An energy-aware computation offloading method for smart edge computing in wireless metropolitan area networks

Abstract

Currently, the booming popularity and growth of mobile devices in urban cities leads to the surge of various computation-intensive mobile applications, such as virtual reality and online video, which are strict with the computing capability and battery life of the mobile devices. To address this issue, smart edge computing in Wireless Metropolitan Area Networks (WMAN) is proposed to enable mobile users to offload computation-intensive tasks to the edge computing nodes which deploys computing resources nearby the mobile devices. However, the normal operation of edge computing nodes consumes plenty of energy. Thus, it is still a challenge to be aware of energy consumption while the computing tasks are migrated to the Edge Computing Nodes (ECNs). In view of this challenge, an Energy-Aware Computation Offloading method, named EACO, is designed to reduce the energy consumption. Technically, we analyze all access point (AP) routings between the original AP to destination AP and select the shortest path to offload the computing tasks. Furthermore we adopt Non-dominated Sorting Genetic Algorithm II (NSGA-II) to realize multi-objective optimization to shorten the offloading time of the computing tasks and reduce the energy consumption of the ECNs. Besides, we exploit Multiple Criteria Decision Marking (MCDM) and Simple Additive Weighting (SAW) to select the optimal offloading solution. Finally, the simulation experimental results show that our proposed EACO outperforms other methods.