafios com um único provedor de nuvem, e uma estratégia de várias nuvens apenas os amplia. Data Center de pesquisa. Avalie casos de uso de leitura intensiva de leitura e SSD com uso intensivo de gravação. Considere escrever desgaste, desempenho e outros fatores ao escolher entre leitura intensiva, gravação intensiva e uso misto. Alguns casos de uso de infraestrutura hiperconvergente apresentam armadilhas. A adoção de infraestrutura hiperconvergente está aumentando vertiginosamente, mas isso não significa que a tecnologia seja a melhor opção para todos. O reorg hiper-convergente da Dell simplifica os produtos, aumenta as probabilidades de CI. As pressões do mercado e as sinergias de fabricação levaram a Dell a integrar seus produtos HCI e CI com suas principais unidades de negócios, mas. Gerenciamento de dados de pesquisa. O Hyperledger Fabric oferece um caminho para o futuro do blockchain corporativo. Blockchain surgiu de bitcoin, mas está procurando um lugar na empresa. Estruturas como o Hyperledger Fabric podem. O MongoDB 4.0 leva as transações do ACID ao nível de vários documentos. O MongoDB está dando um passo mais profundo nas águas de processamento no estilo SQL com uma atualização 4.0 que traz suporte aprimorado para. O conceito de data lake precisa de uma mão firme para pagar dividendos de big data. Os lagos de dados representam desafios de implantação de tecnologia e gerenciamento de dados que podem deixar os usuários de análise altos e secos se o. Segurança de pesquisa. O malware do Destruidor Olímpico é mais complexo do que se pensava inicialmente. Roundup de notícias: O malware do Destroyer Olímpico é mais sofisticado do que os pesquisadores pensavam. Além disso, a Microsoft parece mudar. SonicWall detecta quebras de Meltdown com tecnologia de aprendizado de máquina. A SonicWall diz que sua nova tecnologia de inspeção de memória profunda, que alimenta o serviço de sandbox do Capture Cloud, pode bloquear. Os programas de recompensas de bugs da Intel aumentaram após o Meltdown e o Specter. O programa de recompensas de bugs da Intel expandiu seu escopo e recompensas por bugs em todos os produtos da Intel, e a empresa adicionou um novo programa. Todos os direitos reservados, Copyright 2009 - 2018, TechTarget. Avaliação de SOAP para aplicativos de negócios de alto desempenho: sistemas de negociação em tempo real. Tenermerx Pty Ltd. 6/50 Ben Boyd Rd, Baía Neutra. NSW 2089 Austrália. chris @ tenermerx. Robert Steele Universidade de Tecnologia, Sydney. Caixa Postal 123 Broadway. NSW 2007 Austrália. rsteele@it.uts.edu.au Os serviços da Web, com ênfase em padrões abertos e flexibilidade, podem fornecer benefícios em relação às práticas de integração existentes nos mercados de capitais. No entanto, os serviços da Web devem primeiro atender a determinados requisitos técnicos, incluindo desempenho, segurança e tolerância a falhas. Este artigo apresenta uma avaliação experimental do desempenho do SOAP usando o conteúdo realístico da mensagem do aplicativo de negócios. Para obter alguma indicação de se o SOAP é apropriado para sistemas de mercados de capital de alto desempenho, os resultados são comparados com um protocolo existente amplamente utilizado. O estudo conclui que, embora o SOAP tenha um desempenho relativamente ruim, a diferença é menor do que em ambientes de computação científica. Além disso, descobrimos que em aplicativos de negócios realistas é possível que os formatos de fios baseados em texto tenham desempenho comparável ao binário e que a natureza baseada em texto do XML não seja suficiente para explicar a ineficiência do SOAP. Isso sugere que trabalhos adicionais podem permitir que o SOAP se torne um formato de ligação viável para aplicativos de negócios de alto desempenho. Estudo de desempenho, SOAP, serviços da Web, FIX. Introdução. Na última década, os rápidos avanços na tecnologia de computação provocaram mudanças drásticas no setor financeiro. O fornecimento de serviços on-line, como o comércio de ações em tempo real, levou a uma mudança para novos modelos de negócios, como mercados globais, negociações de 24 horas e processos diretos [21]. O enorme crescimento do número de redes financeiras de terceiros aumentou a concorrência e acelerou a corrida para desenvolver sistemas de negociação automatizados e avançados [7]. Para competir efetivamente, as organizações existentes precisam formar alianças e fornecer serviços integrados. No entanto, a integração business-to-business não é um conceito novo para os mercados de capitais. O domínio financeiro utilizou protocolos padrão da indústria para a integração de aplicações distribuídas desde a década de 1970 [26]. Os serviços da Web estão surgindo como uma tecnologia para integração sistemática e flexível entre aplicativos [5]. Os serviços da Web diferem da maioria das práticas de integração existentes, pois utilizam protocolos da Web comprovados e estabelecidos e padrões XML abertos. Os sistemas de mercados de capitais podem se beneficiar da introdução de serviços da web. A prática de integração existente é caracterizada por padrões competitivos da indústria e muitos protocolos proprietários, mas a ênfase dos serviços da Web em padrões abertos pode ser uma vantagem em conseguir que os participantes do setor concordem. O uso de XML e sua linguagem de definição formal XML Schema podem ajudar a melhorar as implementações do protocolo de integração, eliminando ambigüidades, bem como fornecendo suporte para validação automática de mensagens. Por fim, a extensibilidade de serviços da Web e XML pode permitir que os mecanismos de integração evoluam à medida que os mercados exigem novas funcionalidades, sem causar fragmentação adicional dos protocolos. No entanto, antes que os serviços da Web possam ser usados ​​para sistemas de mercado de capitais, vários requisitos técnicos devem ser atendidos. Esses requisitos incluem desempenho, segurança e tolerância a falhas. A pesquisa existente sobre o desempenho do SOAP considerou sua aplicação em áreas científicas, como a computação em grade, e concentrou-se na transmissão de dados numéricos. Com essa ênfase, o custo e a fraqueza predominantes das mensagens baseadas em XML foram identificados como a codificação e decodificação de valores de ponto flutuante [4]. Por outro lado, este estudo examina o desempenho do SOAP em cenários de computação de negócios realistas. Mais especificamente, o objetivo principal desta pesquisa é considerar a viabilidade do uso de SOAP em sistemas de mercado de capitais e, particularmente, em sistemas de negociação em tempo real. A abordagem adotada por este estudo é avaliar o desempenho do SOAP em relação à prática existente. O estudo compara o desempenho do SOAP com o protocolo FIX, estabelecido e amplamente utilizado e específico para cada domínio. O desempenho relativo de SOAP e FIX pode ser útil para determinar se o SOAP pode atender aos requisitos de desempenho dos mercados de capitais. A ênfase deste estudo foi sobre as limitações de desempenho inerentes dos formatos de fios. Tanto o SOAP quanto o FIX usam uma representação eletrônica baseada em texto e, portanto, pode parecer razoável concluir, com base na pesquisa existente, que ambos seriam afetados pelas mesmas ineficiências inerentes. Por esse motivo, um formato de wire binário, CDR, foi incluído na comparação para avaliar os custos associados à codificação de texto. O estudo constata, em primeiro lugar, que nos aplicativos de negócios o SOAP realmente apresenta um desempenho ruim em comparação ao formato de fio binário, o CDR. As mensagens SOAP têm cerca de 2 a 4 vezes o tamanho das mensagens CDR equivalentes. A latência em redes locais é substancialmente aumentada, com codificação de 8 a 10 vezes e decodificação cerca de 5 vezes mais cara. Estes resultados são semelhantes às conclusões de estudos anteriores, embora os resultados mostrem uma diferença menos acentuada do que quando o foco é na transmissão de dados numéricos. Quando comparado ao FIX, o SOAP exibe novamente um desempenho pior. As mensagens SOAP são 3,5-4,5 maiores que FIX, a latência é 2-3 vezes pior e os custos de codificação / decodificação são aumentados em até quase 9 vezes. Dado que o FIX, como o SOAP, é baseado em texto, o resultado surpreendente é que o FIX funcionou de forma comparável ao CDR. A partir disso, fomos levados a concluir que, em cenários realistas de aplicação de negócios, o desempenho insatisfatório do SOAP não pode ser adequadamente explicado simplesmente pelas desvantagens dos formatos de transferência binários baseados em texto. Isso também sugere que melhorias na eficiência dos codificadores e decodificadores SOAP podem permitir seu uso em aplicativos de negócios de alto desempenho. Fundo. Os sistemas de software utilizados nos mercados de capitais podem ser classificados de acordo com a sua posição no ciclo de vida de negociação [21]: Serviços de pré-negociação. A entrega de dados de mercado históricos e em tempo real, a análise desses dados e o encaminhamento de um pedido para a melhor entidade comercial de uma transação. Negociação. A execução de uma negociação em si em uma entidade comercial, como uma bolsa de valores como a Bolsa de Valores da Austrália (ASX). Serviços pós-negociação. Operações realizadas antes da finalização de um negócio, como vigilância, verificação de conformidade ou gerenciamento de risco. Assentamento. Finalização de uma transação, transferindo dinheiro entre o comprador e o vendedor. Registro. Transferência de propriedade dos títulos do vendedor para o comprador. Figura 1: Integração entre sistemas de negociação em tempo real. Este documento enfocará a parte pré-negociação do ciclo de vida e, em particular, as necessidades de integração dos sistemas de negociação em tempo real. A integração entre sistemas de negociação em tempo real normalmente envolve a comunicação de dados do mercado ativo e o fluxo de ordens de compra e venda, como mostrado na Figura 1. Dados os volumes potencialmente grandes de dados e a necessidade de entrega pontual, a integração entre os dados reais Os sistemas de negociação de tempo têm, na experiência dos autores, os mais altos requisitos de desempenho no domínio. Protocolo FIX. O protocolo Financial Information eXchange (FIX) [10] é um padrão de mensagens desenvolvido especificamente para a troca eletrônica em tempo real de transações com títulos. Figura 2: Um exemplo de fragmento de mensagem do protocolo FIX. As mensagens FIX são baseadas em texto e consistem em pares tag-value separados por um caractere delimitador especial (SOH, que é o valor ASCII 0x01), conforme ilustrado na Figura 2. As tags são strings curtas de dígitos e tipos de valores incluem strings, inteiros, valores de ponto flutuante, registros de data e hora e dados binários arbitrários. Embora o conteúdo de uma mensagem seja representado por estruturas de aplicativo complexas, o layout de uma mensagem codificada é simples com a ordenação flexível de campos. A especificação de protocolo descreve, em linguagem natural, o conjunto de tags disponíveis, seus significados de negócios correspondentes e a estrutura de mensagens necessária. Versões recentes do protocolo FIX introduziram um formato de mensagem baseado em XML, chamado FIXML [9]. Isso fornece mensagens FIX com uma rica estrutura on-the-wire, permitindo a validação automatizada e reduzindo as ambigüidades inerentes da abordagem baseada em tag. O XML também permite que o padrão FIX evolua para incluir nova funcionalidade sem causar fragmentação de versão adicional. Como este artigo avalia a adequação do SOAP para sistemas de mercado de capitais, o protocolo FIX será usado como base para algumas comparações. O FIX foi selecionado para essa finalidade em relação a outros protocolos do setor devido ao seu amplo uso. Uma pesquisa de 1999 com participantes do mercado, referenciada em [12], constatou que 82% dos corretores pesquisados ​​usaram o FIX. A influência do FIX também se estende a muitas organizações que usam variantes do protocolo padrão, ou usam definições de mensagem de protocolo que podem ser classificadas como do tipo FIX, como a SEATS Open Interface do ASX [1]. Trabalho relatado. Vários estudos avaliaram o desempenho de SOAP e XML [6,13,3]. Todos esses estudos concordaram que o SOAP e o XML incorrem em uma substancial penalidade de desempenho em comparação com os protocolos binários. [6] realizaram uma avaliação experimental do desempenho de latência de várias implementações de SOAP, comparando com outros protocolos como o Java RMI e o CORBA / IIOP. Uma conclusão extraída desses resultados foi que o SOAP é ordens de magnitude mais lenta, embora para alguns dos sistemas de SOAP mais lentos isso possa ser parcialmente explicado pela implementação deficiente. [13] avaliaram o desempenho do SOAP para computação científica de alto desempenho. Seus experimentos compararam o Java RMI com o SOAP enviando grandes matrizes de duplas (isto é, valores de ponto flutuante com 18 dígitos decimais de precisão). Os resultados mostraram que o SOAP é muito mais lento que o Java RMI, normalmente por um fator de dez. Eles concluíram que as mensagens XML do SOAP eram inerentemente inadequadas para uso na transferência de dados em massa, mas devido à flexibilidade e acessibilidade do formato, podem ser úteis como parte de um sistema multiprotocolo com o SOAP como "lingua franca". [3] apresentaram os resultados de experimentos que compararam a codificação, decodificação e desempenho de rede de vários formatos de mensagens, incluindo XML. Eles descobriram que os custos de empacotamento e comunicações do XML são incrivelmente altos em comparação com abordagens mais tradicionais, com XML de 2 a 4 ordens de magnitude mais lenta na codificação e decodificação do que o CORBA / IIOP e formatos de fios binários semelhantes. Eles concluíram que os formatos de fios XML são inadequados para sistemas de alto desempenho, já que o desempenho de linha de base de todos os sistemas é fortemente determinado pelo seu formato de fios. Esses estudos identificaram alguns fatores que podem afetar o desempenho dos serviços da Web e do SOAP, que podem ser agrupados em três categorias principais. Qualidade de Implementação. As decisões de design e implementação feitas