CoOR: Collaborative Task Offloading and Service Caching Replacement for Vehicular Edge Computing Networks

Abstract

In vehicular edge computing networks, edge service caching has emerged as a promising technology that supports delay sensitive applications. When the vehicles pass the coverage areas of roadside units (RSUs), the vehicles can offload part/all of their tasks to the RSUs that had cached the related service data in advance. However, it is difficult to utilize the service caching resources in RSUs efficiently, for the high mobility, variable task computing requests of vehicles and the limited storage and computation capacity of RSUs. In this paper, we propose a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">co</u> llaborative task <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</u> ffloading and service caching <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</u> eplacement scheme from a vehicle perspective, named CoOR, the task processing cooperation between the adjacent RSUs and the service caching replacement for vehicle are mainly considered. We formulate the system computing cost and delay minimization as a mixed integer programming problem, it faces challenges of task offloading and service caching coupling, the heterogeneous computation requests and dynamic data transmission conditions. Thus, we develop an iterative algorithm combined with the Gibbs sampling and deep reinforcement learning (DRL) to find the optimal decisions. Extensive simulation results show that the proposed schemes have good convergence and better performance than the traditional baselines.