Collaborative Task Offloading Scheduling Of Green Edge Computing For Internet Of Vehicles

In recent years,Internet of vehicles(IoV)technology has developed rapidly.Vehicles can provide intelligent on-board services such as multimedia entertainment and automatic driving,and provide users with all kinds of real-time and convenient information.However,vehicle tasks have higher requirements for delay,bandwidth and energy consumption.Edge computing introduces the distributed processing ability close to the end user to improve the quality of service(QoS)of the Internet of vehicles system.However,the high energy consumption and high carbon emission of edge computing have triggered a strong demand for green edge computing.In order to provide satisfactory computing performance and realize green computing,this paper studies a green edge computing system for vehicle network(GEC-LoV).This system integrates energy harvesting technology(EH)to effectively utilize time-varying hybrid renewable energy to achieve self-sustainable power supply for edge computing nodes.This paper formulates the cooperative task scheduling problem based on energy collection,proposes an effective computational offloading strategy and energy collection strategy,that is,the Collaborative Task Scheduling with Energy Harvesting(CTSEH)algorithm with an approximate ratio of(2-ε).The algorithm dynamically schedules tasks between computing nodes to minimize the system response time.Theoretical analysis and simulation results show that CTSEH algorithm can effectively use green energy and reduce system response time.The coverage of fixed edge computing nodes is limited,and the new mobile edge network of deploying V-edge can better adapt to the diversified computing needs of smart cities.Therefore,this paper designs a V-edge-based Partitioned User Task Scheduling Mechanism(PUTSM)for a largescale vehicle edge system for multiple users.Based on the Bisecting K-means algorithm,users are grouped into groups,and an efficient Task Scheduling Mechanism(TSM)is implemented in the task group to obtain an efficient V-edge scheduling scheme,which maximizes the operating benefits of Vedge.Theoretical analysis shows that TSM provides an approximate ratio of O(log OPT).Finally,this paper performs extensive simulation experiments and shows that the TSM outperforms 41.3%more than the stochastic task scheduling algorithm and 14.9%more than the "tree" heuristic.The performance is greatly improved,and the specific is very practical.