Advancement Through Research
Cloud Computing is emerging as an influential architecture to perform complex and large scale computing. It provides on-demand access to services on the “Pay-as-you-go” method. In cloud computing environment Task Scheduling is an essential technique that is required for allocating tasks to appropriate resources for proper resource utilization and optimizing overall system performance. Task scheduling is an NP-hard problem. Many researchers have proposed various algorithms like ACO, PSO, GA, and Bat etc to get optimal results. In this paper, the enhanced Ant Colony Optimization (EACO) algorithm has been proposed that serves improved task scheduling with minimum makespan while maintaining cost. This algorithm mainly contributes in minimizing total completion time for scheduling tasks on resources. This is attained by splitting the ordered submitted tasks into bunches -the sub list of tasks. The main goal of EACO is to minimize total execution time. The proposed algorithm EACO is simulated using the CloudSim toolkit and compared with the existing nature-inspired algorithm. The experimental results show that the presented algorithm improves results in terms of makespan.
cloud computing, task scheduling, ant colony optimization (ACO), makespan
