P2P Content Distribution Networks Research Articles

A novel fuzzy programming approach for piece selection problem in P2P content distribution network.

Piece selection policy in dynamic P2P networks play crucial role and avoid the last piece problem. BitTorrent uses rarest-first piece selection mechanism to deal with this problem, but its efficacy is limited because each peer only has a local view of piece rareness. The problem of piece section is multiple objectives. A novel fuzzy programming approach is introduced in this article to solve the multiple objectives piece selection problem in P2P network, in which some of the factors are fuzzy in nature. Piece selection problem has been prepared as a fuzzy mixed integer goal programming piece selection problem that includes three primary goals such as minimizing the download cost, time, maximizing speed and useful information transmission subject to realistic constraints regarding peer's demand, capacity and dynamicity. The proposed approach has the ability to handle practical situations in a fuzzy environment and offers a better decision tool to each peer to select optimal pieces to download from other peers in dynamic P2P network. Extensive simulations are carried out to demonstrate the effectiveness of the proposed model. It is proved that proposed system outperforms existing with respect to download cost, time and meaningful exchange of useful information.

Contracting Models for P2P Content Distribution

In recent years, peer‐to‐peer (P2P) networks have become an increasingly popular method for distributing digital content. In this study, we consider the development of optimal contracts for a P2P network by a profit‐seeking provider to support the operations of an online file exchange service. By utilizing the principal‐agent model of incentive theory, we propose appropriate reward and pricing schemes for profit‐seeking P2P content distribution networks. We show that when peers are homogeneous, upload compensation increases with propagation delay uncertainty, maximum uploading nodes allowed, peers' provision cost and disutility of download delay, but decreases with the network size and content availability. We also characterize a general contracting model where there are a countable number of peer classes which are heterogeneous in their provisioning costs. For the case of two peer classes where optimal delays are separable, we derive the optimal upload compensations under different scenarios and show that the impact of operational parameters is quite similar to the case of homogeneous peers, lending support to the robustness of our analysis.

An Efficient Framework for Large Scale Multimedia Content Distribution in P2P Network: I2NC.

Network coding (NC) makes content distribution more effective and easier in P2P content distribution network and reduces the burden of the original seeder. It generalizes traditional network routing by allowing the intermediate nodes to generate new coded packet by combining the received packets. The randomization introduced by network coding makes all packets equally important and resolves the problem of locating the rarest block. Further, it reduces traffic in the network. In this paper, we analyze the performance of traditional network coding in P2P content distribution network by using a mathematical model and it is proved that traffic reduction has not been fully achieved in P2P network using traditional network coding. It happens due to the redundant transmission of noninnovative information block among the peers in the network. Hence, we propose a new framework, called I2NC (intelligent-peer selection and incremental-network coding), to eliminate the unnecessary flooding of noninnovative coded packets and thereby to improve the performance of network coding in P2P content distribution further. A comparative study and analysis of the proposed system is made through various related implementations and the results show that 10–15% of traffic reduced and improved the average and maximum download time by reducing original seeder's workload.

Clustering Model of P2P CDN Based on The Prediction of User Requirements

With the increasing scale of the P2P (Peer to Peer) network, the contradictions among the scope expanding, the time expending reducing and the network loading of content search is growing more serious. As the required contents and replicas are distributed in the P2P network, the searching scope reduction will result in achieving less or even no required replicas. Contrarily, expanding the searching scope will increasing the search time and network loading. In this paper, an clustering model of P2P content distribution network (CDN) based on the prediction of user requirements is proposed in which the organization of the P2P CDN is clustered continuously according to the improvement of the prediction of user requirements. In the model, the peers and the surrogates that have the requiring contents of a designate peer will be assembled at nearby locations to the designated peer, and then more replicas can be found in smaller search scope and shorter time expending. Simulative experiments indicate the clustering model can acquire much smaller mean response time (MRT) and mean distribution time (MDT).

Mastering selfishness and heterogeneity in mobile P2P content distribution networks with multiple source download in cellular networks

The performance of Peer-to-Peer (P2P) content distribution networks depends highly on the coordination of the peers. This is especially true for cellular networks with mobile and often selfish users, as the resource constraints on accessible bandwidth and battery power are even more limitating in this context. Thus, it is a major challenge to identify mobile network specific problems and to develop sophisticated cooperation strategies to overcome these difficulties. Cooperation strategies, which are able to cope these problems, are the foundation for efficient mobile file exchange. The detailed performance of the strategies are determined by the peer capabilities and the peer behavior, such as the number of parallel upload connections, the selfishness, or the altruistic re-distribution of data. The purpose of this work is to evaluate and investigate different cooperation strategies which are based on multiple source download and select the best one for mobile scenarios with even leeching peers, i.e. peers which depart as soon as they have finished their download. The question arises whether the cooperation strategy can smoothen the overall performance degradation caused by a selfish peer behavior. As performance indicators the efficiency, fairness, and robustness of the cooperation strategies are applied. The considered scenarios comprise best-case (altruistic peers) and worst-case scenarios (selfish peers). We further propose a new cooperation strategy to improve the file transfer even when mainly selfish peers are present, the CycPriM (cyclic priority masking) strategy. The strategy allows an efficient P2P based content distribution using ordered chunk delivery with only local information available at a peer.