LBATSM: Load Balancing Aware Task Selection and Migration Approach in Fog Computing Environment

Abstract

With the rapid advancement of Internet of Things technology, the field of fog computing has garnered significant attention and hence become a workable processing platform for upcoming applications. However, compared with vast computing capability of the cloud, the fog nodes have resource constraints, are heterogeneous in nature, and highly distributed. Due to the growing demand as well as diversity of applications, the nodes in a fog network become overloaded, which makes load balancing a prime concern. In this work, a load balancing aware task selection and migration approach is proposed comprising two algorithms to select and place tasks from multiple overloaded nodes to suitable destination nodes. The Selection algorithm determines the tasks that should be migrated from overloaded nodes. Placement algorithm focuses on finding a near optimal solution by applying modified binary particle swarm optimization. Specifically, the objective is to minimize execution time and transfer time of tasks. Simulation studies conducted on iFogSim prove that the suggested approach outperforms the existing approaches in terms of task execution time, task transfer time, and makespan.