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MT-EA4Cloud: A Methodology For testing and optimising energy-aware cloud systems

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Cañizares, Pablo C. and Núñez Covarrubias, Alberto and Lara, Juan de and Llana Díaz, Luis Fernando (2020) MT-EA4Cloud: A Methodology For testing and optimising energy-aware cloud systems. Journal of Systems and Software, 163 . p. 110522. ISSN 01641212

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Official URL: https://doi.org/10.1016/j.jss.2020.110522




Abstract

Currently, using conventional techniques for checking and optimising the energy consumption in cloud systems is unpractical, due to the massive computational resources required. An appropriate test suite focusing on the parts of the cloud to be tested must be efficiently synthesised and executed, while the correctness of the test results must be checked. Additionally, alternative cloud configurations that optimise the energetic consumption of the cloud must be generated and analysed accordingly, which is challenging.

To solve these issues we present MT-EA4Cloud, a formal approach to check the correctness – from an energy-aware point of view – of cloud systems and optimise their energy consumption. To make the checking of energy consumption practical, MT-EA4Cloud combines metamorphic testing, evolutionary algorithms and simulation. Metamorphic testing allows to formally model the underlying cloud infrastructure in the form of metamorphic relations. We use metamorphic testing to alleviate both the reliable test set problem, generating appropriate test suites focused on the features reflected in the metamorphic relations, and the oracle problem, using the metamorphic relations to check the generated results automatically. MT-EA4Cloud uses evolutionary algorithms to efficiently guide the search for optimising the energetic consumption of cloud systems, which can be calculated using different cloud simulators.


Item Type:Article
Uncontrolled Keywords:Cloud modelling; Metamorphic testing; Simulation; Evolutionary algorithms; Energy-aware systems
Subjects:Sciences > Computer science > Artificial intelligence
Sciences > Mathematics > Cybernetics
Sciences > Mathematics > Operations research
ID Code:63712
Deposited On:21 Jan 2021 19:20
Last Modified:25 Jan 2021 08:34

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