Volume 18, Number 3

An Enhanced Starfish Optimized Virtual Machine Migration for Improving Load Balancing in Fog-Cloud Environments

  Authors

Charles Mahimainathan A and Suganya M, RVS College of Arts and Science, India

  Abstract

In this age of the Internet of Things (IoT), crucial management frameworks for large, dispersed systems must include fog and cloud computing technologies. By combining IoT with fog and cloud computing, massive amounts of IoT data can be processed in real time, meeting all of your processing needs. Nonetheless, optimizing Resource Allocation (RA) and Load Balancing (LB) in dynamic and varying operating settings continues to be a critical issue. Many traditional RA-LB techniques in IoT-fog-cloud systems frequently experience dynamic and unpredictable workload variations, leading to the overloading of some Physical Machines (PMs) and the underutilization of others. This disparity might result in high energy usage and resource scarcity in fog-cloud data centres. This study presents a novel Optimized Virtual Machine (VM) Migration-based RA-LB (OVM2-RALB) technique utilizing the Enhanced Starfish Optimization Algorithm (ESOA) for IoT-fog-cloud systems. To drastically cut down on energy consumption in fog-cloud data centers, the main objective is to dynamically move VMs from overcrowded PMs to underused ones. At first, the incoming job is categorized as eitherfog-dependent or cloud-dependent based on its guaranteed ratio. This ratio is decided by available PMs and their corresponding VMs in fog and cloud. The mean load for each PM is computed, which is utilized to calculate the load balancing factor to identify overloaded and underloaded PMs. Then, the ESOA, which is an improvement upon the standard SOA by combining tent chaotic mapping and Logarithmic Spiral Reverse (LSR) learning, is adopted for the VM migration process. This ESOA seeks to choose the most suitable PM for VM migration. It also determines the most appropriate VM for migration based on migration expenses, load balancing factor, energy use, and bandwidth utilization. Furthermore, the selected VM in the overloaded PM is migrated to the chosen underloaded PM. Thus, this VM migration results in a balanced load and alleviates overload on the PM. Finally, simulation results show that this OVM2-RALB outperforms conventional RA-LB techniques in IoT-fog-cloud environments.

  Keywords

IoT-fog-cloud systems, Task scheduling, Resource allocation, Load balancing, VM migration, Starfish optimization