Efficient resource prediction model for small and medium scale cloud data centers

Multi-objective prediction-based optimization of power consumption for cloud data centers

 The overall development of the cloud paradigm is 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 small and medium-scale cloud data centers. The crucial factor for excessive power utilization of SMSDCs encompasses providing excessive resources, 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. This paper proffers a framework for a predictive optimization approach for delivering the data center services to end-users. In the first phase, the Multi-Output (MO) Random Forest Regressor (RFR) (MO-RFR) concurrently predicts the multiple-resource utilization of Virtual Machines (VMs). The predictive framework outcome was utilized by the Multi-Objective Particle Swarm Optimization (MO-PSO) framework in the second phase to resolve the issue in virtual machine placement and to accomplish better physical machine consolidation. The proposed multi-prediction-based MO-PSO to escalate the resource usage, minimizes the power utilization, and curtail the carbon footprint. The efficacy of the proposed approach was appraised via performance metrics and actual workload traces. The acquired result from the proposed method outperforms the baseline approaches.