Storage and Search in Dynamic Peer-to-Peer Networks

We study robust and efficient distributed algorithms for searching, storing, and maintaining data in dynamic Peer-to-Peer (P2P) networks. P2P networks are highly dynamic networks that experience heavy node churn (i.e., nodes join and leave the network continuously over time). Our goal is to guarantee, despite high node churn rate, that a large number of nodes in the network can store, retrieve, and maintain a large number of data items. Our main contributions are fast randomized distributed algorithms that guarantee the above with high probability (whp) even under high adversarial churn: 1. A randomized distributed search algorithm that (whp) guarantees that searches from as many as $n - o(n)$ nodes ($n$ is the stable network size) succeed in ${O}(\log n)$-rounds despite ${O}(n/\log^{1+\delta} n)$ churn, for any small constant $\delta > 0$, per round. We assume that the churn is controlled by an oblivious adversary (that has complete knowledge and control of what nodes join and leave and at what time, but is oblivious to the random choices made by the algorithm). 2. A storage and maintenance algorithm that guarantees (whp) data items can be efficiently stored (with only $\Theta(\log{n})$ copies of each data item) and maintained in a dynamic P2P network with churn rate up to ${O}(n/\log^{1+\delta} n)$ per round. Our search algorithm together with our storage and maintenance algorithm guarantees that as many as $n - o(n)$ nodes can efficiently store, maintain, and search even under ${O}(n/\log^{1+\delta} n)$ churn per round. Our algorithms require only polylogarithmic in $n$ bits to be processed and sent (per round) by each node. To the best of our knowledge, our algorithms are the first-known, fully-distributed storage and search algorithms that provably work under highly dynamic settings (i.e., high churn rates per step).Comment: to appear at SPAA 201

A game theoretic approach to a peer-to-peer cloud storage model

Classical cloud storage based on external data providers has been recognized to suffer from a number of drawbacks. This is due to its inherent centralized architecture which makes it vulnerable to external attacks, malware, technical failures, as well to the large premium charged for business purposes. In this paper, we propose an alternative distributed peer-to-peer cloud storage model which is based on the observation that the users themselves often have available storage capabilities to be offered in principle to other users. Our set-up is that of a network of users connected through a graph, each of them being at the same time a source of data to be stored externally and a possible storage resource. We cast the peer-to-peer storage model to a Potential Game and we propose an original decentralized algorithm which makes units interact, cooperate, and store a complete back up of their data on their connected neighbors. We present theoretical results on the algorithm as well a good number of simulations which validate our approach.Comment: 10 page

Understanding the Session Durability in Peer-to-Peer Storage System

This paper emphasizes that instead of long-term availability and reliability, the short-term session durability analysis will greatly impact the design of the real large-scale Peer-to-Peer storage system. In this paper, we use a Markov chain to model the session durability, and then derive the session durability probability distribution. Subsequently, we show the difference between our analysis and the traditional Mean Time to Failure (MTTF) analysis, from which we conclude that the misuse of MTTF analysis will greatly mislead our understanding of the session durability. We further show the impact of session durability analysis on the real system design. To our best knowledge, this is the first time ever to discuss the effects of session durability in large-scale Peer-to-Peer storage system.Computer Science, Theory & MethodsSCI(E)EICPCI-S(ISTP)

Analysis of Failure Correlation in Peer-to-Peer Storage Systems

In this paper, we propose and study analytical models of self-repairing peer-to-peer storage systems subject to failures. The failures correspond to the simultaneous loss of multiple data blocks due to the definitive loss of a peer (or following a disk crash). In the system we consider that such failures happen continuously, hence the necessity of a self-repairing mechanism (data are written once for ever). We show that, whereas stochastic models of independent failures similar to those found in the literature give a correct approximation of the average behavior of real systems, they fail to capture their variations (e.g. in bandwidth needs). We propose to solve this problem using a new stochastic model based on a fluid approximation and we give a characterization of the behavior of the system according to this model (expectation and standard deviation). This new model is validated using comparisons between its theoretical behavior and computer simulations

Key-value storage system synchronization in peer-to-peer environments

Data synchronization is the problem of bringing multiple versions of the same data on different remote devices to the most up to date version. This thesis looks into the particular problem of key-value storage systems synchronization between mobile devices in a peer-to-peer environment. In this research, we describe, implement and evaluate a new key-value storage system synchronization algorithm using a 2-phase approach, combining approximate synchronization in the first phase and exact synchronization in the second phase. The 2-phase architecture helps the algorithm achieve considerable boost in performance in all three major criteria of a data synchronization algorithm, namely synchronization time, processing time and communication cost, while still being suitable to operate in a peer-to-peer environment. The performance increase makes it feasible to employ database synchronization technique in a wider range of mobile applications, especially those operating on a slow peer-to-peer network