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Evaluating cluster-based network servers

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2 Author(s)
Carrera, E.V. ; Dept. of Comput. Sci., Rutgers Univ., Piscataway, NJ, USA ; Bianchini, R.

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

Published in:

High-Performance Distributed Computing, 2000. Proceedings. The Ninth International Symposium on

Date of Conference:

2000