Prediction based Multi Objective Optimization Framework for Energy Efficiency in Small and Medium Scale Data Centers

Abstract

The overall development of the cloud paradigm is a dominating omnipresence in the industry 4.0 business world. Over the last decade, the control measures for power utilization among the proliferative Hyper-Scale Data Centers (HSDCs) have been elucidated. However, the lack of attention to regulating power in Small and Medium-Scale Data Centers (SMSDCs) has ensued in excessive power drainage in SMSDCs. The crucial factor for excessive power utilization of SMSDCs encompasses providing excessive resources and high certainty tasks. Majority of the previously reported studies zeroed-in on problems associated with hyper-scale data centers, excluding probes of the issues prevalent in small and medium-scale cloud data centers. By leveraging the performance of 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 increased power consumption in SMSDCs, which are pivotal factors contributing to high carbon footprints from SMSDCs. The excessive emission of CO2 is more elevated in SMSDCs compared with that of hyper-scale data centers. 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. This research proffers a framework for a predictive optimization approach for delivering data center services to end-users. In the first phase, the Multi Output Random Forest Regressor (MO-RFR) concurrently predicts the multiple resource utilization of Virtual Machines (VMs). The obtained result shows that the proposed approach yields better predictions than other single-output prediction methods for future resource demand from end users. In the second phase, the Multi-Objective Particle Swarm Optimization (MO-PSO) framework utilized the predictive..