Mobile Edge Computing (MEC) holds excellent potential in Congestion Management (CM) of 6G vehicular networks. A reasonable schedule of MEC ensures a more reliable and efficient CM system. Unfortunately, existing parallel and sequential models cannot cope with scarce computing resources and constrained channels, especially during traffic rush hour. In this paper, we propose a channel-constrained multi-core sequential model (CCMSM) for task offloading and resource allocation. The CCMSM incorporates a utility index that couples system energy consumption and delay, applying Genetic Algorithm combining Sparrow Search Algorithm (GA-SSA) in the branching optimization. Furthermore, we prove that the system delay is the shortest with the FCFS computing strategy in the MEC server. Simulation demonstrates that the proposed CCMSM achieves a higher optimization level and exhibits better robustness and resilient scalability for traffic spikes.

翻译：移动边缘计算（MEC）在6G车载网络的拥塞管理（CM）中具有巨大潜力。合理的MEC调度可确保更可靠、高效的CM系统。然而，现有并行与串行模型难以应对稀缺计算资源和受限通道的挑战，尤其在交通高峰时段。本文提出一种通道约束多核串行模型（CCMSM）用于任务卸载与资源分配。该模型引入耦合系统能耗与延时的效用指标，并在分支优化中采用麻雀搜索算法与遗传算法的混合策略（GA-SSA）。进一步，我们证明了在MEC服务器中采用先来先服务（FCFS）计算策略可使系统延时最短。仿真结果表明，所提出的CCMSM模型能实现更高优化水平，并对交通突发表现出更优的鲁棒性与弹性扩展能力。