Title: Modified tabu-based ant colony optimisation algorithm for energy-efficient cloud computing systems

Authors: Jyoti Chauhan; Taj Alam

Addresses: Department of Computer Science & Engineering and Information Technology, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India ' Department of Computer Science & Engineering and Information Technology, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India

Abstract: The widespread adoption of Cloud Computing (CC) and rapid rise in capacity and scale of data centres to host and provide services via internet results in a significant increase in electricity usage and increased carbon footprints. One of the challenging research problems is to motivate green CC by reducing datacentres energy consumption. The well-known Task Scheduling (TS) considered as NP-hard problem needs to be addressed to facilitate green CC which influences the overall efficiency of cloud system. This paper presented a modified novel Tabu-based Ant Colony Optimisation Algorithm (TACO) energy efficient TS approach for heterogeneous cloud environment. TACO maintains a Long-Term Memory (LTM) in terms of aspiration list and Short-Term Memory (STM) in terms of tabu list. TACO is evaluated in CloudSim 3.0.3 to measure its performance with existing energy-based metaheuristics Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA) which shows its outperformance in energy consumption, makespan and resource utilisation.

Keywords: ant colony optimisation; cloud computing; scheduling; particle swarm optimisation; genetic algorithm; energy efficiency.

DOI: 10.1504/IJGUC.2024.137903

International Journal of Grid and Utility Computing, 2024 Vol.15 No.2, pp.160 - 180

Received: 05 Sep 2022
Received in revised form: 30 May 2023
Accepted: 13 Jul 2023
Published online: 08 Apr 2024 *

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