An Improved Ant Colony Optimization Algorithm for Scheduling in Cloud Computing Environment

Abstract

Cloud computing is a model for delivering, hosting and accessing shared pool of resources and services over the internet in on-demand, self-service, dynamically scalable and metered manner. Scheduling access to cloud resources is a topic of interest to both researchers and IT community. Several approaches have been proposed from the traditional methods to those that are exhaustive in nature. However, Cloud task scheduling is an NP-hard optimization problem, and can break down deterministic or exhaustive approaches with the increase in the number of variables to be optimized. Recently there is has been attempt to use meta-heuristic algorithm for scheduling in cloud computing. These include Genetic Algorithms (GA), Particle Swamp Optimization (PSO), Ant Colony Optimization Algorithm (ACO) and other nature inspired algorithms. The algorithms offer NP-hard problems global solutions acceptable in time frame proportional to the number of variables to be optimized. We use ACO algorithm for scheduling in cloud computing environment. Load balancing is added into the algorithm to prevent the algorithm from falling into local minima. A comparison with (43) shows that the proposed algorithm achieved 51.95% in makespan. However, the compared work is better than this work in the average running time by 86.50%.