Computation Task Scheduling Optimization For Intelligent Connected Vehicles’ Cloud Control System

The development of Intelligent Connected Vehicles has become the strategic direction of the global automotive industry,but there are still severe problems in its applications.The limitations of computing and perception capabilities,as well as conflicts and competitions among vehicles,are two key issues.Intelligent Connected Vehicles’ Cloud Control System is a promising way to break through these limitations.This paper discusses the working logic,system requirements,and mathematical modeling methods of Cloud Control System,and then proposes optimization strategies for Cloud Control System in terms of computation task scheduling.Specifically,for the factors of service migration caused by vehicle mobility and the dynamic characteristics of Cloud Control System,a long-term optimization problem is constructed,and the cost and performance in the dynamic process are the optimization goals.Using Lyapunov Optimization,the long-time domain optimization problem is decoupled in the time domain and becomes a real-time optimization problem.Considering the problem of multi-vehicle contention for cloud resources in optimization decisionmaking,the mechanism design of computing resource leasing is adopted to construct a dynamic Cournot game relationship among multiple vehicles.Based on the descent method,a distributed iteration algorithm is designed.Through iteration,the system achieves the Nash equilibrium state and achieves the approximate optimal resource allocation among multiple vehicles.In this way,the long-term domain and global collaborative optimization of computing task scheduling are realized,and the strategy of computation scheduling in the environment of Intelligent Connected vehicles’ Cloud Control System is proposed.Based on SUMO and OMNet++,a simulation platform for Intelligent Connected Vehicles’ Cloud Control System is built.Road and traffic models are constructed in SUMO,and network protocols and cloud control component models are developed in OMNet++,and then test scenarios are established.Veins framework is used to realize the joint simulation of SUMO and OMNet++,and then an automated test tool for batch processing of large-scale simulations is developed.The simulation results verify the optimization effect of the optimization algorithm on the performance of the Cloud Control System.Taking Adaptive Cruise Control,vehicle queue control as examples.This paper verifies the support effect of the Cloud Control System on vehicles’ applications.To test and verify the feasibility and performance of the Cloud Control System in more depth,this paper builds an Intelligent Connected Vehicle test field and a prototype Cloud Control System.The test filed contains RSU and OBU based on the LTE-V protocol,several roadside sensors,and infrastructure of Cloud Control System.Using container technology Docker and cluster management technology Kubernetes,This paper designs and implement the software architecture of Cloud Control System.Taking the car-side and roadside fusion perception application as an example,the application development and verification test in the container environment have been carried out,which proves that Cloud Control System based on container technology can meet the requirements of function and performance.