Cloud Job Scheduling with Ions Motion Optimization Algorithm
Mohammed-Albarra Hassan, Adil Yousif · Engineering Technology & Applied Science Research · 2020
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.