Optimal Scheduling and Load Balancing in Cloud using Enhanced Genetic Algorithm

Abstract

The cloud computing is the enlargement of distributed computing, equivalent computing and gridiron computing, or defined as the commercial achievement of these computer science concepts. One of the elementary issues in these circumstances is interrelated to task scheduling and Load Balancing. Cloud task arrangement is an NP-hard optimization dilemma, and numerous meta-heuristic algorithms have been anticipated to crack it. A superior task scheduler should acclimatize its arrangement stratagem to the varying situation and the types of tasks. This manuscript proposes a cloud task arrangement course of action based on Load Balancing Enhanced Genetic (EGA) algorithm. The major involvement of our exertion is to balance the whole system load although trying to minimizing the Makespan of a prearranged tasks set. The innovative scheduling strategy was simulated using the Net Beans toolkit package. Experiments results showed the proposed Enhanced Genetic (EGA) algorithm and Compare the EGA, ACO. General Terms Cloud Computing, Genetic algorithm (GA), Ant Colony algorithm (ACO), Load Balancing,