pelos fornecedores de infra-estrutura SOAP podem ter um impacto considerável no desempenho. Esses fatores incluem: Escolha do método de análise XML. Diferentes modelos de análise XML têm diferentes compromissos em relação à eficiência de memória, velocidade computacional e facilidade de uso. Dos modelos genéricos de análise XML, a análise pull oferece o mais alto desempenho, bem como alta eficiência de memória [14]. No entanto, [4] descobriu que os analisadores específicos de esquema podem melhorar muito o desempenho em comparação com analisadores XML de propósito geral, particularmente onde grandes estruturas de dados estão envolvidas. Cálculo do comprimento da mensagem Ao usar o SOAP com uma ligação de rede HTTP / 1.0, o comprimento do corpo deve ser especificado no campo de cabeçalho `` Content-Length '' [2]. Definir o campo Comprimento do Conteúdo HTTP é difícil para dados dinâmicos, porque a mensagem deve ser armazenada em buffer, pois é construída para determinar quanto tempo ela é, e a própria mensagem não é enviada até que a codificação seja finalizada [6]. Custos de estabelecimento de conexão. O HTTP / 1.0 [2] determina o estabelecimento de uma nova conexão para cada operação. Estabelecer uma nova conexão para cada transação pode ter um impacto negativo no desempenho devido à interação com certos recursos do TCP [23], como o handshake de três vias [24] e o algoritmo de início lento [16]. Ao olhar para o desempenho do HTTP / 1.0 na Internet, pelo menos um quarto do tempo de transação pode ser ocupado pelo estabelecimento da conexão [17]. O HTTP / 1.1 fornece um recurso keep-alive de conexão que permite que um cliente execute várias operações em uma única conexão [8]. O pipelining HTTP / 1.1 adiciona suporte ao pipeline [8], que é a capacidade de enviar várias solicitações na conexão antes de aguardar uma resposta. Isso permite que a conexão seja usada com mais eficiência. Como alternativa ao pipelining, uma implementação HTTP / 1.0 pode fazer várias conexões de operação única em paralelo, geralmente usando multithreading. [19] descobriram que uma implementação HTTP / 1.1 usando pipelining em buffer usará menos de 10% do número de pacotes TCP que HTTP / 1.0 faz, e executará em menos tempo decorrido. No entanto, o HTTP / 1.1 sem pipelining tem um tempo decorrido maior que o HTTP / 1.0 usando várias conexões. Isso indica que a conexão keep-alive sozinho é insuficiente para melhorar o desempenho. Opções TCP inapropriadas. [6] descobriram que algumas implementações de SOAP sofreram um desempenho significativamente pior devido à interação entre o algoritmo Nagle e o algoritmo de confirmação atrasada TCP nos sistemas operacionais para o cliente e servidor [24]. Obviamente, isso não é um problema inerente ao SOAP ou ao HTTP. Ligação de protocolo de rede. O protocolo FIX define uma sessão como um "fluxo bidirecional de mensagens ordenadas entre duas partes" [10] e, portanto, não há semântica de solicitação-resposta imposta por sua especificação. Consequentemente, ao tentar aplicar o SOAP a um sistema de negociação em tempo real, preferimos usar o estilo de mensagens em vez da comunicação no estilo RPC. Como o HTTP é um protocolo de solicitação-resposta [8] com funções estritas de cliente e servidor, ele pode não ser adequado para uso na comunicação no estilo de mensagem. Felizmente, o SOAP não especifica uma ligação de transporte de rede específica e o uso de SOAP com protocolos de rede alternativos pode oferecer vantagens de desempenho. Isto implica claramente que as ineficiências atribuídas ao HTTP não são inerentes ao SOAP. Limitações Inerentes do SOAP. Tecnologias abertas de metadados, como XML, podem proporcionar um grande ganho de usabilidade, mas o sucesso dessas tecnologias exige que seu uso não prejudique o desempenho excessivamente [28]. O XML é extremamente robusto no que diz respeito a alterações no formato do registro recebido [3]. No entanto, o uso de XML pode impactar negativamente o desempenho do SOAP nas seguintes áreas: Velocidade de codificação e decodificação. A conversão de dados de binário para ASCII e vice-versa é o principal custo de desempenho de XML [3]. Esse é particularmente o caso dos valores de ponto flutuante, que foram encontrados como o maior custo de codificação e decodificação XML [4]. O uso de um protocolo baseado em texto também impede a aplicação de otimizações disponíveis para protocolos binários quando a comunicação ocorre entre sistemas homogêneos [3]. Tamanho da mensagem. Para XML, um fator de expansão de 6-8 vezes sobre os dados binários originais não é incomum [3]. [4] mediram a expansão em 4 a 10 vezes, e [13] descobriram que o tamanho da representação de dados do SOAP é tipicamente cerca de 10 vezes o tamanho da representação binária equivalente. Esse tamanho substancialmente maior pode resultar em custos de transmissão de rede mais altos e maior latência. Uma estratégia sugerida para superar essas ineficiências de desempenho inerentes é o uso de representações XML binárias [28,11,25,18]. Design experimental. O protocolo FIX, como XML e SOAP, é baseado em texto [10]. Isso significa que o FIX tem os mesmos problemas de desempenho com relação à codificação e decodificação de dados numéricos. Da mesma forma, as mensagens FIX podem ser maiores que sua representação binária equivalente, embora a sobrecarga seja menor que para XML devido ao formato compacto de valor de tag do FIX. O foco deste estudo está nos problemas inerentes de desempenho dos formatos de fio SOAP e FIX. Com isso em mente, os experimentos foram projetados para eliminar a qualidade da implementação e os fatores do protocolo de rede. Isto foi feito através do envio de mensagens codificadas nos vários formatos de fios através de sockets TCP `` raw '', usando um modelo de programação de rede consistente em cada caso. Ligações SOAP, como HTTP, não foram usadas. Além disso, as transmissões iniciais foram excluídas dos resultados para eliminar os efeitos do algoritmo TCP slow start [16], e a opção TCP_NODELAY foi ativada para desabilitar o algoritmo Nagle [24]. Para auxiliar na identificação de problemas de desempenho associados a formatos de fios baseados em texto, comparações também foram feitas com um formato de fio binário. O Common Data Representation (CDR) [20], que é usado como base da comunicação CORBA, foi selecionado para este propósito. Três tipos de experimentos foram conduzidos: Tamanho da Mensagem A representação codificada da estrutura da aplicação nos três formatos de fios foi comparada. Latência. Programas de teste foram escritos para medir tempos de ida e volta para enviar uma única mensagem usando cada um dos formatos de fio. Temporizadores de alta resolução permitiram a separação dos tempos gastos em codificação, decodificação e transmissão de rede. Taxa de transferência. Esses testes tentaram medir a quantidade de tempo necessária para transmitir, em uma única direção, um grande volume de mensagens. O primeiro conjunto de testes mediu a taxa de transferência da estrutura de dados do aplicativo em uma extremidade para a estrutura de dados do aplicativo na outra. A comparação com a taxa de transferência de mensagens pré-codificadas permite determinar se os custos de codificação / decodificação ou a rede foram o fator limitante. Figura 3: Um exemplo de estrutura de dados do aplicativo para a mensagem "atualização incremental de dados de mercado". A mensagem FIX "atualização incremental de dados de mercado" foi selecionada como os dados da empresa para a experiência. Essa mensagem é usada para enviar atualizações de dados de mercado, como os preços de ações mais recentes, ao longo de um dia de negociação e normalmente seria um volume alto e um tempo crítico. Instâncias geradas aleatoriamente desse tipo de mensagem, semelhantes às mostradas na Figura 3, foram transmitidas. O número de itens MDEntry em cada mensagem é variado de 1-10 para alterar o tamanho da mensagem e permitir a estimativa dos custos de desempenho fixos e incrementais para cada mensagem. Estruturas de dados de aplicativos foram traduzidas para e a partir dos formatos wire usando codificadores e decodificadores específicos do esquema. As ferramentas de software usadas para realizar isso foram as seguintes: As mensagens SOAP foram produzidas usando o gSOAP [27], um kit de ferramentas gratuito de alto desempenho. Estudos [27,4] mostraram que o gSOAP teve desempenho substancialmente mais alto do que algumas implementações de SOAP comumente usadas, embora não tão alto quanto outra implementação de pesquisa de propósito especial. O esquema XML para a mensagem foi baseado no FIXML DTD [9], com codificadores e decodificadores gerados em C ++. Uma implementação de um subconjunto do protocolo FIX foi desenvolvida especificamente para a pesquisa. A estrutura da aplicação foi definida em CORBA IDL, e os codificadores e decodificadores estavam em C ++. A mesma definição CORBA IDL usada com a implementação do FIX foi compilada usando o compilador IDL [22] IDL do TAB CORBA para gerar codificadores e decodificadores C ++ para o formato de ligação do CDR. Meio Ambiente. Os testes de latência e throughput foram executados em Ethernet de 10 Mbps e 100 Mbps. A comunicação entre sistemas de negociação em tempo real geralmente ocorre em linhas alugadas ou na Internet pública e, portanto, 10 Mbps pode ser mais comparável à largura de banda disponível nesses casos. O sistema cliente era um processador Pentium 3 de 900 MHz com 256 MB de RAM, 256 KB de cache nível 2 e executando o Windows 2000. O software de teste para este sistema foi compilado usando o compilador Borland C ++ 5.6.1. O servidor era um processador Pentium 3 de 500 MHz com 256 MB de RAM, 512 KB de cache nível 2, rodando o Red Hat Linux 7.3 com o kernel 2.4.18-3. O software de teste para este sistema foi compilado usando o compilador g ++ 3.2.1. Resultados experimentais. Tamanho da Mensagem Antes de executar experimentos para medir a latência e a taxa de transferência, os dados eram coletados para comparar o tamanho das mensagens do aplicativo quando codificados em cada um dos formatos de fio SOAP, FIX e CDR. Figura 4: Comparação do tamanho da mensagem dos formatos de fios. Tabela 1: Resumo dos custos do tamanho da mensagem. A Figura 4 e a Tabela 1 mostram os tamanhos das mensagens da estrutura de dados do aplicativo quando codificadas em cada formato. A figura mostra que as mensagens SOAP são substancialmente maiores, sendo cerca de 3,5 a 4,5 vezes maiores que a mensagem FIX equivalente, e 2-4 vezes maiores que uma em codificação usando CDR. Isso está no lado baixo dos resultados de tamanho relativo apresentados pelos estudos de desempenho de SOAP e XML existentes [3,4,13]. Figura 5: representação do FIX. Figura 6: representação SOAP. A Figura 5 mostra a estrutura da aplicação da Figura 3 codificada usando FIX, e a Figura 6 mostra os mesmos dados codificados no SOAP. Aqui vemos que os namespaces XML, os nomes de tag e a sintaxe mais detalhados contribuem para que a mensagem SOAP seja substancialmente maior. Figura 7: Idioma de campo opcional usado para definição da estrutura de dados do aplicativo em CORBA IDL. A Figura 4 também mostra que o FIX tem uma representação de cabo mais compacta do que o CDR, o que contraria o esperado. O CDR é aproximadamente 50% maior devido à falta de suporte integrado do CORBA IDL para campos opcionais. Em vez disso, usamos um idioma comum para definir campos opcionais em CORBA [15], conforme ilustrado na Figura 7. Isso significa que cada campo opcional usa um indicador de byte único para mostrar se está presente ou não. Como o cabeçalho da mensagem contém aproximadamente 20 campos opcionais, e como cada entrada de dados do mercado contém mais de 40, então, com a maioria desses campos não configurados, há uma sobrecarga considerável. Formatos alternativos de fio binário com suporte real para campos opcionais, como ASN.1 / BER, podem oferecer mensagens mais compactas. Figura 8: Tempos de ida e volta através da rede de 10 Mbps. Tabela 2: Resumo dos custos de ida e volta em uma rede de 10 Mbps. A Figura 8 e a Tabela 2 apresentam as medições para tempos de ida e volta em uma rede de 10 Mbps. Isso mostra que o FIX tem o menor tempo com o CDR não muito maior, especialmente quando comparado ao SOAP, que tem um tempo de ida e volta ligeiramente maior que o dobro dos outros dois. Figura 9: Discriminação de custos para tempos de ida e volta para mensagens com 10 entradas de dados de mercado. A divisão dos custos na Figura 9 mostra que, para todos os três formatos de transmissão, em uma rede de 10 Mbps, o maior custo é o tempo gasto na rede. Isso sugere que, nesse ambiente, o tamanho da mensagem no fio é o principal fator limitante. Em uma rede mais lenta, a representação de mensagens mais compacta do FIX contribui para os tempos de ida e volta inferiores aos do CDR. Figura 10: Custos de codificação do lado do servidor. Tabela 3: Resumo dos custos de codificação do lado do servidor. Figura 11: Custos de descodificação do lado do servidor. Tabela 4: Resumo dos custos de decodificação do lado do servidor. Over a 100 Mbps network, time spent on the network is less significant in overall round-trip times. Figure 10 and Table 3 show the encoding costs for the wire formats, and Figure 11 and Table 4 show the relative decoding costs. For 100 Mbps Ethernet, the substantially higher encoding and decoding costs for SOAP contribute most to its poorer performance, with round-trips some 2-3 times more expensive than FIX or CDR. An interesting result shown in Figure 11 is that FIX, a text-based wire format, has lower decoding costs than CDR, a binary format. This is particularly significant given the greater complexity involved in decoding FIX, with the presence of the tags in the wire format, flexible field ordering, and the fact that many fields may or may not be present at all on the wire. With CDR, on the other hand, all fields would be decoded in a fixed order as determined by their definition in the CORBA IDL. This result suggests two things: With this realistic application message content there is a mix of string, integer and floating point values. The cost of converting numerical data from text to binary, identified as major by other studies, does