Optimizing Task Offloading in VEC: A PDQKM Scheme Combining Deep Reinforcement Learning and Kuhn-Munkres Matching

Vehicular Edge Computing (VEC) is an emerging computing paradigm that serves as a specific application of Mobile Edge Computing (MEC) in intelligent transportation systems. As a core technology of VEC, task offloading improves computing efficiency and service quality by offloading computing tasks from vehicular devices to edge nodes. However, the high mobility of vehicles, heterogeneity of resources, and real-time requirements present significant challenges for task offloading. To address the issue of reducing overall system latency and increasing the offloading success rate in multi-task offloading, we propose a task offloading scheme combining Deep Reinforcement Learning (DRL) and Kuhn-Munkres (KM) Matching algorithm, named the PDQKM offloading scheme. Firstly, to mitigate the delay caused by frequent Roadside Unit (RSU) handovers, we propose a method to detect whether a vehicle is within the coverage area of the RSU. This method filters out unreasonable offloading decisions, avoiding the overhead associated with frequent RSU handovers. Secondly, the combination of DRL and the KM matching algorithm leverages the strengths of both approaches. DRL provides initial offloading strategies in highly dynamic and high-dimensional decision environment. Although DRL may get stuck in local optima, it can quickly adapt to environmental changes. The KM matching algorithm, a classic solution for perfect task-resource matching, performs global optimization on the initial strategies provided by DRL. This integration overcomes the limitations that a single algorithm might have. Finally, to effectively coordinate and manage the heterogeneous resources of RSU, we utilize an improved KM matching algorithm to update computational resources in real time, enhancing matching efficiency. Experimental results demonstrate that PDQKM outperforms comparable offloading schemes in terms of overall system latency and offloading success rate optimization.