Joint scheduling algorithm for correlative tasks in multi-access edge computing

Abstract

Multi-access edge computing (MEC) has emerged as a promising computing paradigm for resource-intensive applications.However, most of the previous research work has not considered correlative tasks, which may lead to infeasible scheduling decisions.Considering that some tasks on the application must be completed locally and another non-negligible energy consumption problem in the multi-access edge computing offloading scenario, the joint scheduling algorithm of correlative tasks on the local and edge sides was studied.The problem was formalized as minimizing the energy consumption in the system while satisfying the application’s completion deadline constraints, and the joint scheduling (JS) algorithm was proposed to solve the problem.Finally, the performance of the JS algorithm in the application completion rate and system energy consumption were analyzed through simulation experiments.The simulation results show that the JS algorithm is superior to other comparison algorithms in the application completion rate and can save at least 43% of the system energy consumption.

Keywords

• multi-access edge computing;correlative task;energy consumption;task scheduling;joint scheduling algo-rithm