not have a predominant role. The cost of handling the complexity of the FIX message structure is minor compared to the cost of decoding a field, and that greater benefit is derived from not having to process the large number of optional fields that are not present on the wire. With the message encoding, encoders for all wire formats must test the presence of every field, and the advantage is lost. Throughput. Figure 12: Throughput over 10 Mbps network. Figure 12 displays the measurements for throughput over a 10 Mbps network. For this slower network configuration, the network itself was observed to be the bottleneck for all three wire formats. As with latency, this result suggests that in an environment with lower bandwidth, the size of the message is the major factor affecting performance. This allows FIX, with the most compact messages, to achieve the highest throughput values. Figure 13: Throughput over 100 Mbps network. For 100 Mbps networks, the CPU on the slower server machine was observed to be the bottleneck, and consequently the network was under utilised. The results in Figure 13 show that FIX again achieved the highest throughput, although CDR has lower encoding costs. This is a result of the decoding, which had a lower cost for FIX than CDR, being performed on the slower machine. Reversing the roles of the machines changes the relative throughput of the wire formats. Interestingly, the throughput performance of SOAP relative to the other two wire formats is worse for the 100 Mbps network. This is due to the ratio of SOAP decoding cost to FIX or CDR being greater than the equivalent ratio for message size. SOAP Message Compression. Over lower network bandwidth the size of the message on the wire is the limiting factor for performance. As a result, it may be possible that compression of the SOAP message data would confer some advantage. To determine if this is the case an additional latency test was run where the SOAP messages were compressed immediately before being transmitted. For this purpose, the zlib compression library was used on the lowest (and fastest) compression level. This achieved compression savings of 50-70%. Figure 14: Round-trip times over 10 Mbps network for compressed and uncompressed SOAP messages. The results, as shown in Figure 14, indicate that compression is in fact detrimental, substantially increasing the round-trip time. The increased CPU time spent compressing and decompressing the messages outweighs any benefits. Compression may only be useful for considerably slower networks. Compact XML Tags. Figure 15: Message size reduction when compact XML tags are used. An alternative method for reducing the size of the SOAP messages investigated in this study was to reduce the length of the XML tag names. This was done by replacing the FIXML names with short 2-4 character strings based on the numeric FIX tags. This reduced the size of the SOAP messages by approximately 25-35% as shown in Figure 15, but clearly sacrifices message readability in favour of the potential performance gains. Figure 16: Round-trip times for compact SOAP messages over 10 Mbps network. Figure 17: Decoding costs for compact SOAP messages. Figure 16 shows that the more compact SOAP messages do provide gains in performance over 10 Mbps Ethernet, where the time on the network is the major cost. However, the performance improvement is not in the same proportion to the reduction in message size. When considering the relative decoding costs shown in Figure 17, we see that there is not a commensurate improvement in decoding performance. Furthermore, the use of compact SOAP has a negligible effect on encoding efficiency. This suggests that the major cost of the XML encoding and decoding is in the structural complexity and syntactic elements, rather than the data contained in the message or the tag names. Discussão. Earlier studies into SOAP and XML performance [3,4] found that the conversion from text to binary and vice versa was the major cost, and particularly the costs associated with encoding and decoding floating point values. However, these studies were oriented towards the application of SOAP and XML to scientific computing, with message data consisting, for the large part, of numerical values. In this study we have attempted to study the performance of SOAP using realistic business application messages, with capital markets trading systems as the context. The results comparing SOAP to the binary wire format, CDR, do display poor performance for SOAP, although the difference is not as large as for the numerical data used in earlier studies. Given that the overall performance of FIX, with its text-based wire format, was comparable to CDR -- and in fact outperforming it for decoding -- it is clear that conversion of text-to-binary and back is not a major factor affecting performance in this case. Two important results of this study with respect to the performance of SOAP are: In business computing scenarios, it is possible for text-based formats to have comparable performance to binary wire formats. The poorer performance of SOAP implementations compared to other wire formats cannot be fully explained simply by the fact that SOAP is text-based. Simply reducing the size of the encoded SOAP messages by using shorter tags did not proportionally improve the speed of encoding and decoding. Together, these results mean that a likely cause of the poor performance of SOAP as a wire format is the complexity of the XML syntax and the richness of its on-the-wire structure. The SOAP message definition used in this study, based closely on FIXML [9], is complex with a high degree of nesting. It may be useful to conduct further research to gauge the effect on performance of alternative XML message representations. Results from such research could provide some guidance to developers on how to effectively design SOAP message layouts for high performance applications. The results of this study suggest some areas where SOAP implementors, in focusing any efforts to improve performance for business applications, may find the most benefit. Further study would be valuable in clarifying the causes of SOAP's poor performance, and what approaches may be used to address them. Furthermore, in this study we have considered only the inherent performance characteristics of the SOAP wire format. The other requirements for using SOAP in capital markets systems, such as security and fault tolerance, may have an additional impact on SOAP performance. Finally, the results show that it is important to consider the environment in which a system will be deployed when identifying the performance issues related to SOAP most relevant to that application. Although for fast networks the speed of encoding and decoding is the predominant determining factor, for slower networks it is the size of the encoded message that determines both latency and throughput performance. This is important for business-to-business integration which, in capital markets as in most other domains, often occurs over wide area networks. Conclusões In this paper we have presented the results of a performance evaluation of SOAP in a business application context. Our results indicate that, while SOAP did fare poorly when compared to both binary CDR and the established industry protocol FIX, the difference is less than that measured for scientific computing applications. Furthermore, in realistic business environments it is possible for text-based wire formats to have comparable performance to binary. This indicates that the text-based nature of XML is not in itself the major contributing factor to inefficiency in SOAP encoding and decoding. This finding suggests that further work in improving the performance of SOAP encoders and decoders may make it viable for use in high performance business applications. In spite of this, when designing performance-conscious systems for integration across wide area networks, bandwidth is generally the limiting factor, and it is worth considering the size of an encoded message when selecting an appropriate wire format. Referências. [1] Australian Stock Exchange . The SEATS computer system , 2000. asx.au/markets/l4/SEATS_AM4.shtm, accessed 1 June 2002. [2] T. Berners-Lee, R. Fielding, and H. Frystyk . Hypertext transfer protocol - HTTP/1.0 , 1996. IETF RFC 1945, ietf/rfc/rfc1945.txt. [3] F. E. Bustamante, G. Eisenhauer, K. Schwan, and P. Widener . Efficient wire formats for high performance computing . In Proceedings of the 2000 Conference on Supercomputing, 2000. [4] K. Chiu, M. Govindaraju, and R. Bramley . Investigating the limits of SOAP performance for scientific computing . In Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing, pages 246-254, 2002. [5] F. Curbera, M. Duftler, R. Khalaf, W. Nagy, N. Mukhi, and S. Weerawarana . Unraveling the web services web: An introduction to SOAP, WSDL, UDDI . IEEE Internet Computing, 6(2):86-93, March-April 2002. [6] D. Davis and M. Parashar . Latency performance of SOAP implementations . In Proceedings of the 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid, pages 407-412, 2002. [7] M. Fan, J. Stallaert, and A. B. Whinston . The internet and the future of financial markets . Communications of the ACM, 43(11):83-88, November 2000. [8] R. Fielding, J. Gettys, J. Mogul, H. Frystyk, L. Masinter, P. Leach, and T. Berners-Lee . Hypertext transfer protocol - HTTP/1.1 , 1999. IETF RFC 2616, ietf/rfc/rfc2616.txt. [9] FIX Protocol Ltd . FIXML: A markup language for the FIX application message layer . fixprotocol/WORKGROUPS/928951581/wpaper.html, accessed 8 June 2002. [10] FIX Protocol Ltd . The Financial Information Exchange Protocol (FIX), version 4.3 , August 2001. fixprotocol/specification/fix-43-pdf.zip, accessed 8 June 2002. [11] M. Girardot and N. Sundaresan . Millau: An encoding format for efficient representation and exchange of XML over the web . In Proceedings of the 9th International World Wide Web Conference, pages 747-765, 2000. [12] J. Goeller . FIXML and STP related efforts , 2000. fixprotocol/WORKGROUPS/928951581/XML_STP_John6.ppt, powerpoint presentation, accessed 8 June 2002. [13] M. Govindaraju, A. Slominski, V. Choppella, R. Bramley, and D. Gannon . Requirements for and evaluation of RMI protocols for scientific computing . 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In Proceedings of the ACM SIGCOMM '97 conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, pages 155-166, 1997. [20] Object Management Group . The Common Object Request Broker Architecture: Core Specification, version 3.0 , November 2002. [21] F. A. Rabhi and B. Benatallah . An integrated service architecture for managing capital market systems . IEEE Network, 16(1):15-19, 2002. [22] D. C. Schmidt, D. L. Levine, and S. Mungee . The design of the TAO real-time object request broker . Computer Communications, 21(4):294-324, April 1998. [23] S. E. Spero . Analysis of HTTP performance problems , 1994. w3/Protocols/HTTP/1.0/HTTPPerformance.html, accessed 15 June 2002. [24] W. R. Stevens . TCP/IP Illustrated, Volume 1: The Protocols . Addison-Wesley, Reading, Massachusetts, 1994. [25] N. Sundaresan and R. Moussa . Algorithms and programming models for efficient representation of XML for internet applications . In Proceedings of the 10th International World Wide Web Conference, pages 366-375, 2001. [26] SWIFT . About SWIFT - History . swift, accessed 3 June 2002. [27] R. A. van Engelen and K. A. Gallivan . The gSOAP toolkit for web services and peer-to-peer computing networks . In Proceedings of the 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid, pages 128-135, 2002. [28] P. Widener, G. Eisenhauer, and K. Schwan . Open metadata formats: Efficient XML-based communication for high performance computing . In Proceedings of the 10th IEEE International Symposium on High Performance Distributed Computing, pages 371-380, 2001. Your Products Made Better with Creo Imagine, Design, Create, Innovate your products better with PTC Creo. The 3D CAD / CAM / CAE software and solutions for product design and development. The Leading Product Design 3D CAD Software. 