Load Balancing Based Task Scheduling with ACO in Cloud Computing

Scheduling in Cloud computing infrastructure contain several challenging issues like computation time, budget, load balancing etc. Out of them, load balancing is one the major challenges for Cloud platform. Load balancing basically balances the load to achieve higher throughput and better resource utilization. Since scheduling task is NP-complete problem, so heuristic and meta heuristic approaches are preferred options to solve the same. In this paper, we chose a meta-heuristic approach of Ant colony optimization algorithm to solve the task scheduling problem in cloud environment focussing mainly on two objectives, i.e., minimizing the makespan/ computation time and better load balancing. Comparative analysis shows that proposed load balancing ant colony optimization algorithm (LB-ACO) provides better results than NSGA-II algorithm by providing better load balancing and less makespan. Simulations have been carried out using CloudSim Toolkit.