Improving the simulation of IaaS Clouds

Abstract : The ability to provision resources on the fly and their pay-as-you-go nature has made cloud computing platforms a staple of modern computer infrastructure. Such platforms allow for new scheduling strategies for the execution of computing workloads. Finding a strategy that satisfies a user’s cost and time constraints is a difficult problem that requires a prediction tool. However the inherent variability of these platforms makes building such a tool a complex endeavor. Our thesis is that, by producing probability distributions of possible outcomes, stochastic simulation can be used to produce predictions that account for the variability. To demonstrate this we used Monte Carlo methods to produce a stochastic simulation by repeatedly running deterministic simulations. We show that this method used in conjunction with specific input models can model the variability of a platform using a single parameter. To validate our method we compare our results to real executions of scientific workloads. Our experiments show that our method produces predictions capable of representing theobserved real executions.
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Submitted on : Tuesday, October 15, 2019 - 5:27:31 PM
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Luke Bertot. Improving the simulation of IaaS Clouds. Data Structures and Algorithms [cs.DS]. Université de Strasbourg, 2019. English. ⟨NNT : 2019STRAD008⟩. ⟨tel-02161866v2⟩

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