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Generating representative Web workloads for network and server performance evaluation
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Source Joint International Conference on Measurement and Modeling of Computer Systems archive
Proceedings of the 1998 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems table of contents
Madison, Wisconsin, United States
Pages: 151 - 160  
Year of Publication: 1998
ISBN:0-89791-982-3
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Authors
Paul Barford  Computer Science Department, Boston University, 111 Cummington St, Boston, MA
Mark Crovella  Computer Science Department, Boston University, 111 Cummington St, Boston, MA
Sponsors
SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
IFIP WG 7.3 : IFIP WG 7.3
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 33,   Downloads (12 Months): 208,   Citation Count: 269
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ABSTRACT

One 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.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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CITED BY  269

Collaborative Colleagues:
Paul Barford: colleagues
Mark Crovella: colleagues