Performance evaluation of hybrid GAACO for task scheduling in cloud computing

Abstract

Cloud computing is really a new computing mode. Load balancing of resources across virtual machines is the fundamental problem of Cloud Computing. Effective job scheduling device must meet people 'requirements and increase the source usage, to be able to increase the entire efficiency of the cloud processing environment. In optimization issue. Genetic Algorithm and Ant Colony Optimization Algorithm have already been referred to as excellent option method. GA is created by adopting the organic progress process, while ACO is encouraged by the foraging behavior of ant species. This paper evaluated hybridization of ACO and GA adopt with multi-objective function to improve the global optimization solution.