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Abstract. This paper evaluates the performance of two schemes for recovering lost data in a peer-to-peer (P2P) storage systems. The first scheme is centralized and relies on a server that recovers multiple losses at once, whereas the second one is distributed. By representing the state of each scheme by an absorbing Markov chain, we are able to compute their performance in terms of the delivered data lifetime and data availability. Numerical computations are provided to better illustrate the impact of each system parameter on the performance. Depending on the context considered, we provide guidelines on how to tune the system parameters in order to provide a desired data lifetime.

Abstract. This paper studies the performance of Peer-to-Peer Storage Systems (P2PSS) in terms of data lifetime and availability. Two schemes for recovering lost data are modeled through absorbing Markov chains and their performance are evaluated and compared. The first scheme relies on a centralized controller that can recover multiple losses at once, whereas the second scheme is distributed and recovers one loss at a time. The impact of each system parameter on the performance is evaluated, and guidelines are derived on how to engineer the system and tune its key parameters in order to provide desired lifetime and/or availability of data. We find that, in stable environments such as local area or research laboratory networks where machines are usually highly available, the distributed-repair scheme offers a reliable, scalable and cheap storage/backup solution. This is in contrast with the case of highly dynamic environments, where the distributed-repair scheme is inefficient as long as the storage overhead is kept reasonable. P2PSS with centralized-repair scheme are efficient in any environment but have the disadvantage of relying on a centralized authority. Our analysis also suggests that the use of large size fragments reduces the efficiency of the recovery mechanism.

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International audienceResponse time is the primary Quality of Service metric for parallel download systems, where pieces of large files can be simultaneously downloaded from several servers. Determining response times in such systems is still a difficult issue, because the way the network bandwidth is shared between flows is as yet not well understood. We address the issue by exploring the practical relevance of the hypothesis that flows share the network bandwidth according to the max- min fairness paradigm. We have implemented into a flow-level simulator a version of the algorithm, which calculates such a bandwidth allocation, which we have called the "progressive- filling flow-level algorithm" (PFFLA). We have programmed a similar model over NS2 and compared the empirical distri- butions resulting from both simulations. Our results indicate that flow-level predictions are very accurate in symmetric networks and good in asymmetric networks. Therefore, PFFLA would be extremely useful to build flow-level simulators and, possibly, to perform probabilistic QoS calculations in general P2P networks