International Journal of Computer Science and Technology

International Journal of Computer Science and Technology | Vol. 2, No. 12, December 2021 | pp. 89–96

Research Article

Title: A Multi-Criteria Task Scheduling Framework for Heterogeneous Cloud Systems Using Simulated Annealing and Ant Colony Optimization

Names of Authors: Kofi Mensah¹, Kwame Boateng², and Ama Asare³

Authors’ Affiliations:
¹Department of Computer Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
²Department of Computer Engineering, University of Ghana, Accra, Ghana
³School of Technology, GIMPA, Accra, Ghana

Abstract: Efficient task scheduling remains a critical operational priority for infrastructure-as-a-service cloud datacenters seeking to optimize system performance while reducing resource overheads. The assignment of diverse, inter-dependent computational tasks onto non-uniform physical servers represents a complex, multi-objective NP-hard problem. Standard scheduling heuristics usually prioritize single metrics like overall completion time, which frequently leads to uneven server load distribution, high energy waste, and service level agreement violations. This study proposes an optimized hybrid metaheuristic framework, SA-ACO, combining Ant Colony Optimization (ACO) with Simulated Annealing (SA) to resolve complex cloud scheduling demands. The algorithm utilizes an initial ACO loop to establish global routing routes across available nodes, while an integrated SA mutation routine alters local node assignments to avoid getting trapped in local optima. The objective model evaluates multiple performance trade-offs, factoring in task completion time, resource utilization balance, and server energy consumption. Extensive benchmark simulations with 1000 tasks running across 100 virtual machines reveal that the SA-ACO framework lowers overall completion time by 21.4% and decreases system energy consumption by 18.7% compared to traditional genetic algorithms and standard round-robin scheduling setups.

Keywords: Cloud computing, Task scheduling, Ant colony optimization, Simulated annealing, Resource allocation, Energy optimization

Manuscript Timeline: Received: September 15, 2021; Revised: October 24, 2021; Accepted: November 19, 2021; Published: December 1, 2021

Citation: Mensah, K., Boateng, K., & Asare, A. (2021). A multi-criteria task scheduling framework for heterogeneous cloud systems using simulated annealing and ant colony optimization. International Journal of Computer Science and Technology, 2(12), 89–96. DOI: 10.46882/2021/IJCST/000024

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