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Over the same period, a portfolio tracking the fast-growth economies of the Far East would have provided even higher returns. Previous researches in learning methods has focused on predictability based on comparative evaluation even these techniques may be employed to forecast financial markets as a prelude to intelligent trading systems. This paper explores the effect of a number of possible scenarios in this context. The alternative combinations of parameters include the selection of a learning method, whether a neural net or case based reasoning; the choice of markets, whether in one country or two; and the deployment of a passive or active trading strategy. When coupled with a forecasting system, however, a trading strategy offers the possibility for returns in excess of a passive buy-and-hold approach. In this study, we investigated the implications for portfolio management using an implicit learning technique (neural nets) and an explicit approach (CBR) Escolha uma opção para localizar / acessar este artigo: Verifique se você tem acesso através de suas credenciais de login ou sua instituição. Trading Floor Architecture. Available Languages. Download Options. View with Adobe Reader on a variety of devices. Índice. Trading Floor Architecture. Executive Overview. Increased competition, higher market data volume, and new regulatory demands are some of the driving forces behind industry changes. Firms are trying to maintain their competitive edge by constantly changing their trading strategies and increasing the speed of trading. A viable architecture has to include the latest technologies from both network and application domains. It has to be modular to provide a manageable path to evolve each component with minimal disruption to the overall system. Therefore the architecture proposed by this paper is based on a services framework. We examine services such as ultra-low latency messaging, latency monitoring, multicast, computing, storage, data and application virtualization, trading resiliency, trading mobility, and thin client. The solution to the complex requirements of the next-generation trading platform must be built with a holistic mindset, crossing the boundaries of traditional silos like business and technology or applications and networking. This document's main goal is to provide guidelines for building an ultra-low latency trading platform while optimizing the raw throughput and message rate for both market data and FIX trading orders. To achieve this, we are proposing the following latency reduction technologies: • High speed inter-connect—InfiniBand or 10 Gbps connectivity for the trading cluster. • High-speed messaging bus. • Application acceleration via RDMA without application re-code. • Real-time latency monitoring and re-direction of trading traffic to the path with minimum latency. Industry Trends and Challenges. Next-generation trading architectures have to respond to increased demands for speed, volume, and efficiency. For example, the volume of options market data is expected to double after the introduction of options penny trading in 2007. There are also regulatory demands for best execution, which require handling price updates at rates that approach 1M msg/sec. for exchanges. They also require visibility into the freshness of the data and proof that the client got the best possible execution. In the short term, speed of trading and innovation are key differentiators. An increasing number of trades are handled by algorithmic trading applications placed as close as possible to the trade execution venue. A challenge with these "black-box" trading engines is that they compound the volume increase by issuing orders only to cancel them and re-submit them. The cause of this behavior is lack of visibility into which venue offers best execution. The human trader is now a "financial engineer," a "quant" (quantitative analyst) with programming skills, who can adjust trading models on the fly. Firms develop new financial instruments like weather derivatives or cross-asset class trades and they need to deploy the new applications quickly and in a scalable fashion. In the long term, competitive differentiation should come from analysis, not just knowledge. The star traders of tomorrow assume risk, achieve true client insight, and consistently beat the market (source IBM: www-935.ibm/services/us/imc/pdf/ge510-6270-trader.pdf). Business resilience has been one main concern of trading firms since September 11, 2001. Solutions in this area range from redundant data centers situated in different geographies and connected to multiple trading venues to virtual trader solutions offering power traders most of the functionality of a trading floor in a remote location. The financial services industry is one of the most demanding in terms of IT requirements. The industry is experiencing an architectural shift towards Services-Oriented Architecture (SOA), Web services, and virtualization of IT resources. SOA takes advantage of the increase in network speed to enable dynamic binding and virtualization of software components. This allows the creation of new applications without losing the investment in existing systems and infrastructure. The concept has the potential to revolutionize the way integration is done, enabling significant reductions in the complexity and cost of such integration (gigaspaces/download/MerrilLynchGigaSpacesWP.pdf). Another trend is the consolidation of servers into data center server farms, while trader desks have only KVM extensions and ultra-thin clients (e.g., SunRay and HP blade solutions). High-speed Metro Area Networks enable market data to be multicast between different locations, enabling the virtualization of the trading floor. High-Level Architecture. Figure 1 depicts the high-level architecture of a trading environment. The ticker plant and the algorithmic trading engines are located in the high performance trading cluster in the firm's data center or at the exchange. The human traders are located in the end-user applications area. Functionally there are two application components in the enterprise trading environment, publishers and subscribers. The messaging bus provides the communication path between publishers and subscribers. There are two types of traffic specific to a trading environment: • Market Data—Carries pricing information for financial instruments, news, and other value-added information such as analytics. It is unidirectional and very latency sensitive, typically delivered over UDP multicast. It is measured in updates/sec. and in Mbps. Market data flows from one or multiple external feeds, coming from market data providers like stock exchanges, data aggregators, and ECNs. Each provider has their own market data format. The data is received by feed handlers, specialized applications which normalize and clean the data and then send it to data consumers, such as pricing engines, algorithmic trading applications, or human traders. Sell-side firms also send the market data to their clients, buy-side firms such as mutual funds, hedge funds, and other asset managers. Some buy-side firms may opt to receive direct feeds from exchanges, reducing latency. Figure 1 Trading Architecture for a Buy Side/Sell Side Firm. There is no industry standard for market data formats. Each exchange has their proprietary format. Financial content providers such as Reuters and Bloomberg aggregate different sources of market data, normalize it, and add news or analytics. Examples of consolidated feeds are RDF (Reuters Data Feed), RWF (Reuters Wire Format), and Bloomberg Professional Services Data. To deliver lower latency market data, both vendors have released real-time market data feeds which are less processed and have less analytics: – Bloomberg B-Pipe—With B-Pipe, Bloomberg de-couples their market data feed from their distribution platform because a Bloomberg terminal is not required for get B-Pipe. Wombat and Reuters Feed Handlers have announced support for B-Pipe. A firm may decide to receive feeds directly from an exchange to reduce latency. The gains in transmission speed can be between 150 milliseconds to 500 milliseconds. These feeds are more complex and more expensive and the firm has to build and maintain their own ticker plant (financetech/featured/showArticle.jhtml?articleID=60404306). • Trading Orders—This type of traffic carries the actual trades. It is bi-directional and very latency sensitive. It is measured in messages/sec. and Mbps. The orders originate from a buy side or sell side firm and are sent to trading venues like an Exchange or ECN for execution. The most common format for order transport is FIX (Financial Information eXchange—fixprotocol/). The applications which handle FIX messages are called FIX engines and they interface with order management systems (OMS). An optimization to FIX is called FAST (Fix Adapted for Streaming), which uses a compression schema to reduce message length and, in effect, reduce latency. FAST is targeted more to the delivery of market data and has the potential to become a standard. FAST can also be used as a compression schema for proprietary market data formats. To reduce latency, firms may opt to establish Direct Market Access (DMA). DMA is the automated process of routing a securities order directly to an execution venue, therefore avoiding the intervention by a third-party (towergroup/research/content/glossary.jsp?page=1&glossaryId=383). DMA requires a direct connection to the execution venue. The messaging bus is middleware software from vendors such as Tibco, 29West, Reuters RMDS, or an open source platform such as AMQP. The messaging bus uses a reliable mechanism to deliver messages. The transport can be done over TCP/IP (TibcoEMS, 29West, RMDS, and AMQP) or UDP/multicast (TibcoRV, 29West, and RMDS). One important concept in message distribution is the "topic stream," which is a subset of market data defined by criteria such as ticker symbol, industry, or a certain basket of financial instruments. Subscribers join topic groups mapped to one or multiple sub-topics in order to receive only the relevant information. In the past, all traders received all market data. At the current volumes of traffic, this would be sub-optimal. The network plays a critical role in the trading environment. Market data is carried to the trading floor where the human traders are located via a Campus or Metro Area high-speed network. High availability and low latency, as well as high throughput, are the most important metrics. The high performance trading environment has most of its components in the Data Center server farm. To minimize latency, the algorithmic trading engines need to be located in the proximity of the feed handlers, FIX engines, and order management systems. An alternate deployment model has the algorithmic trading systems located at an exchange or a service provider with fast connectivity to multiple exchanges. Deployment Models. There are two deployment models for a high performance trading platform. Firms may chose to have a mix of the two: • Data Center of the trading firm (Figure 2)—This is the traditional model, where a full-fledged trading platform is developed and maintained by the firm with communication links to all the trading venues. Latency varies with the speed of the links and the number of hops between the firm and the venues. Figure 2 Traditional Deployment Model. • Co-location at the trading venue (exchanges, financial service providers (FSP)) (Figure 3) The trading firm deploys its automated trading platform as close as possible to the execution venues to minimize latency. Figure 3 Hosted Deployment Model. Services-Oriented Trading Architecture. We are proposing a services-oriented framework for building the next-generation trading architecture. This approach provides a conceptual framework and an implementation path based on modularization and minimization of inter-dependencies. This framework provides firms with a methodology to: • Evaluate their current state in terms of services. • Prioritize services based on their value to the business. • Evolve the trading platform to the desired state using a modular approach. The high performance trading architecture relies on the following services, as defined by the services architecture framework represented in Figure 4. Figure 4 Service Architecture Framework for High Performance Trading. Table 1 Service Descriptions and Technologies. Ultra-low latency messaging. Instrumentation—appliances, software agents, and router modules. OS and I/O virtualization, Remote Direct Memory Access (RDMA), TCP Offload Engines (TOE) Middleware which parallelizes application processing. Middleware which speeds-up data access for applications, e.g., in-memory caching. Hardware-assisted multicast replication through-out the network; multicast Layer 2 and Layer 3 optimizations. Virtualization of storage hardware (VSANs), data replication, remote backup, and file virtualization. Trading resilience and mobility. Local and site load balancing and high availability campus networks. Wide Area application services. Acceleration of applications over a WAN connection for traders residing off-campus. Thin client service. De-coupling of the computing resources from the end-user facing terminals. Ultra-Low Latency Messaging Service. This service is provided by the messaging bus, which is a software system that solves the problem of connecting many-to-many applications. The system consists of: • A set of pre-defined message schemas. • A set of common command messages. • A shared application infrastructure for sending the messages to recipients. The shared infrastructure can be based on a message broker or on a publish/subscribe model. The key requirements for the next-generation messaging bus are (source 29West): • Lowest possible latency (e.g., less than 100 microseconds) • Stability under heavy load (e.g., more than 1.4 million msg/sec.) • Control and flexibility (rate control and configurable transports) There are efforts in the industry to standardize the messaging bus. Advanced Message Queueing Protocol (AMQP) is an example of an open standard championed by J.P. Morgan Chase and supported by a group of vendors such as Cisco, Envoy Technologies, Red Hat, TWIST Process Innovations, Iona, 29West, and iMatix. Two of the main goals are to provide a more simple path to inter-operability for applications written on different platforms and modularity so that the middleware can be easily evolved. In very general terms, an AMQP server is analogous to an E-mail server with each exchange acting as a message transfer agent and each message queue as a mailbox. The bindings define the routing tables in each transfer agent. Publishers send messages to individual transfer agents, which then route the messages into mailboxes. Consumers take messages from mailboxes, which creates a powerful and flexible model that is simple (source: amqp/tikiwiki/tiki-index.php?page=OpenApproach#Why_AMQP_). Latency Monitoring Service. The main requirements for this service are: • Sub-millisecond granularity of measurements. • Near-real time visibility without adding latency to the trading traffic. • Ability to differentiate application processing latency from network transit latency. • Ability to handle high message rates. • Provide a programmatic interface for trading applications to receive latency data, thus enabling algorithmic trading engines to adapt to changing conditions. • Correlate network events with application events for troubleshooting purposes. Latency can be defined as the time interval between when a trade order is sent and when the same order is acknowledged and acted upon by the receiving party. Addressing the latency issue is a complex problem, requiring a holistic approach that identifies all sources of latency and applies different technologies at different layers of the system. Figure 5 depicts the variety of components that can introduce latency at each layer of the OSI stack. It also maps each source of latency with a possible solution and a monitoring solution. This layered approach can give firms a more structured way of attacking the latency