An efficient Δ-causal algorithm for real-time distributed systems

The study presents an efficient Δ-causal algorithm for the transmission of real-time continuous media (e.g., audio and video) in distributed systems. The Δ-causal algorithm is oriented to be used in real-time collaborative applications, such as Teleconferece and Teleimmersion. The Δ-causal algorithm...

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Detalhes bibliográficos
Autores: SAUL EDUARDO POMARES HERNANDEZ, EDUARDO LOPEZ DOMINGUEZ, GUSTAVO RODRIGUEZ GOMEZ
Tipo de documento: artigo
Estado:Versión aceptada para publicación
Data de publicação:2009
País:México
Recursos:Instituto Nacional de Astrofísica, Óptica y Electrónica
Repositório:Repositorio Institucional del INAOE
Idioma:inglês
OAI Identifier:oai:inaoe.repositorioinstitucional.mx:1009/1192
Acesso em linha:http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/1192
Access Level:Acceso aberto
Palavra-chave:info:eu-repo/classification/Real-time distributed systems/Real-time distributed systems
info:eu-repo/classification/Group communication/Group communication
info:eu-repo/classification/∆-causal order/∆-causal order
info:eu-repo/classification/cti/1
info:eu-repo/classification/cti/12
info:eu-repo/classification/cti/1203
Descrição
Resumo:The study presents an efficient Δ-causal algorithm for the transmission of real-time continuous media (e.g., audio and video) in distributed systems. The Δ-causal algorithm is oriented to be used in real-time collaborative applications, such as Teleconferece and Teleimmersion. The Δ-causal algorithm ensures causal delivery of messages with time constraints in partial-reliable and asynchronous networks without using global references. To achieve this, the algorithm introduces an original Forward Error Correction (FEC) mechanism and a method to calculate the message lifetime based on relative time points. One interesting aspect of the FEC mechanism is that the redundant data sent is dynamically adapted according to the behavior of the system. Finally, it is shown that the Δ-causal algorithm is efficient in terms of the overhead (causal history) attached per message.