We propose a distributed data replenishment mechanism for some distributed peer-to-peer-based storage systems that automates the process of maintaining a sufficient level of data redundancy to ensure the availability of data in presence of peer departures and failures. The dynamics of peers entering and leaving the network are modeled as a stochastic process. A novel analytical time-backward technique is proposed to bound the expected time for a piece of data to remain in P2P systems. Both theoretical and simulation results are in agreement, indicating that the data replenishment via random linear network coding (RLNC) outperforms other popular strategies. Specifically, we show that the expected time for a piece of data to remain in a P2P system, the longer the better, is exponential in the number of peers used to store the data for the RLNC-based strategy, while they are quadratic for other strategies.

We investigate a class of randomized peer-to-peer (P2P) approach to Internet-wide distributed data storage systems that promises to reduce the coordination complexity and increases performance scalability. The core of these randomized P2P data storage systems is the data replenishment mechanism. The data replenishment automates the process of maintaining a sufficient level of data redundancy to ensure the availability of data in presence of peer departures and failures. The dynamics of peers entering and leaving the network is modeled as a stochastic process. A novel analytical time-backward technique is proposed to bound the expected time for a piece of data to remain in P2P systems. Both theoretical and simulation results are in agreement, indicating that a proposed data replenishment via random linear network coding (RLNC) outperforms other strategies that employ popular repetition and channel coding techniques. Specifically, we show that the expected time for a piece of data to remain in a P2P system, the longer the better, is exponential in the redundancy amount for the RLNC-based strategy, while they are quadratic for other strategies.

This work addresses the uneven energy consumption problem in data gathering sensor networks where the nodes closer to the sink tend to consume more energy than those of the farther nodes. This energy unfairness can significantly shorten the life time of a sensor network. We propose a novel cross-sensor coding technique using On-Off keying which exploits (a) the trade-off between delay and bandwidth and (b) the network topology in order to reduce the overall energy consumption of the network, and to alleviate the problem of unequal energy consumption. We formulate our coding problem as an integer programming problem, and construct a number of codes based on different criteria. We show that using our proposed technique under certain assumptions, the energy consumption can be reduced significantly and the unequal energy consumption problem can be practically eliminated.

Current video streaming and storage systems such as YouTube, are based on the client-server model, thus do not scale well in terms of bandwidth and computation. This paper describes a peer-to-peer video delivery network (P2P-VDN) that provides both performance improvement and scalability based on three architectural elements. First, the proposed P2P-VDN employs a random network coding (RNC) scheme that breaks a video stream into multiple smaller pieces, codes, and disperses them throughout peers in the network, in such a way to maximize the probability of recovering the original video under peer departures and failures. Second, the proposed P2P-VDN employs a scalable mechanism for automating the data replenishment process using RNC that is necessary to maintain a sufficient level of redundancy for video stored in the network. Third, the proposed P2P-VDN employs a path-diversity protocol for a client to simultaneously stream a video from multiple peers in the P2P-VDN. Simulations demonstrate that under certain scenarios, our proposed P2P-VDN can result in bandwidth saving up to 60% over the traditional architecture.

In recent years, Content Delivery Networks (CDN) and Peer-to-Peer (P2P) networks have emerged as two effective paradigms for delivering multimedia contents over the Internet. An important feature in CDN and P2P networks is the data redundancy across multiple servers/peers which enables efficient media delivery. In this paper, we propose a network coding framework for efficient media streaming in either content delivery networks or P2P networks in which, multiple servers/peers are employed to simultaneously stream a video to a single receiver. Unlike previous multi-sender schemes, we show that network coding technique can (a) reduce the redundancy storage, (b) eliminate the need for tight synchronization between the senders, and (c) be integrated easily with TCP. Furthermore, we propose the Hierarchical Network Coding (HNC) technique to be used with scalable video bit stream to combat bandwidth fluctuation on the Internet. Simulation results demonstrate that our proposed scheme can result in bandwidth saving up to 40% for many cases over the traditional schemes.