Optimization of Edge–Cloud Collaborative Computing Resource Management for Internet of Vehicles Based on Multiagent Deep Reinforcement Learning

In the current Augmented Intelligence of Things (AIoT) vehicular road collaborative edge offloading application scenario, considering the limited computing resources of edge servers and the unpredictability of vehicle user offloading requests, this article fully utilizes the computing resources in the Internet of Vehicles (IoV). During the offloading task execution phase, the caching system is applied to the task execution process to solve the problem of resource waste caused by redundant computing. At the same time, in order to alleviate the problem of low cache hit rate of task calculation results, the task is decomposed into several sub tasks, including vehicle self-execution, edge offloading, cloud offloading, etc. Considering the high dependency between tasks and application services in edge offloading scenarios, as well as the limited storage space of edge servers, this article adopts a cluster edge server application layout strategy. Through the cooperation of various nodes, the offloading services are provided to vehicles as a whole, giving the cluster the ability to handle various tasks. On this basis, in order to reduce the additional system costs caused by task and application misalignment within the cluster, a multiagent deep reinforcement learning algorithm (MADRL) for edge-cloud collaboration is further proposed to obtain the corresponding placement relationship between in vehicle applications and edge servers, and optimize in vehicle computing resources. The simulation results show that the algorithm proposed in this article can improve the success rate of edge offloading, effectively reduce task offloading latency, and improve system resource utilization.