Evaluating cluster-based network servers

Abstract

Uses analytic modeling and simulation to evaluate network servers implemented on clusters of workstations. More specifically, we model the potential benefits of locality-conscious request distribution within the cluster and evaluate the performance of a cluster-based server called L2S (Locality and Load-balancing Server) which we designed in light of our experience with the model. Our most important modeling results show that locality-conscious distribution on a 16-node cluster can increase server throughput with respect to a locality-oblivious server by up to seven-fold, depending on the average size of the files requested and on the size of the server's working set. Our simulation results demonstrate that L2S achieves throughput that is within 22% of the full potential of locality-conscious distribution on 16 nodes, outperforming and significantly outscaling the best-known locality-conscious server. Based on our results and on the fact that the files serviced by network servers are becoming larger and more numerous, we conclude that our locality-conscious network server should prove very useful for its performance, scalability and availability.