Task Offloading Strategy and Simulation Platform Construction in Multi-User Edge Computing Scenario

Abstract

Various types of service applications increase the amount of computing in vehicular networks. The lack of computing resources of the vehicle itself will hinder the improvement of network performance. Mobile edge computing (MEC) technology is an effective computing method that is used to solve this problem at the edge of network for multiple mobile users. In this paper, we propose the multi-user task offloading strategy based on game theory to reduce the computational complexity and improve system performance. The task offloading decision making as a multi-user task offloading game is formulated to demonstrate how to achieve the Nash equilibrium (NE). Additionally, a task offloading algorithm is designed to achieve a NE, which represents an optimal or sub-optimal system overhead. In addition, the vehicular communication simulation frameworks Veins, SUMO model and OMNeT++ are adopted to run the proposed task offloading strategy. Numerical results show that the system overhead of the proposed task offloading strategy can degrade about 24.19% and 33.76%, respectively, in different scenarios.

Deep-Reinforcement-Learning-Based Distributed Computation Offloading in Vehicular Edge Computing Networks

“…In [21], a computation offloading method based on the game theory was proposed to trade off the energy consumption and delay in a multi-user and multi-channel environment. Wu et al [22] studied a multi-user task offloading strategy based on the game theory to reduce computational complexity. Despite the research progress, most of the above work focused on the binary offloading method.…”

Edge Collaborative Online Task Offloading Method Based on Reinforcement Learning

“…As the two main factors of task importance, the characteristics of the task are considered comprehensively. Yu et al [27][28][29] integrated mobility prediction in offload strategy and resource allocation methods, combining offload strategy and mobility management modules. This method can intelligently allocate tasks according to the user's mobile mode to allocate tasks to locations with better network conditions in the future as much as possible to reduce energy consumption.…”