An Energy Efficient Meta-Heuristic Task Scheduling and Joint Uplink and Downlink Optimization Framework for Real-Time Mobile Edge Cloud Data

Abstract

Mobile edge cloud(MEC) plays a vital role in medium to large scale applications for task scheduling process. Join task uplink and downlink computations are used to optimize the cost and time computation for large scale applications. Most of the traditional mobile edge cloud based task scheduling models are independent of uplink and downlink estimations. The primary objective is to enhance energy efficiency and improve the user experience by maximizing the number of offloaded tasks during uplink communication, while ensuring that the computation resources of MEC remain at an acceptable level. In this work, an efficient joint load task scheduling approach is designed in order to improve the time, energy and load balancing properties in large scale applications. Experimental results show that the proposed approach has better efficiency in terms of runtime and energy consumption, leading to the improved energy efficiency in mobile edge cloud environments.