Cloud Job ‎Scheduling with‎ Ions Motion Optimization Algorithm

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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.

Keywords:

optimization, ions motion, cloud, job scheduling

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[1]
Hassan, M.E. and Yousif, A. 2020. Cloud Job ‎Scheduling with‎ Ions Motion Optimization Algorithm. Engineering, Technology & Applied Science Research. 10, 2 (Apr. 2020), 5459–5465. DOI:https://doi.org/10.48084/etasr.3408.

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