Cloud Task Scheduling Based on Ant Colony Optimization

Cloud computing is the development of distributed computing, parallel computing and grid computing, or defined as the commercial implementation of these computer science concepts. One of the fundamental issues in this environment is related to task scheduling. Cloud task scheduling is an NP-hard optimization problem and many meta-heuristic algorithms have been proposed to solve it. A good task scheduler should adapt its scheduling strategy to the changing environment and the types of tasks. In this paper, a cloud task scheduling policy based on Ant Colony Optimization (ACO) algorithm compared with different scheduling algorithms First Come First Served (FCFS) and Round-Robin (RR), has been presented. The main goal of these algorithms is minimizing the makespan of a given tasks set. ACO is random optimization search approach that will be used for allocating the incoming jobs to the virtual machines. Algorithms have been simulated using cloudsim toolkit package. Experimental results showed that cloud task scheduling based on ACO outperformed FCFS and RR algorithms.