Multi-objective optimization of task assignment in distributed mobile edge computing

Traditional computing models and centralized cloud computing are not capable of meeting today’s application requirements, especially when deploying technologies, such as the Internet of things (IoT), 5G, and wearable devices, on a large scale. Mobile edge computing (MEC) introduces the feasibility of using edge and smart devices, such as gateways and smart phones, to perform task execution of different applications. Moreover, an efficient task scheduling approach should consider the deadlines requirements and the power consumption of the edge devices. This paper proposes a multi-objective optimization solution to assign different application tasks to different edge devices while minimizing the energy consumption of edge devices and the computation time of tasks. Task dependencies and data distribution are considered within a new and more general MEC model. Multi-objective evolutionary algorithm (MOEA) framework is used to solve the optimization problem subject to deadline and power consumption constraints. Results show that the proposed multi-objective approach achieves better performance in terms of energy and computation time when compared to a single objective approach.