Multi-User Multi-Server Multi-Channel Computation Offloading Strategy for Mobile Edge Computing

Mobile edge computing is a new computing paradigm that can extend cloud computing capabilities to the edge network, supporting computation-intensive applications such as face recognition, natural language processing, augmented reality. Notably, computation offloading is a key technology of mobile edge computing to improve mobile devices performance and user experience by offloading local tasks to edge servers. In this paper, we study the problem of computation offloading under multi-user, multi-server, and multi-channel scenarios, and propose a computation offloading strategy considering the quality of service (QoS) of users, server resources, and channel interference. This strategy consists of three stages: (1) In offloading decision stage, the offloading decision is made based on the beneficial degree of computation offloading, which is measured by the total cost of local computing of mobile device in comparison with the edge-side server. (2) In the server selection stage, the candidate is comprehensively evaluated and selected by a multi-objective decision based on Cov-AHP for computation offloading. (3) In the channel selection stage, a multi-user and multi-channel distributed computation offloading model based on potential game is proposed by considering the influence of channel interference on the user's overall overhead. The corresponding multi-user and multi-channel task scheduling algorithm is designed to maximize the overall benefit by finding the Nash equilibrium point of the potential game. Amounts of experimental results show that the proposed method can greatly increase the number of beneficial computation offloading users, and effectively reduce the energy consumption and time delay.

I. Introduction

With the development of artificial intelligence and Internet of Things (IoT) technology, a large number of computation-intensive and time-sensitive mobile applications have appeared on mobile terminals, such as face recognition, natural language processing, and augmented reality. However, the limited computing capability and energy storage of mobile terminals cannot provide intensive computing and complete high-energy tasks. A large number of application training process must be deployed on the cloud service platform, so that a large amount of training data generated on the mobile terminal needs to be transmitted to the cloud through the core network, resulting in a sharp increase in the already congested core network load. Application transmission delay and transmission energy consumption will also increase greatly, which will cause service request failure and QoS of user to decline.