Percentile Performance Estimation of Unreliable IaaS Clouds and Their Cost-Optimal Capacity Decision
Through Internet, a cloud computing system provides shared resources, data, and information
to users or tenant users in an on-demand and pay-as-you-go styles. It delivers large-scale utility computing
services to a wide range of consumers. To ensure that their provisioned service is acceptable, cloud providers
must exploit techniques and mechanisms that meet the service-level-agreement (SLA) performance commitment
to their clients. Thus, performance issues of cloud infrastructures have been receiving considerable
attention by both researchers and practitioners as a prominent activity for improving service quality. This
paper presents an analytical approach to percentile-based performance analysis of unreliable infrastructureas-
a-service clouds. The proposed analytical model is capable of calculating percentiles of the request
response time under variable load intensities, fault frequencies, multiplexing abilities, and instantiation
processing time. A case study based on a real-world cloud is carried out to prove the correctness of the
proposed theoretical model. To achieve optimal performance-cost tradeoff, we formulate the performance
model into an optimal capacity decision problem for cost minimization subjected to the constraints of request
rejection and SLA violation rates.We show that the optimization problem can be numerically solved through
a simulated-annealing method.
KeyWords
Infrastructure-as-a-service clouds, percentile-based performance, service-level-agreement,
heavy-tailed distribution, optimal capacity decision.
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