Lookup NU author(s): Professor Raj Ranjan
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© 2017 IEEE. 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%.
Author(s): Mitra K, Saguna S, Ahlund C, Ranjan R
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: IEEE 14th International Conference on Services Computing, SCC 2017
Year of Conference: 2017
Online publication date: 14/09/2017
Acceptance date: 02/04/2016
Publisher: Institute of Electrical and Electronics Engineers Inc.
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