Dual-Tree Genetic Programming for Automated Discovery of Computing Power Network Scheduling Heuristics

The computing power network links distributed and heterogeneous computing resources via the network, to enable efficient configuration and utilization of computing power. However, scheduling computing resources within this network presents several challenges, such as resource heterogeneity, vast search spaces, uncertainty, high constraints, and real-time requirements. To simulate the real-world computing power network scheduling problem, this paper integrates cloud servers, fog servers, and edge servers into a unified computing power network, considering their respective GPU, CPU, and bandwidth resources. We introduce a Dual-Tree Genetic Programming (DTGP) approach that simultaneously optimizes two critical decisions—routing and sequencing—to automatically evolve computing power network scheduling heuristics for real-time decision-making. Additionally, to improve the performance of DTGP, we propose new terminal sets tailored to fit within these two GP trees. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art methods in six test scenarios, achieving up to 40% reduction in completion time.