Lightweight Causal Cluster Consistency
Within an effort for providing a layered architecture of services supporting multi-peer collaborative applications, this paper proposes a new type of consistency management aimed for applications where a large number of processes share a large set of replicated objects. Many such applications, like peer-to-peer collaborative environments for training or entertaining purposes, platforms for distributed monitoring and tuning of networks, rely on a fast propagation of updates on objects, however they also require a notion of consistent state update. To cope with these requirements and also ensure scalability, we propose the cluster consistency model. We also propose a two-layered architecture for providing cluster consistency. This is a general architecture that can be applied on top of the standard Internet communication layers and offers a modular, layered set of services to the applications that need them. Further, we present a fault-tolerant protocol implementing causal cluster consistency with predictable reliability, running on top of decentralised probabilistic protocols supporting group communication. Our experimental study, conducted by implementing and evaluating the two-layered architecture on top of standard Internet transport services, shows that the approach scales well, imposes an even load on the system, and provides high-probability reliability guarantees.
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- 3.Carlsson, C., Hagsand, O.: DIVE - a multi-user virtual reality system. In: Proc. of the IEEE Annual Int. Symp., pp. 394–400 (1993)Google Scholar
- 4.Lamport, L.: Time, clocks, and the ordering of events in a distributed system. Communications of the ACM 7(21), 558–565 (1978)Google Scholar
- 10.Rodrigues, L., Baldoni, R., Anceaume, E., Raynal, M.: Deadline-constrained causal order. In: Proc. of the 3rd IEEE Int. Symp. on Object-oriented Real-time distributed Computing (2000)Google Scholar
- 11.Mattern, F.: Virtual time and global states of distributed systems. In: Proc. Of the Int. Workshop on Parallel and Distributed Algorithms, pp. 215–226 (1989)Google Scholar
- 13.Eugster, P.T., Guerraoui, R., Handurukande, S.B., Kermarrec, A.M., Kouznetsov, P.: Lightweight probabilistic broadcast. In: Proc. of the Int. Conf. on Dependable Systems and Networks, pp. 443–452 (2001)Google Scholar
- 15.Koldehofe, B.: Buffer management in probabilistic peer-to-peer communication protocols. In: Proc. of the 22nd Symp. on Reliable Distributed Systems, pp. 76–85. IEEE, Los Alamitos (2003)Google Scholar
- 16.Pereira, J., Rodrigues, L., Monteiro, M., Kermarrec, A.M.: NEEM: Networkfriendly epidemic multicast. In: Proc. of the 22nd Symp. on Reliable Distributed Systems, pp. 15–24. IEEE, Los Alamitos (2003)Google Scholar
- 17.Baehni, S., Eugster, P.T., Guerraoui, R.: Data-aware multicast. In: Proc. of the 5th IEEE Int. Conf. on Dependable Systems and Networks, pp. 233–242 (2004)Google Scholar
- 18.Stoica, I., Morris, R., Karger, D., Kaashoek, F., Balakrishnan, H.: Chord: A scalable Peer-To-Peer lookup service for internet applications. In: Proc. of the ACM SIGCOMM 2001 Conf., pp. 149–160. ACM Press, New York (2001)Google Scholar
- 25.Gidenstam, A., Koldehofe, B., Papatriantafilou, M., Tsigas, P.: Dynamic and faulttolerant cluster management. In: Proc. of the 5th IEEE Int. Conf. on Peer-to-Peer Computing. IEEE, Los Alamitos (2005)Google Scholar
- 27.Gidenstam, A., Koldehofe, B., Papatriantafilou, M., Tsigas, P.: Lightweight causal cluster consistency. Technical Report 2005-09, Computer Science and Engineering, Chalmers University of Technology (2005)Google Scholar