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Content Aware Request Distribution for High Performance Web Service: A Performance Study

The World Wide Web is becoming a basic infrastructure for a variety of services, and the increases in audience size and client network bandwidth create service demands that are outpacing server capacity. Web clusters are one solution to this need for highperformance, highly available web server systems. We are interested in load distribution techniques, specifically Layer-7 algorithms that are content-aware. Layer- 7 algorithms allow distribution control based on the specific content requested, which is advantageous for a system that offers highly heterogenous services. We examine the performance of the Client Aware Policy (CAP) on a Linux/Apache web cluster consisting of a single web switch that directs requests to a pool of dual-processor SMP nodes. We show that the performance advantage of CAP over simple algorithms such as random and round-robin is as high as 29% on our testbed consisting of a mixture of static and dynamic content. Under heavily loaded conditions however, the performance decreases to the level of random distribution. In studying SMP vs. uniprocessor performance using the same number of processors with CAP distribution, we find that SMP dual-processor nodes under moderate workload levels provide equivalent throughput as the same number of CPU’s in a uniprocessor cluster. As workload increases to a heavily loaded state however, the SMP cluster shows reduced throughput compared to a cluster using uniprocessor nodes. We show that the web cluster’s maximum throughput increases linearly with the addition of more nodes to the server pool. We conclude that CAP is advantageous over random or round-robin distribution under certain conditions for highly dynamic workloads, and suggest some future enhancements that may improve its performance.

Identiferoai:union.ndltd.org:pdx.edu/oai:pdxscholar.library.pdx.edu:open_access_etds-3668
Date01 July 2002
CreatorsJones, Robert M.
PublisherPDXScholar
Source SetsPortland State University
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceDissertations and Theses

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