Load balancing optimization based on hybrid Heuristic-Metaheuristic techniques in cloud environment

Load balancing among virtual machines (VMs) is significant for delivering the cloud services in optimized way with minimum cost paid and total time acquired to deliver the services. In this paper, the various research gaps for load balancing optimization in the past literature have been presented, which need to be addressed for solving the load balancing problem in cloud environment. In present work, Hybrid approach based resource provisioning and load balancing framework for workflows execution has been proposed to optimize the utilization of VMs with uniform load distribution. The proposed framework is based on the hybridization of heuristic techniques with metaheuristic algorithm to achieve its optimal performance in terms of makespan and cost. Two hybrid approaches have been proposed for HDD-PLB framework-Hybrid Predict Earliest Finish Time (PEFT) Heuristic with Ant Colony Optimization (ACO) metaheuristic (HPA) and Hybrid Heterogeneous Earliest Finish Time (HEFT) heuristic with ACO (HHA). The two proposed approaches for load balancing have been analyzed and compared to determine which is superior for proposed HDD-PLB framework. (C) 2019 Production and hosting by Elsevier B.V. on behalf of King Saud University.

Automated summary

This paper presents the research gaps in load balancing optimization and proposes a hybrid approach based resource provisioning and load balancing framework to optimize the utilization and distribute the load evenly among virtual machines.