Deploying an efficient and reliable scheduling for mobile edge computing for IoT applications

Abstract

Mobile Edge Computing (MEC), which is the main technology behind 5th Generation (5G) networks, is a nascent paradigm that meets the demands of IoT (Internet of Things) and localized computing. From an end-user point of view, it can be regarded as a refinement of cloud computing and Fog computing. The transfer of computational tasks to the closet MEC servers leads to energy efficiency and low latency. It is important to apply the most suitable policies for scheduling, especially when configuring different modules of an IoT application in MEC. In this paper, three algorithms, namely the heuristic Bald Eagle Search Optimisation [BESO] algorithm, Particle Swarm Optimization algorithm [PSO], and Genetic Algorithm [GA], are presented to carry out heuristic offloading of computational tasks with a view to improving the latency and performance of MEC. However, the most effective algorithm must be adopted to conduct these tasks. As a result, this paper attempts to find an algorithm that is most appropriate for MEC. To demonstrate this. the three algorithms were tested in the Long-Term Evolution [LTE] based Orthogonal frequency-division multiplexing [OFDM] network during a period when the edge nodes had no adequate resources. The performance and efficiency of the three algorithms, BESO, PSO and GA, were determined and compared. After generating the results, the comparative results were evaluated. In terms of offloading the computational tasks, the BESO algorithm was discovered to perform better, with greater energy efficiency and lower latency, than the other two algorithms.