Computation Offloading in a Mobile Edge Communication Network: A Joint Transmission Delay and Energy Consumption Dynamic Awareness Mechanism

Various problems arise in the maintenance of communication networks. For example, on-site maintenance personnel have insufficient work experience. Devices used for maintenance work have limited computing resources and battery life. Moreover, most maintenance systems still use the centralized single processing mode of traditional cloud computing, which increases the data center computing pressure and slows the data flow. To overcome these problems, we propose a communication network edge maintenance system based on smart wearable technology and introduce computation offloading technology for mobile edge computing (MEC). Before offloading, we propose a multimerged computing sorting segmentation (MCSS) algorithm to divide a part of the task to offload. When making an offloading decision, we access a suitable MEC service node for each user with the lowest transmission cost and establish a related model. We use an improved Kuhn-Munkras (KM) algorithm that considers fairness among users to solve this model. After that, we propose a dynamic energy-efficiency awareness strategy. When tasks are processed locally, we optimize the CPU clock frequency. When tasks are offloaded, we adaptively allocate the transmission power. Finally, we conduct a simulation experiment. The results demonstrate that the proposed scheme can reduce the transmission cost and improve the performance, thereby increasing the level of on-site maintenance work.