Research On Data Offloading Scheme Of C-V2X Edge Computing

With the rapid development of vehicle network,a variety of vehicular services have emerged.However,these services have higher requirements for delay and calculation,and the computing resources of the vehicle cannot meet these requirements.Therefore,in order to improve the computing capability and reduce the transmission delay in vehicles network,people have introduced Mobile Edge Computing(MEC)to vehicles network environment,and conducted reserch on the integration of C-V2X-based vehicle networking and MEC technology.In the integration scenario of C-V2 X and MEC,there are many problems when the vehicle performs data offloading.We propose two main issues: how to choose the offloading strategy for vehicles and how to solve the load balancing of edge servers.Then,We conducte in-depth research on these two issues.Aiming at the problem of how to choose the offloading strategy for vehicles,we propose a distributed offloading scheme based on game theory.This scheme converts the vehicle's task offloading problem into a game problem,establishes a communication model and a calculation model,and proves that the problem is a "potential game".And then we use the property of "potential game" to prove that this problem has a Nash equilibrium Finally,we use the characteristics of Nash equilibrium to design a distributed offloading scheme.Experimental simulation proves that the distributed offloading scheme based on game theory can better reduce the overhead of task offloading and improve the offloading efficiency.Aiming at how to solve the load balancing problem of edge servers,we propose a load balancing algorithm based on dynamic load index weights.This algorithm combines various load indicators to analyze the load status of the MEC server,we assign weights to these load indicators,and dynamically adjusts each weight,so that we can more accurately reflect the actual load status of the MEC server.Then,we set up a dispatch center and reasonably allocate transfer tasks based on the load status of each MEC server.Experimental simulation proves that the load balancing algorithm based on dynamic load index weights can better improve the load balancing degree of edge server clusters and reduce task calculation time.