Cloud Job ‎Scheduling with‎ Ions Motion Optimization Algorithm

Abstract

Cloud computing technology success comes from its manner of delivering information ‎technology services, how they are designed, propagated, maintained and scaled. Job Scheduling ‎on cloud computing is a crucial ‎research area and is known to be an NP-complete problem. Scheduling refers to assigning user requests to underlying resources effectively. ‎This paper proposes a new Job Scheduling mechanism for cloud computing ‎environment. The proposed mechanism is based on the Ions Motion Optimization (IMO) algorithm. IMO has two phases, liquid, and crystal. These two phases balance the algorithm behavior ‎between convergence and local optima avoidance. To evaluate the proposed mechanism, a ‎simulation with different scenarios using the CloudSim simulator is conducted. The performance of ‎the proposed algorithm is compared with two metaheuristic algorithms known as Cat Swarm ‎Optimization (CSO) and Glowworm Swarm Optimization (GSO). Furthermore, the proposed IMO ‎mechanism is compared with First Come First Served and random solution. The experimental ‎results demonstrated that the proposed mechanism outperformed both CSO ‎and GSO and produced the shortest execution time in all experimental scenarios.