Design and analysis of parallel file downloading algorithms in peer-to-peer networks

It is well known that the method of parallel downloading can be used to reduce file download times in a peer-to-peer (P2P) network. There has been little investigation on parallel download and chunk allocation for source peers with random service capacities. The main contribution of this paper is to address the problem of efficient parallel file download in P2P networks with random service capacities. A precise analysis of the expected download time is given when the service capacity of a source peer is a random variable. A general framework is developed for analyzing the expected download time of a parallel download and chunk allocation algorithm, and is applied to the analysis of several algorithms. Two chunk allocation algorithms for parallel download are proposed. It is observed that the performance of parallel download can be significantly improved by using the method of probing high-capacity peers. One such algorithm is proposed and its expected parallel download time is analyzed. The performance of these parallel file download algorithms in P2P networks with random service capacities are compared. The above parallel download algorithms are extended to multiple file download by dividing source peers into clusters. It is noticed that there is an important issue of optimal parallelism which minimizes the combined effect of intracluster and intercluster overhead of parallel download and load imbalance.