Optimal node-selection algorithm for parallel download in overlay content-distribution networks

In this paper, we investigate the issue of server selection for parallel download in overlay content-distribution networks. To achieve high performance and resilience to failures, a receiver can make connections with multiple servers simultaneously and receive different portions of the data from the servers in parallel. Prior studies mostly focus on the user-centric performance objectives, such as reducing the round-trip time (RTT) or the completion time of individual download, but tend to ignore the congestion caused by the concurrent connections from different servers or the total network resource usage. The latter performance concerns are important for the service providers who operate content-distribution networks. In this paper, we present a node-selection scheme in a hypercube-like overlay network that generates the optimal server set with respect to the worst-case link stress (WLS) criterion. The algorithm allows scaling to a large system because it is very efficient and does not require network measurement or collection of topology or routing information. It has performance advantages in a number of areas, particularly against the random selection scheme. First, it minimizes the level of congestion at the bottleneck link. This is equivalent to maximizing the achievable throughput. Second, it consumes less network resources in terms of the total number of links used and the total bandwidth usage. Third, it often leads to low average round-trip time to selected servers, hence, allowing nearby nodes to exchange more data, an objective sought by many content-distribution systems.