COMPASS: An Integrated Computing-Network Routing Mechanism for Spatiotemporal Mismatch in Computing Power Network

XIAO Wei; ZHAO Baokang; SU Jinshu; HUANG Xuefeng; SHI Weijia

doi:10.11999/JEIT260555

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XIAO Wei, ZHAO Baokang, SU Jinshu, HUANG Xuefeng, SHI Weijia. COMPASS: An Integrated Computing-Network Routing Mechanism for Spatiotemporal Mismatch in Computing Power Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260555

Citation:

XIAO Wei, ZHAO Baokang, SU Jinshu, HUANG Xuefeng, SHI Weijia. COMPASS: An Integrated Computing-Network Routing Mechanism for Spatiotemporal Mismatch in Computing Power Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260555

Citation:

XIAO Wei, ZHAO Baokang, SU Jinshu, HUANG Xuefeng, SHI Weijia. COMPASS: An Integrated Computing-Network Routing Mechanism for Spatiotemporal Mismatch in Computing Power Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260555

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COMPASS: An Integrated Computing-Network Routing Mechanism for Spatiotemporal Mismatch in Computing Power Network

doi: 10.11999/JEIT260555 cstr: 32379.14.JEIT260555

1.
College of Computer Science and Technology, National University of Defense Technology, Changsha 410000, China
2.
Academy of Military Science, Beijing 100850, China

Funds: National Natural Science Foundation of China (Grant No. U22B2005), National Natural Science Foundation of China (Grant No. 62372462)

Received Date: 2026-05-03
Accepted Date: 2026-09-15
Rev Recd Date: 2026-09-15

Available Online: 2026-09-19

Abstract

Abstract

Objective Global data growth challenges computing infrastructure, while Computing Power Networks (CPN) face a critical limitation: separating network routing from computing offloading yields suboptimal scheduling due to inconsistent computing and network state dimensions. This spatiotemporal resource mismatch causes nearly 20% higher task failure rates in imbalanced versus uniform task arrival scenarios. Furthermore, existing DRL-based scheduling schemes lack topology generalization and flexible action spaces for arbitrary CPN nodes. This work aims to design an end-to-end integrated computing-network routing mechanism to resolve this mismatch, jointly optimize task failure rates and completion times, and enhance CPN dynamic adaptability and service quality. Methods First, we construct a centralized software-defined CPN architecture, mathematically modeling task arrivals, network bandwidth, computing resources, and service times, alongside a multi-objective function minimizing task failures and overall service time. Next, we develop the DDQN-based COMPASS algorithm featuring: (1) a 3D state representation integrating network topology, real-time computing status, and task attributes; (2) a joint, truncated action space coupling computing node selection and routing paths to reduce exploration complexity; and (3) a multi-objective weighted reward with heavy penalties for task failure to prevent poor strategy convergence. A tailored training workflow addresses experience delays caused by execution latency, incorporating routing and DRL training algorithms. To validate performance, we developed the open-source CNRSim platform based on EdgeCloudSim, integrating three real backbone topologies, four task types, and mainstream benchmark schemes. Results and Discussions Parameter tuning identified an optimal action space dimension of 20 (Fig. 5) and a learning rate of 5.0E-5, effectively balancing convergence speed and final performance (Fig. 6). Evaluated on three real backbone topologies, COMPASS outperformed traditional benchmarks, improving the average reward by 48% (Fig. 7) and achieving the lowest cumulative task failure rate (Fig. 8). Notably, under imbalanced task distributions, it significantly reduced the failure rate from 27.0% to 9.5% (Fig. 9). In-depth analysis on the GEANT2 topology revealed that COMPASS ensures superior computing load balancing across servers (Fig. 10) and processes more tasks with lower bandwidth occupancy (Fig. 11). Failure analysis indicated that jointly optimizing resources mitigates failures caused by bandwidth shortages and node overloads (Fig. 9). Furthermore, COMPASS maintained peak average rewards under dynamic workloads (Table 3). Conclusions This paper proposes COMPASS, a DRL-based integrated computing-network routing algorithm, to address CPN bottlenecks caused by spatiotemporal mismatches between resource distribution and task arrivals. By utilizing a 3D state representation, joint action design, and a multi-objective reward mechanism, COMPASS enables global collaborative scheduling. Extensive experiments on the CNRSim platform confirmed its superiority over benchmarks in reward maximization, failure reduction, and load balancing, particularly in imbalanced scenarios. This work offers a framework for CPN optimization and large-scale distributed resource scheduling. Future work will explore task fault tolerance and retransmission mechanisms, and investigate the potential of collaborative optimization by combining COMPASS with emerging intelligent technologies.
- Computing Power Network,
- Reinforcement Learning,
- Routing,
- Scheduling

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References(24)

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