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Modelling exogenous variability in cloud deployments

Casale, Giuliano and Tribastone, Mirco Modelling exogenous variability in cloud deployments. Performance Evaluation Review, 40 (4). pp. 73-82. ISSN 0163-5999 (2013)

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Describing exogenous variability in the resources used by a cloud application leads to stochastic performance models that are difficult to solve. In this paper, we describe the blending algorithm, a novel approximation for queueing network models immersed in a random environment. Random environments are Markov chain-based descriptions of timevarying operational conditions that evolve independently of the system state, therefore they are natural descriptors for exogenous variability in a cloud deployment. The algorithm adopts the principle of solving a separate transient-analysis subproblem for each state of the random environment. Each subproblem is then approximated by a system of ordinary differential equations formulated according to a fluid limit theorem, making the approach scalable and computationally inexpensive. A validation study on several hundred models shows that blending can save up to two orders of magnitude of computational time compared to simulation, enabling efficient exploration of a decision space, which is useful in particular at design-time.

Item Type: Article
Identification Number: 10.1145/2479942.2479951
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Research Area: Computer Science and Applications
Depositing User: Ms T. Iannizzi
Date Deposited: 11 Feb 2015 14:24
Last Modified: 11 Feb 2015 14:24
URI: http://eprints.imtlucca.it/id/eprint/2604

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