Enhanced Ant Colony Optimization for Cloud Scheduling with Local Search and Elitist

Abstract

Cloud computing allows the execution of various types of jobs with diverse resource requirements, so an efficient scheduling mechanism is needed to minimize processing time and improve resource utilization. The Ant Colony Optimization (ACO) algorithm is one of the metaheuristic methods widely used for job scheduling optimization. However, the ACO algorithm still has limitations in slow convergence and a tendency to get stuck in local solutions. This study proposes Enhanced Ant Colony Optimization with the Swap-Based Local Search and Elitist Strategy approaches to improve the quality of scheduling decisions. Evaluations were conducted in a heterogeneous emulated cloud environment with a focus on scheduling performance, convergence behaviour, and resource utilization metrics. The experimental results show that the proposed method outperforms FCFS, ACO, and ACO with Local Search for both normal and heavy scenarios. The study also demonstrates that the integration of local search and elitist strategy effectively improves the convergence process toward the global optimal solution while promoting a fairer utilization of resources. This paper may serve as a basis for further development on larger job scales and the addition of other optimization techniques.