  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.
