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.
A 2026 study studied this question.