Mining Web Server Logs for Creating Workload Models

Fredrik Abbors, Dragos Truscan, Tanwir Ahmad

Research output: Chapter in Book/Conference proceedingChapterScientificpeer-review

3 Citations (Scopus)
2 Downloads (Pure)

Abstract

We present a tool-supported approach where we used data mining techniques for automatically inferring workload models from historical web access log data. The workload models are represented as Probabilistic Timed Automata (PTA) and describe how users interact with the system. Via their stochastic nature, PTAs have more advantages over traditional approaches which simply playback scripted or pre-recorded traces: they are easier to create and maintain and achieve higher coverage of the tested application. The purpose of these models is to mimic real-user behavior as closely as possible when generating load. To show the validity and applicability of our proposed approach, we present a few experiments. The results show, that the workload models automatically derived from web server logs are able to generate similar load with the one applied by real-users on the system and that they can be used as the starting point for performance testing process.
Original languageUndefined/Unknown
Title of host publicationSoftware Technologies - 9th International Joint Conference, ICSOFT 2014, Vienna, Austria, August 29-31, 2014, Revised Selected Papers
EditorsA Holzinger, J Cardoso, J Cordeiro, T Libourel, LA Maciaszek, M Sinderen
PublisherSpringer
Pages131–150
ISBN (Electronic)978-3-319-25579-8
ISBN (Print)978-3-319-25578-1
DOIs
Publication statusPublished - 2015
MoE publication typeA3 Part of a book or another research book

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