Abstract
The problem of optimal distribution of virtual resources in multi-cloud systems for big data processing is relevant to this day. End users want to access virtual resources as soon as possible using one of the existing content distribution models: virtual desktop as a service, specific software as a service or even infrastructure as a service. The cloud system scheduler can handle incoming requests from users in physical constraints, taking into account the reduction of energy consumption and limited resources of cloud system. Violation of the Service Level Agreement in terms of exceeding the response time can lead to penalty for the service owner. This paper formalized the optimization problem of close to optimal energy efficient scheduling and provided general architecture of cloud platform under consideration. On the basis of the evolutionary annealing simulation algorithm, a cloud system simulator is implemented. Conducted experimental studies have shown the effectiveness of the proposed method in comparison with the greedy algorithm.
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Acknowledgment
The reported study was funded by RFBR (№ 20–07-01065), and by a grant of the President of the Russian Federation (NSh-2502.2020.9).
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Legashev, L.V., Zabrodina, L.S., Parfenov, D.I., Bolodurina, I.P. (2022). Development of an Algorithm for Energy Efficient Resource Scheduling of a Multi-cloud Platform for Big Data Processing. In: Hu, Z., Petoukhov, S., He, M. (eds) Advances in Artificial Systems for Medicine and Education V. AIMEE 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 107 . Springer, Cham. https://doi.org/10.1007/978-3-030-92537-6_10
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DOI: https://doi.org/10.1007/978-3-030-92537-6_10
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