Data Currency in Replicated DHTs

Abstract : Distributed Hash Tables (DHTs) provide a scalable solution for data sharing in P2P systems. To ensure high data availability, DHTs typically rely on data replication, yet without data currency guarantees. Supporting data currency in replicated DHTs is difficult as it requires the ability to return a current replica despite peers leaving the network or concurrent updates. In this paper, we give a complete solution to this problem. We propose an Update Management Service (UMS) to deal with data availability and efficient retrieval of current replicas based on timestamping. For generating timestamps, we propose a Key-based Timestamping Service (KTS) which performs distributed timestamp generation using local counters. Through probabilistic analysis, we compute the expected number of replicas which UMS must retrieve for finding a current replica. Except for the cases where the availability of current replicas is very low, the expected number of retrieved replicas is typically small, e.g. if at least 35% of available replicas are current then the expected number of retrieved replicas is less than 3. We validated our solution through implementation and experimentation over a 64-node cluster and evaluated its scalability through simulation up to 10,000 peers using SimJava. The results show the effectiveness of our solution. They also show that our algorithm used in UMS achieves major performance gains, in terms of response time and communication cost, compared with a baseline algorithm.
Type de document :
Communication dans un congrès
ACM International Conference on Management of Data (SIGMOD), Jun 2007, Beijing, China. pp.211-222, 2007
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Contributeur : Reza Akbarinia <>
Soumis le : lundi 15 février 2010 - 16:25:00
Dernière modification le : mardi 4 décembre 2018 - 14:32:01
Document(s) archivé(s) le : jeudi 10 juin 2010 - 21:58:52


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  • HAL Id : inria-00378860, version 1



Reza Akbarinia, Esther Pacitti, Patrick Valduriez. Data Currency in Replicated DHTs. ACM International Conference on Management of Data (SIGMOD), Jun 2007, Beijing, China. pp.211-222, 2007. 〈inria-00378860〉



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