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ALPINE: A Bayesian System For Cloud Performance Diagnosis And Prediction
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-3489-7429
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
Newcastle University.
2017 (English)In: 2017 IEEE International Conference on Services Computing (SCC), Piscataway, NJ: IEEE, 2017, 281-288 p., 8034996Conference paper, Published paper (Refereed)
Abstract [en]

Cloud performance diagnosis and prediction is a challenging problem due to the stochastic nature of the cloud systems. Cloud performance is affected by a large set of factors such as virtual machine types, regions, workloads, wide area network delay and bandwidth. Therefore, necessitating the determination of complex relationships between these factors. The current research in this area does not address the challenge of modeling the uncertain and complex relationships between these factors. Further, the challenge of cloud performance prediction under uncertainty has not garnered sufficient attention. This paper proposes, develops and validates ALPINE, a Bayesian system for cloud performance diagnosis and prediction. ALPINE incorporates Bayesian networks to model uncertain and complex relationships between several factors mentioned above. It handles missing, scarce and sparse data to diagnose and predict stochastic cloud performance efficiently. We validate our proposed system using extensive real data and show that it predicts cloud performance with high accuracy of 91.93%.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2017. 281-288 p., 8034996
Keyword [en]
Bayesian Network, Cloud Computing, Diagnosis, Quality of Service, Prediction
National Category
Computer and Information Science Computer Science Other Computer and Information Science Computer Systems
Research subject
Mobile and Pervasive Computing
Identifiers
URN: urn:nbn:se:ltu:diva-64458DOI: 10.1109/SCC.2017.43Scopus ID: 2-s2.0-85032332116ISBN: 978-1-5386-2005-2 (electronic)OAI: oai:DiVA.org:ltu-64458DiVA: diva2:1114675
Conference
14th IEEE International Conference on Services Computing (SCC), Honolulu, HI, USA, 25-30 June 2017
Available from: 2017-06-25 Created: 2017-06-25 Last updated: 2017-11-24Bibliographically approved

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Mitra, KaranSaguna, SagunaÅhlund, Christer

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