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• W3005989820 abstract "The cloud service providers require a large number of computing resources to provide services on-demand that consume the electricity at large and leave high carbon footprints which must be minimized. A cloud system must optimally use its resources to achieve a low operational cost without degrading the quality of services. In this context, an ensemble learning based workload forecasting method is presented that uses extreme learning machines and their corresponding forecasts are weighted by a voting engine. A metaheuristic algorithm inspired by blackhole theory is used to select the optimal weights. The accuracy of the approach is tested on CPU and memory demand requests of Google cluster trace. The method is also compared with recent existing work in the literature on CPU utilization of Google cluster and PlanetLab traces. The results validate the superiority of the approach over existing methods with an improvement up to 99.20% in root mean squared error." @default.
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• W3005989820 date "2020-07-01" @default.
• W3005989820 modified "2023-10-12" @default.
• W3005989820 title "Ensemble learning based predictive framework for virtual machine resource request prediction" @default.
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• W3005989820 doi "https://doi.org/10.1016/j.neucom.2020.02.014" @default.
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