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

## Abstract

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 |
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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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