Hybrid Metaheuristic Technique for Optimization of Virtual Machine Placement in Cloud

Abstract

The massive growth in cloud users and applications has caused a tremendous increase in the number of virtual machine (VM) requests. Consequently, VM placement has become an important factor in the management of cloud datacenters and their resources. In this context, a new hybrid mechanism named simulated annealing-intelligent water drop cycle algorithm (SA-IWDCA) has been proposed that improves the computational efforts and convergence rate of the water cycle algorithm (WCA) to efficiently place VM in a heterogeneous cloud. WCA performs hybridization in two levels using preselection and memetic algorithms. In level one, an intelligent water drop (IWD) is invoked to generate and feed the initial VM solution to the WCA. At the second level, simulated annealing (SA) is used to improve the VM solution quality using neighborhood search and targeted approaches of temperature scheduling and acceptance probability. The proposed approach incorporates the energy consumption and resource management of datacenters, resulting in a minimum number of active servers and reduced resource dissipation. Extensive simulations and analysis using MATLAB showed that the SA-IWDCA performed significantly well and has shown an improvement of 25% in energy consumption and 20% in resource utilization as compared to other heuristic and metaheuristic approaches.