issue, whereby each component can be thought of as a service and treated consistently across the firm. Maintaining an accurate measure of the dynamic state of this time interval across alternative routes and destinations can be of great assistance in tactical trading decisions. The ability to identify the exact location of delays, whether in the customer's edge network, the central processing hub, or the transaction application level, significantly determines the ability of service providers to meet their trading service-level agreements (SLAs). For buy-side and sell-side forms, as well as for market-data syndicators, the quick identification and removal of bottlenecks translates directly into enhanced trade opportunities and revenue. Figure 5 Latency Management Architecture. Cisco Low-Latency Monitoring Tools. Traditional network monitoring tools operate with minutes or seconds granularity. Next-generation trading platforms, especially those supporting algorithmic trading, require latencies less than 5 ms and extremely low levels of packet loss. On a Gigabit LAN, a 100 ms microburst can cause 10,000 transactions to be lost or excessively delayed. Cisco offers its customers a choice of tools to measure latency in a trading environment: • Bandwidth Quality Manager (BQM) (OEM from Corvil) • Cisco AON-based Financial Services Latency Monitoring Solution (FSMS) Bandwidth Quality Manager. Bandwidth Quality Manager (BQM) 4.0 is a next-generation network application performance management product that enables customers to monitor and provision their network for controlled levels of latency and loss performance. While BQM is not exclusively targeted at trading networks, its microsecond visibility combined with intelligent bandwidth provisioning features make it ideal for these demanding environments. Cisco BQM 4.0 implements a broad set of patented and patent-pending traffic measurement and network analysis technologies that give the user unprecedented visibility and understanding of how to optimize the network for maximum application performance. Cisco BQM is now supported on the product family of Cisco Application Deployment Engine (ADE). The Cisco ADE product family is the platform of choice for Cisco network management applications. BQM Benefits. Cisco BQM micro-visibility is the ability to detect, measure, and analyze latency, jitter, and loss inducing traffic events down to microsecond levels of granularity with per packet resolution. This enables Cisco BQM to detect and determine the impact of traffic events on network latency, jitter, and loss. Critical for trading environments is that BQM can support latency, loss, and jitter measurements one-way for both TCP and UDP (multicast) traffic. This means it reports seamlessly for both trading traffic and market data feeds. BQM allows the user to specify a comprehensive set of thresholds (against microburst activity, latency, loss, jitter, utilization, etc.) on all interfaces. BQM then operates a background rolling packet capture. Whenever a threshold violation or other potential performance degradation event occurs, it triggers Cisco BQM to store the packet capture to disk for later analysis. This allows the user to examine in full detail both the application traffic that was affected by performance degradation ("the victims") and the traffic that caused the performance degradation ("the culprits"). This can significantly reduce the time spent diagnosing and resolving network performance issues. BQM is also able to provide detailed bandwidth and quality of service (QoS) policy provisioning recommendations, which the user can directly apply to achieve desired network performance. BQM Measurements Illustrated. To understand the difference between some of the more conventional measurement techniques and the visibility provided by BQM, we can look at some comparison graphs. In the first set of graphs (Figure 6 and Figure 7), we see the difference between the latency measured by BQM's Passive Network Quality Monitor (PNQM) and the latency measured by injecting ping packets every 1 second into the traffic stream. In Figure 6, we see the latency reported by 1-second ICMP ping packets for real network traffic (it is divided by 2 to give an estimate for the one-way delay). It shows the delay comfortably below about 5ms for almost all of the time. Figure 6 Latency Reported by 1-Second ICMP Ping Packets for Real Network Traffic. In Figure 7, we see the latency reported by PNQM for the same traffic at the same time. Here we see that by measuring the one-way latency of the actual application packets, we get a radically different picture. Here the latency is seen to be hovering around 20 ms, with occasional bursts far higher. The explanation is that because ping is sending packets only every second, it is completely missing most of the application traffic latency. In fact, ping results typically only indicate round trip propagation delay rather than realistic application latency across the network. Figure 7 Latency Reported by PNQM for Real Network Traffic. In the second example (Figure 8), we see the difference in reported link load or saturation levels between a 5-minute average view and a 5 ms microburst view (BQM can report on microbursts down to about 10-100 nanosecond accuracy). The green line shows the average utilization at 5-minute averages to be low, maybe up to 5 Mbits/s. The dark blue plot shows the 5ms microburst activity reaching between 75 Mbits/s and 100 Mbits/s, the LAN speed effectively. BQM shows this level of granularity for all applications and it also gives clear provisioning rules to enable the user to control or neutralize these microbursts. Figure 8 Difference in Reported Link Load Between a 5-Minute Average View and a 5 ms Microburst View. BQM Deployment in the Trading Network. Figure 9 shows a typical BQM deployment in a trading network. Figure 9 Typical BQM Deployment in a Trading Network. BQM can then be used to answer these types of questions: • Are any of my Gigabit LAN core links saturated for more than X milliseconds? Is this causing loss? Which links would most benefit from an upgrade to Etherchannel or 10 Gigabit speeds? • What application traffic is causing the saturation of my 1 Gigabit links? • Is any of the market data experiencing end-to-end loss? • How much additional latency does the failover data center experience? Is this link sized correctly to deal with microbursts? • Are my traders getting low latency updates from the market data distribution layer? Are they seeing any delays greater than X milliseconds? Being able to answer these questions simply and effectively saves time and money in running the trading network. BQM is an essential tool for gaining visibility in market data and trading environments. It provides granular end-to-end latency measurements in complex infrastructures that experience high-volume data movement. Effectively detecting microbursts in sub-millisecond levels and receiving expert analysis on a particular event is invaluable to trading floor architects. Smart bandwidth provisioning recommendations, such as sizing and what-if analysis, provide greater agility to respond to volatile market conditions. As the explosion of algorithmic trading and increasing message rates continues, BQM, combined with its QoS tool, provides the capability of implementing QoS policies that can protect critical trading applications. Cisco Financial Services Latency Monitoring Solution. Cisco and Trading Metrics have collaborated on latency monitoring solutions for FIX order flow and market data monitoring. Cisco AON technology is the foundation for a new class of network-embedded products and solutions that help merge intelligent networks with application infrastructure, based on either service-oriented or traditional architectures. Trading Metrics is a leading provider of analytics software for network infrastructure and application latency monitoring purposes (tradingmetrics/). The Cisco AON Financial Services Latency Monitoring Solution (FSMS) correlated two kinds of events at the point of observation: • Network events correlated directly with coincident application message handling. • Trade order flow and matching market update events. Using time stamps asserted at the point of capture in the network, real-time analysis of these correlated data streams permits precise identification of bottlenecks across the infrastructure while a trade is being executed or market data is being distributed. By monitoring and measuring latency early in the cycle, financial companies can make better decisions about which network service—and which intermediary, market, or counterparty—to select for routing trade orders. Likewise, this knowledge allows more streamlined access to updated market data (stock quotes, economic news, etc.), which is an important basis for initiating, withdrawing from, or pursuing market opportunities. The components of the solution are: • AON hardware in three form factors: – AON Network Module for Cisco 2600/2800/3700/3800 routers. – AON Blade for the Cisco Catalyst 6500 series. – AON 8340 Appliance. • Trading Metrics M&A 2.0 software, which provides the monitoring and alerting application, displays latency graphs on a dashboard, and issues alerts when slowdowns occur (tradingmetrics/TM_brochure.pdf). Figure 10 AON-Based FIX Latency Monitoring. Cisco IP SLA. Cisco IP SLA is an embedded network management tool in Cisco IOS which allows routers and switches to generate synthetic traffic streams which can be measured for latency, jitter, packet loss, and other criteria (cisco/go/ipsla). Two key concepts are the source of the generated traffic and the target. Both of these run an IP SLA "responder," which has the responsibility to timestamp the control traffic before it is sourced and returned by the target (for a round trip measurement). Various traffic types can be sourced within IP SLA and they are aimed at different metrics and target different services and applications. The UDP jitter operation is used to measure one-way and round-trip delay and report variations. As the traffic is time stamped on both sending and target devices using the responder capability, the round trip delay is characterized as the delta between the two timestamps. A new feature was introduced in IOS 12.3(14)T, IP SLA Sub Millisecond Reporting, which allows for timestamps to be displayed with a resolution in microseconds, thus providing a level of granularity not previously available. This new feature has now made IP SLA relevant to campus networks where network latency is typically in the range of 300-800 microseconds and the ability to detect trends and spikes (brief trends) based on microsecond granularity counters is a requirement for customers engaged in time-sensitive electronic trading environments. As a result, IP SLA is now being considered by significant numbers of financial organizations as they are all faced with requirements to: • Report baseline latency to their users. • Trend baseline latency over time. • Respond quickly to traffic bursts that cause changes in the reported latency. Sub-millisecond reporting is necessary for these customers, since many campus and backbones are currently delivering under a second of latency across several switch hops. Electronic trading environments have generally worked to eliminate or minimize all areas of device and network latency to deliver rapid order fulfillment to the business. Reporting that network response times are "just under one millisecond" is no longer sufficient; the granularity of latency measurements reported across a network segment or backbone need to be closer to 300-800 micro-seconds with a degree of resolution of 100 ì seconds. IP SLA recently added support for IP multicast test streams, which can measure market data latency. A typical network topology is shown in Figure 11 with the IP SLA shadow routers, sources, and responders. Figure 11 IP SLA Deployment. Serviços de computação. Computing services cover a wide range of technologies with the goal of eliminating memory and CPU bottlenecks created by the processing of network packets. Trading applications consume high volumes of market data and the servers have to dedicate resources to processing network traffic instead of application processing. • Transport processing—At high speeds, network packet processing can consume a significant amount of server CPU cycles and memory. An established rule of thumb states that 1Gbps of network bandwidth requires 1 GHz of processor capacity (source Intel white paper on I/O acceleration intel/technology/ioacceleration/306517.pdf). • Intermediate buffer copying—In a conventional network stack implementation, data needs to be copied by the CPU between network buffers and application buffers. This overhead is worsened by the fact that memory speeds have not kept up with increases in CPU speeds. For example, processors like the Intel Xeon are approaching 4 GHz, while RAM chips hover around 400MHz (for DDR 3200 memory) (source Intel intel/technology/ioacceleration/306517.pdf). • Context switching—Every time an individual packet needs to be processed, the CPU performs a context switch from application context to network traffic context. This overhead could be reduced if the switch would occur only when the whole application buffer is complete. Figure 12 Sources of Overhead in Data Center Servers. • TCP Offload Engine (TOE)—Offloads transport processor cycles to the NIC. Moves TCP/IP protocol stack buffer copies from system memory to NIC memory. • Remote Direct Memory Access (RDMA)—Enables a network adapter to transfer data directly from application to application without involving the operating system. Eliminates intermediate and application buffer copies (memory bandwidth consumption). • Kernel bypass —Direct user-level access to hardware. Dramatically reduces application context switches. Figure 13 RDMA and Kernel Bypass. InfiniBand is a point-to-point (switched fabric) bidirectional serial communication link which implements RDMA, among other features. Cisco offers an InfiniBand switch, the Server Fabric Switch (SFS): cisco/application/pdf/en/us/guest/netsol/ns500/c643/cdccont_0900aecd804c35cb.pdf. Figure 