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dc.contributor.authorBarford, Paulen_US
dc.contributor.authorCrovella, Marken_US
dc.date.accessioned2011-10-20T04:37:44Z
dc.date.available2011-10-20T04:37:44Z
dc.date.issued1997-12-31
dc.identifier.citationBarford, Paul; Crovella, Mark. "Generating Representative Web Workloads for Network and Server Performance Evaluation", Technical Report BUCS-1997-006, Computer Science Department, Boston University, May 5, 1997. [Available from: http://hdl.handle.net/2144/1607]
dc.identifier.urihttps://hdl.handle.net/2144/1607
dc.description.abstractOne role for workload generation is as a means for understanding how servers and networks respond to variation in load. This enables management and capacity planning based on current and projected usage. This paper applies a number of observations of Web server usage to create a realistic Web workload generation tool which mimics a set of real users accessing a server. The tool, called Surge (Scalable URL Reference Generator) generates references matching empirical measurements of 1) server file size distribution; 2) request size distribution; 3) relative file popularity; 4) embedded file references; 5) temporal locality of reference; and 6) idle periods of individual users. This paper reviews the essential elements required in the generation of a representative Web workload. It also addresses the technical challenges to satisfying this large set of simultaneous constraints on the properties of the reference stream, the solutions we adopted, and their associated accuracy. Finally, we present evidence that Surge exercises servers in a manner significantly different from other Web server benchmarks.en_US
dc.description.sponsorshipNational Science Foundation (CCR-9501822, CCR-9706685); Hewlett-Packard Laboratoriesen_US
dc.language.isoen_US
dc.publisherBoston University Computer Science Departmenten_US
dc.relation.ispartofseriesBUCS Technical Reports;BUCS-TR-1997-006
dc.titleGenerating Representative Web Workloads for Network and Server Performance Evaluationen_US
dc.typeTechnical Reporten_US


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