An Automated Approach for CreatingWorkload Models From Server Log Data

Fredrik Abbors; Dragos Truscan; Tanwir Ahmad

doi:10.5220/0005002200140025

An Automated Approach for CreatingWorkload Models From Server Log Data

Fredrik Abbors, Dragos Truscan, Tanwir Ahmad

Research output: Chapter in Book/Conference proceeding › Conference contribution › Scientific › peer-review

3 Citations (Scopus)

43 Downloads (Pure)

Abstract

We present a tool-supported approach for creating workload models from historical web access log data. The resulting workload models are stochastic, represented as Probabilistic Timed Automata (PTA), and describe how users interact with the system. Such models allow one to analyze different user profiles and to mimic real user behavior as closely as possible when generating workload. We provide an experiment to validate the approach. We present a tool-supported approach for creating workload models from historical web access log data. The resulting workload models are stochastic, represented as Probabilistic Timed Automata (PTA), and describe how users interact with the system. Such models allow one to analyze different user profiles and to mimic real user behavior as closely as possible when generating workload. We provide an experiment to validate the approach.

Original language	Undefined/Unknown
Title of host publication	Proceedings of the 9th International Conference on Software Engineering and Application
Editors	Andreas Holzinger, Therese Libourel, Leszek Maciaszek, Stephen Mellor
Publisher	SCITEPRESS Science And Technology Publications
Pages	14–25
ISBN (Print)	978-989-758-036-9
DOIs	https://doi.org/10.5220/0005002200140025
Publication status	Published - 2014
MoE publication type	A4 Article in a conference publication
Event	conference; 2014-08-29; 2014-08-31 - Vienna Duration: 29 Aug 2014 → 31 Aug 2014

Conference

Conference	conference; 2014-08-29; 2014-08-31
Period	29/08/14 → 31/08/14

Keywords

Log file analysis
Performance testing
Probabilistic Timed Automata
Workload model generation,

Access to Document

10.5220/0005002200140025

paper.pdf

http://www.scitepress.org/

N4S: Need for Speed
Porres Paltor, I. (Principal Investigator), Nybom, K. (Co-Investigator), Truscan, D. (Co-Investigator), Ashraf, A. (Co-Investigator), Troubitsyna, E. (Co-Investigator), Byholm, B. (Co-Investigator), Rauf, I. M. (Co-Investigator) & Ahmad, T. (Co-Investigator)
01/01/14 → 31/12/16
Project: Industry/Business Finland
PAM: Practical Applications of Model-based technologies to continuous integration & testing methodologies
Truscan, D. (Principal Investigator), Ahmad, T. (Co-Investigator), Iqbal, J. (Principal Investigator), Porres Paltor, I. (Co-Principal Investigator) & Siavashi, F. (Co-Investigator)
01/01/12 → 31/12/15
Project: Industry/Business Finland

Cite this

Abbors, F., Truscan, D., & Ahmad, T. (2014). An Automated Approach for CreatingWorkload Models From Server Log Data. In A. Holzinger, T. Libourel, L. Maciaszek, & S. Mellor (Eds.), Proceedings of the 9th International Conference on Software Engineering and Application (pp. 14–25). SCITEPRESS Science And Technology Publications. https://doi.org/10.5220/0005002200140025

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abstract = "We present a tool-supported approach for creating workload models from historical web access log data. The resulting workload models are stochastic, represented as Probabilistic Timed Automata (PTA), and describe how users interact with the system. Such models allow one to analyze different user profiles and to mimic real user behavior as closely as possible when generating workload. We provide an experiment to validate the approach. We present a tool-supported approach for creating workload models from historical web access log data. The resulting workload models are stochastic, represented as Probabilistic Timed Automata (PTA), and describe how users interact with the system. Such models allow one to analyze different user profiles and to mimic real user behavior as closely as possible when generating workload. We provide an experiment to validate the approach.",

keywords = "Log file analysis, Performance testing, Probabilistic Timed Automata, Workload model generation,, Log file analysis, Performance testing, Probabilistic Timed Automata, Workload model generation,, Log file analysis, Performance testing, Probabilistic Timed Automata, Workload model generation,",

author = "Fredrik Abbors and Dragos Truscan and Tanwir Ahmad",

year = "2014",

doi = "10.5220/0005002200140025",

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editor = "Andreas Holzinger and Therese Libourel and Leszek Maciaszek and Stephen Mellor",

booktitle = "Proceedings of the 9th International Conference on Software Engineering and Application",

publisher = "SCITEPRESS Science And Technology Publications",

note = "conference; 2014-08-29; 2014-08-31 ; Conference date: 29-08-2014 Through 31-08-2014",

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Abbors, F, Truscan, D & Ahmad, T 2014, An Automated Approach for CreatingWorkload Models From Server Log Data. in A Holzinger, T Libourel, L Maciaszek & S Mellor (eds), Proceedings of the 9th International Conference on Software Engineering and Application. SCITEPRESS Science And Technology Publications, pp. 14–25, conference; 2014-08-29; 2014-08-31, 29/08/14. https://doi.org/10.5220/0005002200140025

An Automated Approach for CreatingWorkload Models From Server Log Data. / Abbors, Fredrik; Truscan, Dragos ; Ahmad, Tanwir.
Proceedings of the 9th International Conference on Software Engineering and Application. ed. / Andreas Holzinger; Therese Libourel; Leszek Maciaszek; Stephen Mellor. SCITEPRESS Science And Technology Publications, 2014. p. 14–25.

Research output: Chapter in Book/Conference proceeding › Conference contribution › Scientific › peer-review

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T1 - An Automated Approach for CreatingWorkload Models From Server Log Data

AU - Abbors, Fredrik

AU - Truscan, Dragos

AU - Ahmad, Tanwir

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N2 - We present a tool-supported approach for creating workload models from historical web access log data. The resulting workload models are stochastic, represented as Probabilistic Timed Automata (PTA), and describe how users interact with the system. Such models allow one to analyze different user profiles and to mimic real user behavior as closely as possible when generating workload. We provide an experiment to validate the approach. We present a tool-supported approach for creating workload models from historical web access log data. The resulting workload models are stochastic, represented as Probabilistic Timed Automata (PTA), and describe how users interact with the system. Such models allow one to analyze different user profiles and to mimic real user behavior as closely as possible when generating workload. We provide an experiment to validate the approach.

AB - We present a tool-supported approach for creating workload models from historical web access log data. The resulting workload models are stochastic, represented as Probabilistic Timed Automata (PTA), and describe how users interact with the system. Such models allow one to analyze different user profiles and to mimic real user behavior as closely as possible when generating workload. We provide an experiment to validate the approach. We present a tool-supported approach for creating workload models from historical web access log data. The resulting workload models are stochastic, represented as Probabilistic Timed Automata (PTA), and describe how users interact with the system. Such models allow one to analyze different user profiles and to mimic real user behavior as closely as possible when generating workload. We provide an experiment to validate the approach.

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KW - Performance testing

KW - Probabilistic Timed Automata

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KW - Performance testing

KW - Probabilistic Timed Automata

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KW - Performance testing

KW - Probabilistic Timed Automata

KW - Workload model generation,

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T2 - conference; 2014-08-29; 2014-08-31

Y2 - 29 August 2014 through 31 August 2014

ER -

An Automated Approach for CreatingWorkload Models From Server Log Data

Abstract

Conference

Keywords

Access to Document

Projects

N4S: Need for Speed

PAM: Practical Applications of Model-based technologies to continuous integration & testing methodologies

Cite this