14 Typical SFS Deployment. Trading applications benefit from the reduction in latency and latency variability, as proved by a test performed with the Cisco SFS and Wombat Feed Handlers by Stac Research: Application Virtualization Service. De-coupling the application from the underlying OS and server hardware enables them to run as network services. One application can be run in parallel on multiple servers, or multiple applications can be run on the same server, as the best resource allocation dictates. This decoupling enables better load balancing and disaster recovery for business continuance strategies. The process of re-allocating computing resources to an application is dynamic. Using an application virtualization system like Data Synapse's GridServer, applications can migrate, using pre-configured policies, to under-utilized servers in a supply-matches-demand process (networkworld/supp/2005/ndc1/022105virtual.html?page=2). There are many business advantages for financial firms who adopt application virtualization: • Faster time to market for new products and services. • Faster integration of firms following merger and acquisition activity. • Increased application availability. • Better workload distribution, which creates more "head room" for processing spikes in trading volume. • Operational efficiency and control. • Reduction in IT complexity. Currently, application virtualization is not used in the trading front-office. One use-case is risk modeling, like Monte Carlo simulations. As the technology evolves, it is conceivable that some the trading platforms will adopt it. Data Virtualization Service. To effectively share resources across distributed enterprise applications, firms must be able to leverage data across multiple sources in real-time while ensuring data integrity. With solutions from data virtualization software vendors such as Gemstone or Tangosol (now Oracle), financial firms can access heterogeneous sources of data as a single system image that enables connectivity between business processes and unrestrained application access to distributed caching. The net result is that all users have instant access to these data resources across a distributed network (gridtoday/03/0210/101061.html). This is called a data grid and is the first step in the process of creating what Gartner calls Extreme Transaction Processing (XTP) (gartner/DisplayDocument?ref=g_search&id=500947). Technologies such as data and applications virtualization enable financial firms to perform real-time complex analytics, event-driven applications, and dynamic resource allocation. One example of data virtualization in action is a global order book application. An order book is the repository of active orders that is published by the exchange or other market makers. A global order book aggregates orders from around the world from markets that operate independently. The biggest challenge for the application is scalability over WAN connectivity because it has to maintain state. Today's data grids are localized in data centers connected by Metro Area Networks (MAN). This is mainly because the applications themselves have limits—they have been developed without the WAN in mind. Figure 15 GemStone GemFire Distributed Caching. Before data virtualization, applications used database clustering for failover and scalability. This solution is limited by the performance of the underlying database. Failover is slower because the data is committed to disc. With data grids, the data which is part of the active state is cached in memory, which reduces drastically the failover time. Scaling the data grid means just adding more distributed resources, providing a more deterministic performance compared to a database cluster. Multicast Service. Market data delivery is a perfect example of an application that needs to deliver the same data stream to hundreds and potentially thousands of end users. Market data services have been implemented with TCP or UDP broadcast as the network layer, but those implementations have limited scalability. Using TCP requires a separate socket and sliding window on the server for each recipient. UDP broadcast requires a separate copy of the stream for each destination subnet. Both of these methods exhaust the resources of the servers and the network. The server side must transmit and service each of the streams individually, which requires larger and larger server farms. On the network side, the required bandwidth for the application increases in a linear fashion. For example, to send a 1 Mbps stream to 1000recipients using TCP requires 1 Gbps of bandwidth. IP multicast is the only way to scale market data delivery. To deliver a 1 Mbps stream to 1000 recipients, IP multicast would require 1 Mbps. The stream can be delivered by as few as two servers—one primary and one backup for redundancy. There are two main phases of market data delivery to the end user. In the first phase, the data stream must be brought from the exchange into the brokerage's network. Typically the feeds are terminated in a data center on the customer premise. The feeds are then processed by a feed handler, which may normalize the data stream into a common format and then republish into the application messaging servers in the data center. The second phase involves injecting the data stream into the application messaging bus which feeds the core infrastructure of the trading applications. The large brokerage houses have thousands of applications that use the market data streams for various purposes, such as live trades, long term trending, arbitrage, etc. Many of these applications listen to the feeds and then republish their own analytical and derivative information. For example, a brokerage may compare the prices of CSCO to the option prices of CSCO on another exchange and then publish ratings which a different application may monitor to determine how much they are out of synchronization. Figure 16 Market Data Distribution Players. The delivery of these data streams is typically over a reliable multicast transport protocol, traditionally Tibco Rendezvous. Tibco RV operates in a publish and subscribe environment. Each financial instrument is given a subject name, such as CSCO.last. Each application server can request the individual instruments of interest by their subject name and receive just a that subset of the information. This is called subject-based forwarding or filtering. Subject-based filtering is patented by Tibco. A distinction should be made between the first and second phases of market data delivery. The delivery of market data from the exchange to the brokerage is mostly a one-to-many application. The only exception to the unidirectional nature of market data may be retransmission requests, which are usually sent using unicast. The trading applications, however, are definitely many-to-many applications and may interact with the exchanges to place orders. Figure 17 Market Data Architecture. Design Issues. Number of Groups/Channels to Use. Many application developers consider using thousand of multicast groups to give them the ability to divide up products or instruments into small buckets. Normally these applications send many small messages as part of their information bus. Usually several messages are sent in each packet that are received by many users. Sending fewer messages in each packet increases the overhead necessary for each message. In the extreme case, sending only one message in each packet quickly reaches the point of diminishing returns—there is more overhead sent than actual data. Application developers must find a reasonable compromise between the number of groups and breaking up their products into logical buckets. Consider, for example, the Nasdaq Quotation Dissemination Service (NQDS). The instruments are broken up alphabetically: Another example is the Nasdaq Totalview service, broken up this way: This approach allows for straight forward network/application management, but does not necessarily allow for optimized bandwidth utilization for most users. A user of NQDS that is interested in technology stocks, and would like to subscribe to just CSCO and INTL, would have to pull down all the data for the first two groups of NQDS. Understanding the way users pull down the data and then organize it into appropriate logical groups optimizes the bandwidth for each user. In many market data applications, optimizing the data organization would be of limited value. Typically customers bring in all data into a few machines and filter the instruments. Using more groups is just more overhead for the stack and does not help the customers conserve bandwidth. Another approach might be to keep the groups down to a minimum level and use UDP port numbers to further differentiate if necessary. The other extreme would be to use just one multicast group for the entire application and then have the end user filter the data. In some situations this may be sufficient. Intermittent Sources. A common issue with market data applications are servers that send data to a multicast group and then go silent for more than 3.5 minutes. These intermittent sources may cause trashing of state on the network and can introduce packet loss during the window of time when soft state and then hardware shorts are being created. PIM-Bidir or PIM-SSM. The first and best solution for intermittent sources is to use PIM-Bidir for many-to-many applications and PIM-SSM for one-to-many applications. Both of these optimizations of the PIM protocol do not have any data-driven events in creating forwarding state. That means that as long as the receivers are subscribed to the streams, the network has the forwarding state created in the hardware switching path. Intermittent sources are not an issue with PIM-Bidir and PIM-SSM. Null Packets. In PIM-SM environments a common method to make sure forwarding state is created is to send a burst of null packets to the multicast group before the actual data stream. The application must efficiently ignore these null data packets to ensure it does not affect performance. The sources must only send the burst of packets if they have been silent for more than 3 minutes. A good practice is to send the burst if the source is silent for more than a minute. Many financials send out an initial burst of traffic in the morning and then all well-behaved sources do not have problems. Periodic Keepalives or Heartbeats. An alternative approach for PIM-SM environments is for sources to send periodic heartbeat messages to the multicast groups. This is a similar approach to the null packets, but the packets can be sent on a regular timer so that the forwarding state never expires. S,G Expiry Timer. Finally, Cisco has made a modification to the operation of the S,G expiry timer in IOS. There is now a CLI knob to allow the state for a S,G to stay alive for hours without any traffic being sent. The (S,G) expiry timer is configurable. This approach should be considered a workaround until PIM-Bidir or PIM-SSM is deployed or the application is fixed. RTCP Feedback. A common issue with real time voice and video applications that use RTP is the use of RTCP feedback traffic. Unnecessary use of the feedback option can create excessive multicast state in the network. If the RTCP traffic is not required by the application it should be avoided. Fast Producers and Slow Consumers. Today many servers providing market data are attached at Gigabit speeds, while the receivers are attached at different speeds, usually 100Mbps. This creates the potential for receivers to drop packets and request re-transmissions, which creates more traffic that the slowest consumers cannot handle, continuing the vicious circle. The solution needs to be some type of access control in the application that limits the amount of data that one host can request. QoS and other network functions can mitigate the problem, but ultimately the subscriptions need to be managed in the application. Tibco Heartbeats. TibcoRV has had the ability to use IP multicast for the heartbeat between the TICs for many years. However, there are some brokerage houses that are still using very old versions of TibcoRV that use UDP broadcast support for the resiliency. This limitation is often cited as a reason to maintain a Layer 2 infrastructure between TICs located in different data centers. These older versions of TibcoRV should be phased out in favor of the IP multicast supported versions. Multicast Forwarding Options. PIM Sparse Mode. The standard IP multicast forwarding protocol used today for market data delivery is PIM Sparse Mode. It is supported on all Cisco routers and switches and is well understood. PIM-SM can be used in all the network components from the exchange, FSP, and brokerage. There are, however, some long-standing issues and unnecessary complexity associated with a PIM-SM deployment that could be avoided by using PIM-Bidir and PIM-SSM. These are covered in the next sections. The main components of the PIM-SM implementation are: • PIM Sparse Mode v2. • Shared Tree (spt-threshold infinity) A design option in the brokerage or in the exchange. Details of Anycast RP can be found in: The classic high availability design for Tibco in the brokerage network is documented in: Bidirectional PIM. PIM-Bidir is an optimization of PIM Sparse Mode for many-to-many applications. It has several key advantages over a PIM-SM deployment: • Better support for intermittent sources. • No data-triggered events. One of the weaknesses of PIM-SM is that the network continually needs to react to active data flows. This can cause non-deterministic behavior that may be hard to troubleshoot. PIM-Bidir has the following major protocol differences over PIM-SM: – No source registration. Source traffic is automatically sent to the RP and then down to the interested receivers. There is no unicast encapsulation, PIM joins from the RP to the first hop router and then registration stop messages. All PIM-Bidir traffic is forwarded on a *,G forwarding entry. The router does not have to monitor the traffic flow on a *,G and then send joins when the traffic passes a