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• W3048553842 abstract "By leveraging the performance of small and medium-scale data centers (SMSDCs), which are involved in high-performance computing, data centers are central to the current modern industrial business world. Extensive enhancements in the SMSDC infrastructure comprise a diverse set of connected devices that disseminate resources to the end users. The high certainty workloads of end users and over resource provisioning result in high power consumption in SMSDCs, which are pivotal factors contributing to high carbon footprints from SMSDCs. The excessive emission of CO2 is higher in SMSDCs compared with that of hyperscale data centers (HSDCs). An exorbitant amount of electricity is utilized by 8.6 million data centers worldwide, and is expected to increase by up to 13% in 2030. The power requirement of an SMSDC domain is expected to be 5% of the global power production. However, the power consumption of SMSDCs changes annually. To aid SMSDCs, machine learning prediction is deployed. Literature review indicates that many studies have focused on the recurring issues of HSDCs rather than those of SMSDC. Herein, a regressive predictive analysis, i.e., multi-output random forest regressor, is proposed to forecast the resource usage and power utilization of virtual machines. These prediction results in diminishes the power utilization of SMSDC whilst reduces the CO2 emission from SMSDC. The obtained result shows that the proposed approach yields better predictions than other single-output prediction methods for future resource demand from end users." @default.
• W3048553842 created "2020-08-18" @default.
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• W3048553842 date "2020-10-07" @default.
• W3048553842 modified "2023-09-27" @default.
• W3048553842 title "Efficient resource prediction model for small and medium scale cloud data centers" @default.
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• W3048553842 doi "https://doi.org/10.3233/jifs-200653" @default.
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