Joint Optimization of Energy Consumption and Packet Scheduling for Mobile Edge Computing in Cyber-Physical Networks

Due to advances in Internet of Things technologies, mobile devices have become an inseparable part of human life. The limited executing capabilities of mobile devices along with constrained energy remain as barriers in front of this expectation. To address these challenges, mobile edge computing (MEC) is considered as a promising computing model to offer computing ability to mobile users in fifth-generation networks. In this paper, we jointly create an optimization problem to minimize the combination of energy cost and packet congestion. By adopting a promoted-by-probability scheme, we efficiently control packet congestion of different priority packets transmitted to MEC. An improved krill herd metaheuristic optimization algorithm is presented to obtain optimal results for minimizing the total overhead of MEC in terms of energy consumption and queuing congestion. The evaluation study demonstrates that our proposal performs efficiently in terms of energy consumption and execution delay.