threshold. – No need for an actual RP. The RP does not have an actual protocol function in PIM-Bidir. The RP acts as a routing vector in which all the traffic converges. The RP can be configured as an address that is not assigned to any particular device. This is called a Phantom RP. – No need for MSDP. MSDP provides source information between RPs in a PIM-SM network. PIM-Bidir does not use the active source information for any forwarding decisions and therefore MSDP is not required. Bidirectional PIM is ideally suited for the brokerage network in the data center of the exchange. In this environment there are many sources sending to a relatively few set of groups in a many-to-many traffic pattern. The key components of the PIM-Bidir implementation are: Further details about Phantom RP and basic PIM-Bidir design are documented in: Source Specific Multicast. PIM-SSM is an optimization of PIM Sparse Mode for one-to-many applications. In certain environments it can offer several distinct advantages over PIM-SM. Like PIM-Bidir, PIM-SSM does not rely on any data-triggered events. Furthermore, PIM-SSM does not require an RP at all—there is no such concept in PIM-SSM. The forwarding information in the network is completely controlled by the interest of the receivers. Source Specific Multicast is ideally suited for market data delivery in the financial service provider. The FSP can receive the feeds from the exchanges and then route them to the end of their network. Many FSPs are also implementing MPLS and Multicast VPNs in their core. PIM-SSM is the preferred method for transporting traffic in VRFs. When PIM-SSM is deployed all the way to the end user, the receiver indicates his interest in a particular S,G with IGMPv3. Even though IGMPv3 was defined by RFC 2236 back in October, 2002, it still has not been implemented by all edge devices. This creates a challenge for deploying an end-to-end PIM-SSM service. A transitional solution has been developed by Cisco to enable an edge device that supports IGMPv2 to participate in an PIM-SSM service. This feature is called SSM Mapping and is documented in: Storage Services. The service provides storage capabilities into the market data and trading environments. Trading applications access backend storage to connect to different databases and other repositories consisting of portfolios, trade settlements, compliance data, management applications, Enterprise Service Bus (ESB), and other critical applications where reliability and security is critical to the success of the business. The main requirements for the service are: Storage virtualization is an enabling technology that simplifies management of complex infrastructures, enables non-disruptive operations, and facilitates critical elements of a proactive information lifecycle management (ILM) strategy. EMC Invista running on the Cisco MDS 9000 enables heterogeneous storage pooling and dynamic storage provisioning, allowing allocation of any storage to any application. High availability is increased with seamless data migration. Appropriate class of storage is allocated to point-in-time copies (clones). Storage virtualization is also leveraged through the use of Virtual Storage Area Networks (VSANs), which enable the consolidation of multiple isolated SANs onto a single physical SAN infrastructure, while still partitioning them as completely separate logical entities. VSANs provide all the security and fabric services of traditional SANs, yet give organizations the flexibility to easily move resources from one VSAN to another. This results in increased disk and network utilization while driving down the cost of management. Integrated Inter VSAN Routing (IVR) enables sharing of common resources across VSANs. Figure 18 High Performance Computing Storage. Replication of data to a secondary and tertiary data center is crucial for business continuance. Replication offsite over Fiber Channel over IP (FCIP) coupled with write acceleration and tape acceleration provides improved performance over long distance. Continuous Data Replication (CDP) is another mechanism which is gaining popularity in the industry. It refers to backup of computer data by automatically saving a copy of every change made to that data, essentially capturing every version of the data that the user saves. It allows the user or administrator to restore data to any point in time. Solutions from EMC and Incipient utilize the SANTap protocol on the Storage Services Module (SSM) in the MDS platform to provide CDP functionality. The SSM uses the SANTap service to intercept and redirect a copy of a write between a given initiator and target. The appliance does not reside in the data path—it is completely passive. The CDP solutions typically leverage a history journal that tracks all changes and bookmarks that identify application-specific events. This ensures that data at any point in time is fully self-consistent and is recoverable instantly in the event of a site failure. Backup procedure reliability and performance are extremely important when storing critical financial data to a SAN. The use of expensive media servers to move data from disk to tape devices can be cumbersome. Network-accelerated serverless backup (NASB) helps you back up increased amounts of data in shorter backup time frames by shifting the data movement from multiple backup servers to Cisco MDS 9000 Series multilayer switches. This technology decreases impact on application servers because the MDS offloads the application and backup servers. It also reduces the number of backup and media servers required, thus reducing CAPEX and OPEX. The flexibility of the backup environment increases because storage and tape drives can reside anywhere on the SAN. Trading Resilience and Mobility. The main requirements for this service are to provide the virtual trader: • Fully scalable and redundant campus trading environment. • Resilient server load balancing and high availability in analytic server farms. • Global site load balancing that provide the capability to continue participating in the market venues of closest proximity. A highly-available campus environment is capable of sustaining multiple failures (i.e., links, switches, modules, etc.), which provides non-disruptive access to trading systems for traders and market data feeds. Fine-tuned routing protocol timers, in conjunction with mechanisms such as NSF/SSO, provide subsecond recovery from any failure. The high-speed interconnect between data centers can be DWDM/dark fiber, which provides business continuance in case of a site failure. Each site is 100km-200km apart, allowing synchronous data replication. Usually the distance for synchronous data replication is 100km, but with Read/Write Acceleration it can stretch to 200km. A tertiary data center can be greater than 200km away, which would replicate data in an asynchronous fashion. Figure 19 Trading Resilience. A robust server load balancing solution is required for order routing, algorithmic trading, risk analysis, and other services to offer continuous access to clients regardless of a server failure. Multiple servers encompass a "farm" and these hosts can added/removed without disruption since they reside behind a virtual IP (VIP) address which is announced in the network. A global site load balancing solution provides remote traders the resiliency to access trading environments which are closer to their location. This minimizes latency for execution times since requests are always routed to the nearest venue. Figure 20 Virtualization of Trading Environment. A trading environment can be virtualized to provide segmentation and resiliency in complex architectures. Figure 20 illustrates a high-level topology depicting multiple market data feeds entering the environment, whereby each vendor is assigned its own Virtual Routing and Forwarding (VRF) instance. The market data is transferred to a high-speed InfiniBand low-latency compute fabric where feed handlers, order routing systems, and algorithmic trading systems reside. All storage is accessed via a SAN and is also virtualized with VSANs, allowing further security and segmentation. The normalized data from the compute fabric is transferred to the campus trading environment where the trading desks reside. Wide Area Application Services. This service provides application acceleration and optimization capabilities for traders who are located outside of the core trading floor facility/data center and working from a remote office. To consolidate servers and increase security in remote offices, file servers, NAS filers, storage arrays, and tape drives are moved to a corporate data center to increase security and regulatory compliance and facilitate centralized storage and archival management. As the traditional trading floor is becoming more virtual, wide area application services technology is being utilized to provide a "LAN-like" experience to remote traders when they access resources at the corporate site. Traders often utilize Microsoft Office applications, especially Excel in addition to Sharepoint and Exchange. Excel is used heavily for modeling and permutations where sometime only small portions of the file are changed. CIFS protocol is notoriously known to be "chatty," where several message normally traverse the WAN for a simple file operation and it is addressed by Wide Area Application Service (WAAS) technology. Bloomberg and Reuters applications are also very popular financial tools which access a centralized SAN or NAS filer to retrieve critical data which is fused together before represented to a trader's screen. Figure 21 Wide Area Optimization. A pair of Wide Area Application Engines (WAEs) that reside in the remote office and the data center provide local object caching to increase application performance. The remote office WAEs can be a module in the ISR router or a stand-alone appliance. The data center WAE devices are load balanced behind an Application Control Engine module installed in a pair of Catalyst 6500 series switches at the aggregation layer. The WAE appliance farm is represented by a virtual IP address. The local router in each site utilizes Web Cache Communication Protocol version 2 (WCCP v2) to redirect traffic to the WAE that intercepts the traffic and determines if there is a cache hit or miss. The content is served locally from the engine if it resides in cache; otherwise the request is sent across the WAN the initial time to retrieve the object. This methodology optimizes the trader experience by removing application latency and shielding the individual from any congestion in the WAN. WAAS uses the following technologies to provide application acceleration: • Data Redundancy Elimination (DRE) is an advanced form of network compression which allows the WAE to maintain a history of previously-seen TCP message traffic for the purposes of reducing redundancy found in network traffic. This combined with the Lempel-Ziv (LZ) compression algorithm reduces the number of redundant packets that traverse the WAN, which improves application transaction performance and conserves bandwidth. • Transport Flow Optimization (TFO) employs a robust TCP proxy to safely optimize TCP at the WAE device by applying TCP-compliant optimizations to shield the clients and servers from poor TCP behavior because of WAN conditions. By running a TCP proxy between the devices and leveraging an optimized TCP stack between the devices, many of the problems that occur in the WAN are completely blocked from propagating back to trader desktops. The traders experience LAN-like TCP response times and behavior because the WAE is terminating TCP locally. TFO improves reliability and throughput through increases in TCP window scaling and sizing enhancements in addition to superior congestion management. Thin Client Service. This service provides a "thin" advanced trading desktop which delivers significant advantages to demanding trading floor environments requiring continuous growth in compute power. As financial institutions race to provide the best trade executions for their clients, traders are utilizing several simultaneous critical applications that facilitate complex transactions. It is not uncommon to find three or more workstations and monitors at a trader's desk which provide visibility into market liquidity, trading venues, news, analysis of complex portfolio simulations, and other financial tools. In addition, market dynamics continue to evolve with Direct Market Access (DMA), ECNs, alternative trading volumes, and upcoming regulation changes with Regulation National Market System (RegNMS) in the US and Markets in Financial Instruments Directive (MiFID) in Europe. At the same time, business seeks greater control, improved ROI, and additional flexibility, which creates greater demands on trading floor infrastructures. Traders no longer require multiple workstations at their desk. Thin clients consist of keyboard, mouse, and multi-displays which provide a total trader desktop solution without compromising security. Hewlett Packard, Citrix, Desktone, Wyse, and other vendors provide thin client solutions to capitalize on the virtual desktop paradigm. Thin clients de-couple the user-facing hardware from the processing hardware, thus enabling IT to grow the processing power without changing anything on the end user side. The workstation computing power is stored in the data center on blade workstations, which provide greater scalability, increased data security, improved business continuance across multiple sites, and reduction in OPEX by removing the need to manage individual workstations on the trading floor. One blade workstation can be dedicated to a trader or shared among multiple traders depending on the requirements for computer power. The "thin client" solution is optimized to work in a campus LAN environment, but can also extend the benefits to traders in remote locations. Latency is always a concern when there is a WAN interconnecting the blade workstation and thin client devices. The network connection needs to be sized accordingly so traffic is not dropped if saturation points exist in the WAN topology. WAN Quality of Service (QoS) should prioritize sensitive traffic. There are some guidelines which should be followed to allow for an optimized user experience. A typical highly-interactive desktop experience requires a client-to-blade round trip latency of <20ms for a 2Kb packet size. There may be a slight lag in display if network latency is between 20ms to 40ms. A typical trader desk with a four multi-display terminal requires 2-3Mbps bandwidth consumption with seamless communication with blade workstation(s) in the data center. Streaming video (800x600 at 24fps/full color) requires 9 Mbps bandwidth usage. Figure 22 Thin Client Architecture. Management of a large thin client environment is simplified since a centralized IT staff manages all of the blade workstations dispersed across multiple data centers. A trader is redirected to the most available environment in the enterprise in the event of a particular site failure. High availability is a key concern in critical financial environments and the Blade Workstation design provides rapid provisioning of another blade workstation in the data center. This resiliency provides greater uptime, increases in productivity, and OpEx reduction. Advanced Encryption Standard. Advanced Message Queueing Protocol. Application Oriented Networking. The Archipelago® Integrated Web book gives investors the unique opportunity to view the entire ArcaEx and ArcaEdge books in addition to books made available by other market participants. ECN Order Book feed available via NASDAQ. Chicago Board of Trade. Class-Based Weighted Fair Queueing. Continuous Data Replication. Chicago Mercantile Exchange is engaged in trading of futures contracts and derivatives. Central Processing Unit. Distributed Defect Tracking System. Acesso direto ao mercado. Data Redundancy Elimination. Dense Wavelength Division Multiplexing. Rede de Comunicação Eletrônica. Enterprise Service Bus. Enterprise Solutions Engineering. FIX Adapted for Streaming. Fibre Channel over IP. Financial Information Exchange. Financial Services Latency Monitoring Solution. Financial Service Provider. Information Lifecycle Management. Instinet Island Book. Internetworking Operating System. Keyboard Video Mouse. Low Latency Queueing. Metro Area Network. Multilayer Director Switch. Diretoria de Mercados em Instrumentos Financeiros. Message Passing Interface is an industry standard specifying a library of functions to enable the passing of messages between nodes within a parallel computing environment. Network Attached Storage. Network Accelerated Serverless Backup. Network Interface Card. Nasdaq Quotation Dissemination Service. Order Management System. Open Systems Interconnection. Protocol Independent Multicast. PIM-Source Specific Multicast. Quality of Service. Random Access Memory. Reuters Data Feed. Reuters Data Feed Direct. Remote Direct Memory Access. Regulation National Market System. Remote Graphics Software. Reuters Market Data System. RTP Control Protocol. Real Time Protocol. Reuters Wire Format. Storage Area Network. Small Computer System Interface. Sockets Direct Protocol—Given that many modern applications are written using the sockets API, SDP can intercept the sockets at the kernel level and map these socket calls to an InfiniBand transport service that uses RDMA operations to offload data movement from the CPU to the HCA hardware. Server Fabric Switch. Secure Financial Transaction Infrastructure network developed to provide firms with excellent communication paths to NYSE Group, AMEX, Chicago Stock Exchange, NASDAQ, and other exchanges. It is often used for order routing. The Consolidated Audit Trail. About the CAT Program & General Q&A. As required by the CAT NMS Plan, CATLLC is required to retain an appropriately qualified independent auditor of national recognition (subject to the approval of the Operating Committee by Supermajority Vote, the “Independent Auditor”) and, in collaboration with such Independent Auditor, create and implement an annual audit plan (subject to the approval of the Operating Committee) which shall at a minimum include a review of all Plan Processor policies, procedures and control structures. Additionally, Data Centers housing CAT Systems (whether public or private) must, at a minimum, be AICPA SOC 2 certified by a qualified third-party auditor that is not an affiliate of any of the Participants or the CAT Processor. The frequency of the audit must be at least once per year. The CAT will be structured as an LLC and intends to operate in a manner such that it qualifies as a “business league” within the meaning of Section 501(c) (6) of the Internal Revenue Code. The CAT governance structure, including an Advisory Committee, is defined in the CAT NMS Plan. The Advisory Committee will be a key mechanism for the SROs and the Plan Processor to obtain input and feedback from the industry on a number of CAT-related issues. The composition of the Advisory Committee will include: A broker-dealer with no more than 150 Registered Persons A broker-dealer with at least 151 and no more than 499 Registered Persons A broker-dealer with 500 or more Registered Persons A broker-dealer with a substantial wholesale customer base A broker-dealer that is approved by a national securities exchange (A) to effect transactions on an exchange as a specialist, market maker, or floor broker or (B) to act as an institutional broker on an exchange A proprietary-trading broker-dealer A clearing firm An individual who maintains a securities account with a registered broker or dealer but who otherwise has no material business relationship with a broker or dealer or with a Participant A member of academia with expertise in the securities industry or any other industry relevant to the operation of the CAT System Three institutional investors, including an individual trading on behalf of an investment company or group of investment companies registered pursuant to the Investment Company Act of 1940 An individual with significant and reputable regulatory expertise A service bureau that provides reporting services to one or more CAT Reporters. Timelines and Implementation. CAT processor selected by NMS Plan Participants: No later than two months after effectiveness of the approved CAT NMS Plan. Business clock synchronization for SROs and broker-dealers: No later than four months after effectiveness of the approved CAT NMS Plan. SROs begin submitting data to the central repository: Within one year after effectiveness of the approved CAT NMS Plan. SROs must implement enhanced surveillance using CAT data: Within 14 months after effectiveness of the approved CAT NMS Plan. SRO members, except small members, must begin submitting data to the central repository: Within two years after effectiveness of the approved NMS Plan. Small SRO members must begin submitting data to the central repository: Within three years after effectiveness of the approved NMS Plan. Processo de seleção. CAT Program Funding. Execution Venue Fees: Each Equity Execution Venue will be placed in one of two tiers of fixed fees based on market share, and each Options Execution Venue will be placed in one of two tiers of fixed fees based on market share. Equity Execution Venue market share will be determined by calculating each Equity Execution Venue’s proportion of the total volume of NMS Stock and OTC Equity shares reported by all Equity Execution Venues during the relevant time period. Similarly, market share for Options Execution Venues will be determined by calculating each Options Execution Venue’s proportion of the total volume of Listed Options contracts reported by all Options Execution Venues during the relevant time period. Equity Execution Venues with a larger market share will pay a larger CAT Fee than Equity Execution Venues with a smaller market share. Similarly, Options Execution Venues with a larger market share will pay a larger CAT Fee than Options Execution Venues with a smaller market share. Fees are subject to regulatory review. Any order received by a member of an SRO from any person; Any order originated by a member of an SRO; and Any bid or offer. By one year after the Plan becomes effective, all SROs. By two years after the Plan becomes effective, all members of an SRO, except small broker-dealers, as defined in SEC Rule 0-10(c). By three years after the Plan becomes effective, all SRO members that are small broker-dealers. Receipt or origination; Routing of an order to another broker-dealer, national securities exchange or foreign exchange; Routing of an order between desks or departments within a broker-dealer; Modifications; Cancellations; and Executions. In addition, certain Customer identifying information must be reported to the CAT. Account number; Account type; Customer type; Date account opened; and Large Trader ID, if applicable. Legal Entity Identifier, if available. The Plan also requires the reporting of Customer Identifying Information, which includes, but is not limited to, (a) with respect to individuals: name, address, date of birth, individual tax payer identification number (“ITIN”)/social security number (“SSN”), individual’s role in the account (e.g., primary holder, joint holder, guardian, trustee, person with the power of attorney); and (b) with respect to legal entities: name, address, Employer Identification Number (“EIN”)/Legal Entity Identifier (“LEI”) or other comparable common entity identifier, if applicable; provided, however, that an Industry Member that has an LEI for a Customer must submit the Customer’s LEI in addition to other information of sufficient detail to identify a Customer. In addition to targeted analysis of data from the Central Repository, Participants and other regulators will need the ability to do bulk extraction and download of data, based on a specified date or time range, market, security, and Customer-ID. The Plan Processor must provide for bulk extraction and download of data in industry standard formats. Monitoring and Surveillance. Definições. purposes of the CAT: the end customer, the introducing broker or the clearing broker? The end customer is the ultimate customer, and Rule 613 defines that entity to be (i) the account holder(s) of the account at the broker-dealer originating the order and (ii) any person(s) from whom the broker-dealer is authorized to accept trading instructions for the account, if different from the account holder(s). Under the CAT NMS Plan, CAT Reporters will be required to report existing customer identifiers (i.e., a unique identifier for each trading account provided by each CAT Reporter for its customers)), along with certain customer identifying information. The Plan Processor will use this information to assign a Customer-ID to each customer, and to link all reportable order events associated with an order for a customer. (1) had total capital (net worth plus subordinated liabilities) of less than $500,000 on the date in the prior fiscal year as of which its audited financial statements were prepared pursuant to 240.17a5(d) or, if not required to file such statements, a broker or dealer that had total capital (net worth plus subordinated liabilities) of less than $500,000 on the last business day of the preceding fiscal year (or in the time that it has been in business, if shorter). For these reasons, and based on industry feedback and analysis conducted by the SROs, the CAT will utilize a “daisy chain” approach for the CAT-Order-ID framework whereby existing Order IDs will be used (as opposed to the single CAT-Order-ID approach referenced above). Technical Documentation. (a) Publication and Implementation of the Methods for Providing Information to the Customer-ID Database: Industry Members (other than Small Industry Members) Milestones and Projected Completion Date. Plan Processor begins developing the procedures, connectivity requirements and Technical Specifications for Industry Members to Report Customer Account Information to the Central Repository: No later than 15 months before Industry Members (other than Small Industry Members) are required to begin reporting data to the Central Repository. Plan Processor publishes iterative drafts of the procedures, connectivity requirements and Technical Specifications for Industry Members to Report Customer Account Information to the Central Repositor: As needed before publishing the final documents. Plan Processor publishes the procedures, connectivity requirements and Technical Specifications for Industry Members to report Customer Account Information to the Central Repository: No later than 6 months before Industry Members (other than Small Industry Members) are required to begin reporting data to the Central Repository. (b) Submission of Order and MM Quote Data to Central Repository: Participants. Milestones and Projected Completion Date. Plan Processor begins developing Technical Specification(s) for Participant submission of order and MM Quote data: No later than 10 months before Participants are required to begin reporting data to the Central Repository. Plan Processor publishes iterative drafts of Technical Specification(s): As needed before publishing the final documents. Plan Processor publishes Technical Specification(s) for Participant submission of order and MM Quote data: No later than 6 months before Participants are required to begin reporting data to the Central Repository. Industry Members (other than Small Industry Members) Milestones and Projected Completion Date. Plan Processor begins developing Technical Specification(s) for Industry Members submission of order data: No later than 15 months before Industry Members (other than Small Industry Members) are required to begin reporting data to the Central Repository. Plan Processor publishes iterative drafts of Technical Specification(s): As needed before publishing the final documents. Plan Processor publishes Technical Specification(s) for Industry Member submission of order data: No later than 1 year before Industry Members (other than Small Industry Members) are required to begin reporting data to the Central Repository.
Sistemas de balaustrada externos
Requisitos da interface comercial do sistema de exportação automatizada (aestir)