Comparative analysis of metaheuristics based load balancing optimization in cloud environment

Abstract

Cloud Computing Technology provides computing resources as a utility service. The objective is to achieve maximum resource utilization with minimum service delivery time and cost. The main challenge is to balance the virtual machines (VM) load in cloud environment and it requires distributing the load between many virtual machines while avoiding underflow and overflow conditions, which depend on capacity of VMs. In this paper, load balancing of VMs have been done based on Ant Colony Optimization (ACO) and Bat algorithm for underflow and overflow VM identifications respectively. As cloud applications involve huge computations and are highly dynamic in nature, so Directed Acyclic Graph (DAG) files of various scientific workflows have been used as input data during implementation of the proposed methodology. Workflows used for experiments are Cybershake, Genome, Ligo, Montage, Sipht and VMs vary from 2 to 20 on a single host configuration. Initially, the workflows are parsed through Predict earliest Finish time (PEFT) heuristic which initializes the metaheuristics rather than using random initialization. Thus, metaheuristics are providing optimal initial parameters which further optimize the VM utilization by balancing their load. The performance of metaheuristics on the basis of makespan and cost metrics has been evaluate, analyzed and compared with the Particle Swarm Optimization (PSO) approach used for load balancing.