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Sökning: id:"swepub:oai:DiVA.org:bth-21383" > A Multivariate Char...

LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00004921naa a2200613 4500
001oai:DiVA.org:bth-21383
003SwePub
008210507s2021 | |||||||||||000 ||eng|
0247 a https://urn.kb.se/resolve?urn=urn:nbn:se:bth-213832 URI
0247 a https://doi.org/10.1145/3427921.34502462 DOI
040 a (SwePub)bth
041 a engb eng
042 9 SwePub
072 7a ref2 swepub-contenttype
072 7a kon2 swepub-publicationtype
1001 a Avritzer, Alberto A.u ESulab Solutions, USA4 aut
2451 0a A Multivariate Characterization and Detection of Software Performance Antipatterns
264 c 2021-04-09
264 1a New York, NY, USA :b Association for Computing Machinery, Inc,c 2021
338 a electronic2 rdacarrier
500 a open access
520 a Context. Software Performance Antipatterns (SPAs) research has focused on algorithms for the characterization, detection, and solution of antipatterns. However, existing algorithms are based on the analysis of runtime behavior to detect trends on several monitored variables (e.g., response time, CPU utilization, and number of threads) using pre-defined thresholds. Objective. In this paper, we introduce a new approach for SPA characterization and detection designed to support continuous integration/delivery/deployment (CI/CDD) pipelines, with the goal of addressing the lack of computationally efficient algorithms. Method. Our approach includes SPA statistical characterization using a multivariate analysis approach of load testing experimental results to identify the services that have the largest impact on system scalability. More specifically, we introduce a layered decomposition approach that implements statistical analysis based on response time to characterize load testing experimental results. A distance function is used to match experimental results to SPAs. Results. We have instantiated the introduced methodology by applying it to a large complex telecom system. We were able to automatically identify the top five services that are scalability choke points. In addition, we were able to automatically identify one SPA. We have validated the engineering aspects of our methodology and the expected benefits by means of a domain experts' survey. Conclusion. We contribute to the state-of-The-Art by introducing a novel approach to support computationally efficient SPA characterization and detection in large complex systems using performance testing results. We have compared the computational efficiency of the proposed approach with state-of-The-Art heuristics. We have found that the approach introduced in this paper grows linearly, which is a significant improvement over existing techniques. © 2021 ACM.
650 7a NATURVETENSKAPx Data- och informationsvetenskapx Programvaruteknik0 (SwePub)102052 hsv//swe
650 7a NATURAL SCIENCESx Computer and Information Sciencesx Software Engineering0 (SwePub)102052 hsv//eng
653 a multivariate analysis
653 a software performance antipattern characterization
653 a software performance antipattern detection
653 a Computational efficiency
653 a Response time (computer systems)
653 a Scalability
653 a Computationally efficient
653 a Continuous integrations
653 a Decomposition approach
653 a Engineering aspects
653 a Large complex systems
653 a Multivariate analysis approaches
653 a Software performance
653 a Statistical characterization
653 a Multivariant analysis
7001 a Britto, Ricardo,d 1982-u Blekinge Tekniska Högskola,Institutionen för programvaruteknik4 aut0 (Swepub:bth)rbr
7001 a Trubiani, Catiau Gran Sasso Science Institute, ITA4 aut
7001 a Russo, Barbarau Free University of Bozen-Bolzano, ITA4 aut
7001 a Janes, Andreau Free University of Bozen-Bolzano, ITA4 aut
7001 a Camilli, Matteou Free University of Bozen-Bolzano, ITA4 aut
7001 a Van Hoorn, André V.u University of Stuttgart, DEU4 aut
7001 a Heinrich, Robertu Karlsruhe Institute of Technology, DEU4 aut
7001 a Rapp, Martinau FZI Forschungszentrum Informatik, DEU4 aut
7001 a Henß, Jörgu FZI Forschungszentrum Informatik, DEU4 aut
7102 a ESulab Solutions, USAb Institutionen för programvaruteknik4 org
7730 t ICPE 2021 - Proceedings of the ACM/SPEC International Conference on Performance Engineeringd New York, NY, USA : Association for Computing Machinery, Incg , s. 61-72q <61-72z 9781450381949
8564 u https://bth.diva-portal.org/smash/get/diva2:1553044/FULLTEXT01.pdfx primaryx Raw objecty fulltext:postprint
8564 u http://bth.diva-portal.org/smash/get/diva2:1553044/FULLTEXT01
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:bth-21383
8564 8u https://doi.org/10.1145/3427921.3450246

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