Indian Journal of Science and Technology

Abstract

Backgrounds: The high pace increase in cloud applications requires an optimal computing platform like, Virtual Machine (VM) Consolidationor virtualization to ensure optimal computational efficiency, energy consumption and minimal SLA violation. Methods: In this paper, an evolutionary computing approach called Adaptive Genetic Algorithm (A-GA) has been proposed for VM placement policy, to be used in VM consolidation. In the proposed model, the modified Robust Local Regression (LRR) and Inter-Quartile Range (IQR) schemes estimate the dynamic CPU utilization for overload detection, which is followed by Maximum Correlation (MC) and Minimum Migration Time (MMT) based VM selectionand A-GA based VM placement. Findings: The comparative performance analysis for the proposed system with Planet Lab cloud benchmark dataset has exhibited that the proposed model exhibits better results as compared to other heuristic approaches such as Best Fit Decreasing (BFD) algorithm and Ant Colony Optimization (ACO). The implementation of the proposed A-GA based consolidation with modified IQR and LRR, and MMT selection policyhas performed better in terms of energy efficiency and SLA violation as compared to the other heuristic approaches for placement such as Best Fit Decreasing (BFD) algorithm with conventional IQR, Local Regression (LR), robust local regression, Static Threshold (THR) and Median Absolute Deviation (MAD) based CPU utilization threshold estimation schemes. Furthermore, the proposed A-GA based scheme has outperformed Ant Colony Optimization (ACO) based consolidation scheme. The performance analysis with two distinct VM selection policies, MC and MMT has revealed that A-GA performs better with MMT selection policy and provides higher host shutdown, minimal VM migration and SLA violation, and minimal energy consumption. Applications: The proposed A-GAbased VM consolidation scheme can be significant for energy-aware and QoS oriented virtualization application in large scale cloud infrastructures.
Keywords: Adaptive Genetic Algorithm, Evolutionary Computing, Green Computing, Maximum Correlation, Minimum Migration Time, VM Consolidation.