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Computación y Sistemas

Print version ISSN 1405-5546

Comp. y Sist. vol.14 n.1 México Jul./Sep. 2010




An Efficient Δ–Causal Distributed Algorithm for Synchronous Cooperative Systems in Unreliable Networks


Algoritmo Eficiente Distribuido Δ–Causal para Sistemas Cooperativos Síncronos sobre Redes no Fiables


Saúl E. Pomares Hernández, Eduardo López Domínguez and Gustavo Rodríguez Gómez


Department of Computer Science, National Institute of Astrophysics, Optics and Electronics (INAOE) Luis Enrique Erro No. 1, Tonantzintla, Puebla, Mexico, C.P. 72840,,


Article received on July 03, 2008
Accepted on March 23, 2009



In cooperative systems causal ordering delivery has been used to resolve problems of coherency of type producer–consumer. Causal order delivery is important for distributed systems since it allows an asynchronous execution to participants. When time delivery constraints are considered, ensuring causal delivery becomes more complex, as is the case for synchronous cooperative systems, such as Telemedicine and Teleimmersion. In these systems, the messages (units of data of continuous and discrete media) have an associated lifetime that determines the period of useful time in which the messages must be delivered. On the other hand, generally in these systems there is no time for retransmit them when messages are lost. Causal order with time constraints has previously been addressed, and it is called Δ–causal order. In this paper, we present an efficient Δ–causal distributed algorithm for unreliable networks that is characterized by the use of a forward error correction (FEC) scheme and a distributed method to calculate the message lifetime based on relative time points (i.e. no global time is used). We show the efficiency of our Δ–causal algorithm in terms of the control information attached per message.

Keywords: Cooperative systems, Group communication, Causal order.



En los sistemas cooperativos el ordenamiento causal ha sido usado para resolver problemas de coherencia de tipo productor–consumidor. La entrega de orden causal es importante en general para los sistemas distribuidos debido a que permite a los participantes una ejecución asíncrona. Cuando las restricciones de entrega en tiempo real son contempladas, asegurar la entrega causal se vuelve más complejo, como es el caso para los sistemas cooperativos síncronos, tales como Telemedicina y Teleinmersión. En estos sistemas, los mensajes (datos continuos y discretos) tienen asociado un tiempo de vida que determina el periodo de tiempo útil en cual los mensajes deben ser entregados, y por el otro lado, en general en estos sistemas, cuando los mensajes son perdidos no existe tiempo para retransmitirlos. El orden causal con restricciones de tiempo ha sido previamente estudiado, y es nombrado orden Δ–causal. En este trabajo, presentamos un algoritmo distribuido Δ–causal eficiente sobre redes no fiables, nuestro algoritmo se caracteriza por el uso de un esquema de corrección de errores hacia delante (FEC) y un método distribuido para calcular el tiempo de vida de un mensaje basado en puntos de tiempo relativo (ningún tiempo global es utilizado). Mostramos la eficiencia de nuestro algoritmo Δ–causal en términos de la información de control unida a cada mensaje.

Palabras clave: Sistemas cooperativos, Comunicación en grupo, Orden causal.





1. Baldoni, R., Raynal, M., Prakash, R., & Singhal M. (1996). Broadcast with Time and Causality Constraints for Multimedia Applications, 22nd EUROMICRO Conference '96, Beyond 2000: Hardware and Software Design Strategie, Prague, Czech Republic, 617–624.         [ Links ]

2. Baldoni, R., Prakash, R., Raynal, M., & Singhal, M. (1998). Efficient Δ–causal broadcasting. International Journal of Computer Systems Science and Engineering, 13(5), 263–269.         [ Links ]

3. Birman, K. (1993). The Process Group Approach to Reliable Distributed Computing, Communications of the ACM, 36(12), 36–53.         [ Links ]

4. Kshemkalyani, A. D. & Singhal, M. (1998). Necessary and Sufficient Conditions on Information for Causal Message Ordering and their Optimal Implementation, Distributed Computing Journal, 11( 2), 91–111.         [ Links ]

5. Lamport, L. (1978). Time, Clocks and the Ordering of Events in a Distributed System, Communications of the ACM, 21(7), 558–565.         [ Links ]

6. Lopez, E., Estudillo J., Fanchon J., & Pomares Hernandez, S.E. (2005). A Fault–tolerant Causal Broadcast Algorithm to be Applied to Unreliable Networks, 17th International Conference on Parallel and Distributed Computing and Systems, Phoenix, Arizona, USA, 465–470.         [ Links ]

7. Mattern, F. (1989). Virtual Time and Global States of Distributed Systems, International Workshop on Parallel and Distributed Algorithms, Chateau de Bonas, France, 215–226.         [ Links ]

8. Olsen, J. (2003). Stochastic Modeling and Simulation of the TCP Protocol, PhD thesis, Uppsala University, Uppsala, Sweden.         [ Links ]

9. Perkins, C. (2003). RTP Audio and Video for Internet, Boston : Addison Wesley.         [ Links ]

10. Plesca, C., Grigoras, R., Queinnec, P., & Padiou G. (2005). A Flexible Communication Toolkit for Synchronous Groupware, 2005 Systems Communications, Washington, DC, USA, 216–221.         [ Links ]

11. Pomares Hernandez, S.E., Drira, K., Fanchon J., & Diaz, M. (2002). An Efficient Multi–Channel Distributed Coordination Protocol for Collaborative Engineering Activities, IEEE International Conference on Systems, Man and Cybernetics, Hammamet, Tunisia, 415 – 420.         [ Links ]

12. Pomares Hernandez, S.E., Fanchon, J., & Drira, K. (2004). The Inmediate Dependency Relation: An Optimal Way to Ensure Causal Group Communication, Annual Review of Scalable Computing, 6(1), 61–79.         [ Links ]

13. Prakash, R., Raynal, M., & Singhal, M. (1997). An Adaptive Causal Ordering Algorithm Suited to Mobile Computing Environment, Journal of Parallel and Distributed Computing, 41(2), 190–204.         [ Links ]

14. Tachikawa, T., & Takizawa, M. (1997). Δ–Causality in Wide–Area Group Communications, International Conference on Parallel and Distributed Systems, Seoul, Korea, 260–267.         [ Links ]

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