【摘要】 在万物互联时代，分散计算通过整合异构设备空闲资源缓解了终端计算能力不足的问题，但任务执行延迟与节点能耗的博弈不平衡、设备异构性带来的调度适配难题亟待解决。针对该问题，本文构建了兼顾任务实时性、执行延迟、系统能耗与节点利益的多目标实时任务调度模型，以最小化延迟上限与总能耗、最大化系统满意度为优化目标，提出了基于双边匹配博弈的实时任务调度算法。通过设计任务-计算节点双向偏好机制，结合多轮次稳定匹配策略，实现任务与节点的精准适配；同时引入次级判断规则解决偏好冲突，保障调度公平性与效率。仿真实验表明，与基线相比，该算法显著降低了总执行成本，有效平衡了任务执行延迟和计算节点的能耗，并兼顾了网络中每个计算节点的利益。更多还原

【Abstract】 In the era of the Internet of Things, distributed computing alleviates the problem of insufficient terminal computing power by integrating idle resources of heterogeneous devices. However, the imbalance between task execution delay and node energy consumption, and the scheduling and adaptation challenges brought about by device heterogeneity, urgently need to be addressed. To tackle this problem, this paper constructs a multi-objective real-time task scheduling model that considers task real-time performance, execution delay, system energy consumption, and node interests. The model aims to minimize the delay upper bound and total energy consumption while maximizing system satisfaction. A real-time task scheduling algorithm based on bilateral matching game is proposed. By designing a bidirectional preference mechanism between tasks and computing nodes, combined with a multi-round stable matching strategy, accurate matching between tasks and nodes is achieved. Simulation results show that compared with the baseline scheme, the proposed algorithm significantly reduces the total execution cost, effectively balances the task execution delay and the energy consumption of compute nodes, and takes into account the interests of each network compute node.更多还原