MT-EA4Cloud: A Methodology For testing and optimising energy-aware cloud systems
EntityUAM. Departamento de Ingeniería Informática
10.1016/j.jss.2020.110522Journal of Systems and Software 163 (2020): 110522
ISSN0164-1212 (print); 1873-1228 (online)
Funded byThis work was supported by the Spanish MINECO/FEDER projects DArDOS, FAME and MASSIVE under Grants TIN2015-65845-C3-1-R, RTI2018-093608-B-C31 and RTI2018-095255- B-I00, and the Comunidad de Madrid project FORTE-CM under grant S2018/TCS-4314. The first author is also supported by the Universidad Complutense de Madrid Santander Universidades grant (CT17/17-CT18/17)
ProjectGobierno de España. TIN2015-65845-C3-1-R; Gobierno de España. RTI2018-093608-B-C31; Gobierno de España. RTI2018-095255-B-I00; Comunidad de Madrid. S2018/TCS-4314
SubjectsCloud modelling; Energy-aware systems; Evolutionary algorithms; Metamorphic testing; Simulation; Informática
Esta obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0 Internacional.
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
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