A Hybrid Approach of Genetic Algorithm and Multi Objective PSO Task Scheduling in Cloud Computing Kumari K. Raja*, Dr. Sengottuvelan P.**, Dr. Shanthini J.*** *Department of Information Technology, SNS College of Technology, Coimbatore, Tamil Nadu, India **Department of Computer Science, Periyar University, Dharmapuri, Tamil Nadu, India ***Department of Computer Science and Engineering, SNS College of Technology, Coimbatore, Tamil Nadu, India Online published on 23 March, 2017. Abstract The genetic algorithm is an evolutionary optimization algorithm based upon Initial population, crossover, mutation and Evaluation. On the other side, Multi Objective particle swarm optimization (MOPSO) is a swarm intelligence algorithm functioning by means of inertia weight, learning factors and the mutation probability. In high-performance hyper-heuristic algorithm is used to find better scheduling solutions in cloud computing. To improve the scheduling results in terms of makespan, throughput, cost. Hyper-heuristic algorithm finds better scheduling solutions for cloud computing systems and to further improve the scheduling results in terms of make span. A novel Multi objective particle swarm optimization and Genetic Algorithm based hyper-heuristic resource scheduling algorithm has been designed as the hybrid algorithm. Performance of the proposed algorithm has also been evaluated through the Cloud Sim toolkit. We have compared our hybrid scheduling algorithm with existing common heuristic-based scheduling algorithms. The results thus obtained have shown a better performance by our algorithm than the existing algorithms, in terms of giving reduce cost and improve makespan. The proposed model shows the improved resource utilization, makespan, throughput. Top Keywords Cloud computing, Task Scheduling, Resource allocation, MOPSO- Multi objective particle swarm optimization, GA-Genetic algorithm. Top |