Real-Time Scheduling Mechanisms for Heterogeneous Distributed Systems in Edge-Enabled Urban Renewal Digital Twin Platforms
DOI:
https://doi.org/10.4108/eetsis.11900Keywords:
Urban Digital Twin, Distributed Task Scheduling, Heterogeneous Edge Computing, Deep Reinforcement LearningAbstract
INTRODUCTION: Urban renewal digital twin systems must support high-fidelity rendering and millisecond-level interactive control across geographically distributed, heterogeneous edge computing infrastructures. Conventional scheduling approaches, often assuming hardware homogeneity and static task graphs, struggle with dynamic service-chain reconfigurations driven by urban spatial entropy fluctuations, leading to resource misallocation and service instability in large-scale distributed environments.
OBJECTIVES: This paper aims to develop a real-time, scalable scheduling framework for heterogeneous distributed edge systems that jointly accounts for hardware diversity, evolving directed acyclic graph (DAG)-based workloads, and decentralized decision-making while preserving data locality and model privacy.
METHODS: We propose a hierarchical scheduling architecture integrating physical and logical coordination: (1) a resource affinity mask encodes hardware constraints as a prior to prune infeasible placements; (2) a spatio-temporal graph neural network captures critical path dynamics in non-stationary task DAGs; and (3) a federated policy distillation mechanism enables knowledge transfer across structurally diverse cloud-edge-end agents without sharing raw models or data.
RESULTS: Experiments on the Alibaba Cluster Trace and Shanghai Telecom datasets show that the proposed method reduces average latency to 43.8 ms, achieving a 25.6% reduction compared with CO-MARL, sustains a 94.7% task completion rate under long-tail traffic surges, and achieves an edge inference latency of 2.1 ms.
CONCLUSION: The “physical priors plus topology awareness” paradigm demonstrates that heterogeneous-aware, distributed coordination is essential for real-time digital twin services at scale.
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