Minimizing File Download Time in Stochastic Peer-to-Peer Networks

Minimizing File Download Time in Stochastic Peer-to-Peer Networks

Abstract:

The peer-to-peer (P2P) file-sharing applications are becoming increasingly popular and account for more than 70% of the Internet’s bandwidth usage. Measurement studies show that a typical download of a file can take from minutes up to several hours depending on the level of network congestion or the service capacity fluctuation. In this paper, we consider two major factors that have significant impact on average download time, namely, the spatial heterogeneity of service capacities in different source peers and the temporal fluctuation in service capacity of a single source peer. We point out that the common approach of analyzing the average download time based on average service capacity is fundamentally flawed. We rigorously prove that both spatial heterogeneity and temporal correlations in service capacity increase the average download time in P2P networks and then

analyze a simple, distributed algorithm to effectively remove these negative factors, thus minimizing the average download time. We show through analysis and simulations that it outperforms most of other algorithms currently used in practice under various network configurations.

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Existing system:

PEER-TO-PEER (P2P) technology is heavily used for content distribution applications. The early model for content distribution is a centralized one, in which the service provider simply sets up a server and every user downloads files from it. In this type of network architecture (server-client), many users have to compete for limited resources in terms of bottleneck bandwidth or processing power of a single server. As a result, each user may receive very poor performance. From a single user’s perspective, the duration of a download session, or the download time for that individual user is the most often used performance metric.

However, there have been very few results in minimizing the download time for each user in a P2P network. In recent work, the problem of minimizing the download time is formulated as an optimization problem by maximizing the aggregated service capacity over multiple simultaneous active links (parallel connections) under some global constraints. There are two major issues in this approach. One is that global information of the peers in the network is required, which is not practical in real world. The other is that the analysis is based on the averaged quantities, e.g., average capacities of all possible source peers in the network. The approach of using the average service capacity to analyze the average download time has been a common practice in the literature.

Proposed system:

In this paper, we first characterize the relationship between the heterogeneity in service capacity and the average download time for each user, and show that the degree of diversity in service capacities has negative impact on the average download time. After we formally define the download time over a stochastic capacity process, we prove that the correlations in the capacity make the average download time much larger than the commonly accepted value , where is the average capacity of the source peer. It is thus obvious that the average download time will be reduced if there exists a (possibly distributed) algorithm that can efficiently eliminate the negative impact of both the heterogeneity in service capacities over different source peers and the correlations in time of a given source peer.

In practice, most P2P applications try to reduce the download time by minimizing the risk of getting stuck with a ‘bad’ source peer (the connection with small service capacity) by using smaller file sizes and/or having them downloaded over different source peers (e.g., parallel download). In other words, they try to reduce the download time by minimizing the bytes transferred from the source peer with small capacity. However, we show in this paper that this approach cannot effectively remove the negative impact of both the correlations

in the available capacity of a source peer and the heterogeneity in different source peers. This approach may help to reduce average download time in some cases but not always. Rather, a simple and distributed algorithm that limits the amount of time each peer spends on a bad source peer, can minimize the average download time for each user almost in all cases as we will show in our paper. Through extensive simulations, we also verify that the simple download strategy outperforms all other schemes widely used in practice under various network configurations. In particular, both the average download time and the variation in download time of our scheme are smaller than any other scheme when the network is heterogeneous (possibly correlated) and many downloading peers coexist with source peers, as is the case in reality.

Modules:

Parallel Downloading

File is divided into k chunks of equal size and k simultaneous connections are used. Client downloads a file from k peers at a time. Each peer sends a chunk to the client.

Random chunk Based  Downloading     

File is divided into many chunks and user downloads chunks sequentially one at time. Whenever a user completes a chunk from its current source peer, the user randomly selects a new source peer and connects to it to retrieve a new chunk. Switching source peers based on chunk can reduce average download time.

Random Periodic Switching      

File is divided into many chunks and user downloads chunks sequentially one at a time. The client randomly chooses the source peer at each time slot and download the chunks from each peer in the given time slots.

The implementation requires the following resources:

Hardware requirements:

 Pentium processor, 1 GB RAM

Software requirements:

   JDK 5.0, Java Swings

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