Optimization of VM migration and Energy Consumption using Adaptative Particle Swarm Optimization Algorithm

Abstract

The high energy consumption of cloud computing systems affects both cloud providers and users. Virtualization is needed to save energy. VM consolidation efficiently manages cloud resources for users and cloud providers. It also improves server efficiency and reduces data centers energy use. However, needless VM consolidation efforts lead to poor VM selection and assignment, lowering performance, QoS, and SLAs. Data centers need energy-saving solutions without impacting other metrics. This paper introduces a adaptive Particle Swarm Optimization methodology for energy-efficient Virtual Machine (VM) migration within cloud environments. The technique optimizes energy usage and ensures SLA compliance by optimizing VM-to-Physical Machine (PM) allocations. The evaluation of the proposed method has been done considering the metrics like energy consumption, SLA and resource usage. The results highlight that after incorporating the optimization an enhancement has been observed for all the metrics for the effective VM management.