Abstract: 】To address issues such as resource mismatch, load bottlenecks, and service instability caused by demand fluctuations and large-scale bursty tasks in mobile edge computing, a cooperative supply strategy based on approximate Shapley values (ASVC) is pro posed. First, a task allocation model based on bidirectional preference matching is constructed, which considers both the performance requirements of user tasks and the resource status of edge nodes. The Gale-Shapley algorithm is used to achieve optimal supply-demand matching. Second, to reduce the computational complexity of Shapley value estimation during coalition formation, an adaptive sam pling-based optimization scheme is introduced. This approach significantly reduces the computation time of Shapley values while maintaining accuracy. Finally, task data is allocated according to the proportional contribution of each node, improving system fairness and resource utilization efficiency. Simulation results show that, compared with existing algorithms, the proposed ASVC algorithm improves service quality, delay control, task completion rate, and system load balancing by approximately 27.8%, 31.0%, 30.8%, and 21%, respectively.

摘要： ：针对移动边缘计算中因需求波动与大规模突发任务引发的资源失配、负载瓶颈及服务不稳定等问题，提出 一种基于近似Shapley值的合作供给策略ASVC。首先，构建了基于双向偏好匹配的任务分配模型，结合用户任务 的性能需求与边缘节点的资源状态，通过Gale-Shapley算法实现供需最优匹配；其次，为降低构建联盟时Shapley 值的计算复杂度，提出了基于自适应抽样的 Shapley 值计算优化方案，该方案在保证计算精度的前提下，实现了 Shapley 值计算时间的阶跃式减少；最后，依据节点贡献度比例进行任务数据分配，提升系统公平性与资源利用效 率。仿真结果表明，提出的 ASVC 算法相较于现有算法，在服务质量、时延控制、任务完成率和系统负载均衡方 面分别提升约27.8%、31.0%、30.8%和21%。