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ALPINE: A Bayesian System For Cloud Performance Diagnosis And Prediction
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.ORCID-id: 0000-0003-3489-7429
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.ORCID-id: 0000-0001-8561-7963
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.ORCID-id: 0000-0002-8681-9572
Newcastle University.
2017 (engelsk)Inngår i: 2017 IEEE International Conference on Services Computing (SCC), Piscataway, NJ: IEEE, 2017, s. 281-288, artikkel-id 8034996Konferansepaper, Publicerat paper (Fagfellevurdert)
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%.

sted, utgiver, år, opplag, sider
Piscataway, NJ: IEEE, 2017. s. 281-288, artikkel-id 8034996
Emneord [en]
Bayesian Network, Cloud Computing, Diagnosis, Quality of Service, Prediction
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Identifikatorer
URN: urn:nbn:se:ltu:diva-64458DOI: 10.1109/SCC.2017.43ISI: 000425931600036Scopus ID: 2-s2.0-85032332116ISBN: 978-1-5386-2005-2 (digital)OAI: oai:DiVA.org:ltu-64458DiVA, id: diva2:1114675
Konferanse
14th IEEE International Conference on Services Computing (SCC), Honolulu, HI, USA, 25-30 June 2017
Tilgjengelig fra: 2017-06-25 Laget: 2017-06-25 Sist oppdatert: 2018-06-28bibliografisk kontrollert

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