An Efficient Ant Colony Optimization Algorithm for Resource Provisioning in Cloud
M. Aliyu1, M. Murali2, A. Y. Gital3, S. Boukari4
1M. Aliyu, Mathematical Sciences Department, Abubakar Tafawa Balewa University Bauchi, Nigeria.
2M. Murali, Department of Computer Science and Engineering, SRM IST, Tamilnadu, India.
3A. Y. Gital, Mathematical Sciences Department, Abubakar Tafawa Balewa University Bauchi, Nigeria.
4S. Boukari, Mathematical Sciences Department, Abubakar Tafawa Balewa University Bauchi, Nigeria.
Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 421-429 | Volume-8 Issue-4, November 2019. | Retrieval Number: D6968118419/2019©BEIESP | DOI: 10.35940/ijrte.D6968.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Paper Several Ant Colony Optimization (ACO) techniques for Cloud resources management are considered by many researchers. ACO techniques in existence still need some improvements for effective resource management and planning with the heterogeneous and voluminous services offered. Hence, an optimized hybrid scheme that combined deterministic characteristics for exploiting ACO search process is proposed. Spanning Tree (ST) algorithm was chosen in the hybridization that obtained a faster convergence speed, minimized makespan time and throughput that ensured resource utilization in least time and cost. Extensive experiments were conducted in cloudsim simulator provided an efficient result compared to other ACO techniques as it significantly improves performance.
Keywords: Cloud Computing, Deterministic, Metaheusristics Optimization.
Scope of the Article: Cloud Computing.