Research On Computing Offloading Method Based On Mobile Edge Computing In Internet Of Vehicles

In the Internet of Vehicles scene,the original "cloud-car" network architecture can no longer meet the delay requirements of new tasks such as autonomous driving and virtual reality.In mobile edge computing,servers are deployed near the user side,providing users with an IT service environment and cloud computing capabilities,reducing the delay consumption required for task offloading.However,the resources possessed by edge servers are also limited.In order to increasing the success rate of task offloading and reducing the energy consumption and delay consumption of task offloading,this article proposes the following task uninstall scheme:(1)Aiming at task computing offloading,edge server energy consumption,task success rate and other issues,a software-defined network-based mobile edge computing offloading strategy is proposed.In this strategy,several dimensions such as task characteristics,energy consumption and delay consumption are modeled.According to the characteristics of the task,the K-means clustering algorithm is used to analyze the task and calculate its specific uninstallation position.At the same time,the network resources of the edge server are obtained according to the software-defined network controller to ensure that when multiple computing tasks come,multiple edges can be realized load balancing between servers.The simulation results show that this strategy can not only improve the success rate of task offloading of edge computing servers,but also reduce the delay consumption required to complete tasks and the energy consumption of edge computing servers.(2)Aiming at many of the network traffic that are requested for the same content and according to the emergency of different tasks,a mobile edge offloading strategy based on cloud-side cooperation in the Internet of Vehicles is proposed.In this strategy,the computing resources required to complete the task,the maximum tolerable time,the number of requested contents,and the task size are modeled and their priority is calculated.Finally,the final uninstallation is determined according to the expected resource consumption of the uninstallation to different server positions.The simulation results show that this strategy can not only improve the computing efficiency of high-priority tasks in edge servers,but also reduce the delay and energy consumption required to complete all tasks.