Active & Idle Virtual Machine Migration Algorithm- a new Ant Colony Optimization approach to consolidate Virtual Machines and ensure Green Cloud Computing

Abstract

Energy efficiency in cloud data-centers is an incredibly significant issue in recent cloud computing research. High consumption of power and improper utilization of physical resources are the main drawbacks in cloud architecture. The idle virtual machines tend to consume 50%-70% of the total server energy which ultimately leads to an imbalance and lack of enough power for the actively working machines. In this paper, a new evolutionary computational approach of the Ant Colony System (ACS) algorithm has been applied to address such problem. Inspired by the promising performance of Ant Colony Optimization (ACO) algorithm, one similar but more efficient algorithm has been developed that not only deals with the problem of high consumption of energy but also addresses the Virtual Machine Placement (VMP) problem. This new concept has been named the Active & Idle Virtual Machine Migration (AIVMM) algorithm. It effectively migrates the idle virtual machines from an actively working server and places them in an inactive server with the objective of reducing power interruption for the active machines. The results depict that the AIVMM when implemented with OEMACS results in a hybrid algorithm which outperforms the conventional methods and offers more significant savings of data center energy and resources.