Hybrid Meta-Heuristic Technique Load Balancing for Cloud-Based Virtual Machines

Authors

  • Prabhakara B. K. Department of ISE, A J Institute of Engineering &Technology, Mangaluru, VTU Belagavi, India https://orcid.org/0000-0001-6001-7259
  • Chandrakant Naikodi Department of Studies and Research in Computer Science (PG), Davangere University, India.
  • Suresh L. Department of ISE, RNS Institute of Technology, Bengaluru, VTU Belagavi, India

Keywords:

Particle Swarm Optimization, Virtual Machine, Load Balancing, Heuristics Predict Origin Finish Time

Abstract

To efficiently develop cloud computing at the lowest price and the shortest time to deliver assets, task planning with Virtual Machines (VMs) has been crucial. the current report's several investigation holes for work scheduling optimization. This study included information and should be processed to solve the load balancing mechanism in the cloud environment. In this research, strategy-oriented combined support and load balancing structure has been created to maximize the use of virtual machines using similar weight distribution. The suggested method integrates heuristic and metaheuristic techniques to attain its optimal makespan& pricing efficiency HPOFT-MACO structure used two-step methodologies called Heuristics Predict Origin Finish Time (HPOFT) & Metaheuristic Ant Colony Optimization (MACO) to improve job management as well as cut costs and time.

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POFT heuristics using ACO

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Published

16.01.2023

How to Cite

B. K., P., Naikodi, C. ., & Suresh L. (2023). Hybrid Meta-Heuristic Technique Load Balancing for Cloud-Based Virtual Machines. International Journal of Intelligent Systems and Applications in Engineering, 11(1), 132–139. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2451

Issue

Vol. 11 No. 1 (2023)

Section

Research Article

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