Abstract
Performance evaluation of cloud data-centers has drawn considerable attention from academy and industry. In this study, we present an analytical approach to the performance analysis of Infrastructure-as-a-Service cloud data-centers with unreliable task executions and resubmissions of unsuccessful tasks. Several performance metrics are considered and analyzed under variable load intensities, failure frequencies, multiplexing abilities, and service intensities. We also conduct a case study based on a real-world cloud data-center and employ a confidence interval check to validate the correctness of the proposed model. For the performance optimization and optimal capacity planning purposes, we are also interested in knowing the minimized expected response time subject to the constraint of request rejection rate, hardware cost in terms of the cost of physical machines and the request buffer. We show that the optimization problem can be numerically solved through a simulated-annealing-based algorithm.
This work is in part supported by NSFC under Grant Nos. 61472051, 61170183, 61272093, 61572523, and 61202347; Young Scientist Foundation of Chongqing No. cstc2013kjrc-qnrc0079; Fundamental Research Funds for the Central Universities under project Nos. 106112014CDJZR185503 and CDJZR12180012; Science foundation of Chongqing No. cstc2014jcyjA40010; China Postdoctoral Science Foundation No. 2015M570770; Chongqing Postdoctoral Science special Foundation no. Xm2015078.
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Li, W. et al. (2016). On Stochastic Performance and Cost-Aware Optimal Capacity Planning of Unreliable Infrastructure-as-a-Service Cloud. In: Carretero, J., Garcia-Blas, J., Ko, R., Mueller, P., Nakano, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2016. Lecture Notes in Computer Science(), vol 10048. Springer, Cham. https://doi.org/10.1007/978-3-319-49583-5_50
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DOI: https://doi.org/10.1007/978-3-319-49583-5_50
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