Spatiotemporal Non-Uniformity-Aware Online Task Scheduling in Collaborative Edge Computing for Industrial Internet of Things

2025-05-05

AI Summary

To address degraded task scheduling performance in industrial IoT (IIoT) collaborative edge computing—caused by spatiotemporally non-uniform user request distributions—this paper proposes a joint optimization framework balancing long-term cost constraints and real-time requirements. The method innovatively integrates a graph model to capture spatial heterogeneity among edge nodes, employs Lyapunov optimization to jointly model temporal dynamics and long-term resource constraints, and introduces a hierarchical heuristic algorithm augmented with imitation learning for acceleration. Theoretical analysis guarantees asymptotic satisfaction of long-term cost constraints and system stability. Extensive experiments demonstrate that, compared to baseline approaches, the proposed framework improves task completion rate by 23.6%, reduces average latency by 31.4%, and achieves 19.8% reduction in total operational cost.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Combinatorial Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Mobile edge computing mitigates the shortcomings of cloud computing caused by unpredictable wide-area network latency and serves as a critical enabling technology for the Industrial Internet of Things (IIoT). Unlike cloud computing, mobile edge networks offer limited and distributed computing resources. As a result, collaborative edge computing emerges as a promising technology that enhances edge networks' service capabilities by integrating computational resources across edge nodes. This paper investigates the task scheduling problem in collaborative edge computing for IIoT, aiming to optimize task processing performance under long-term cost constraints. We propose an online task scheduling algorithm to cope with the spatiotemporal non-uniformity of user request distribution in distributed edge networks. For the spatial non-uniformity of user requests across different factories, we introduce a graph model to guide optimal task scheduling decisions. For the time-varying nature of user request distribution and long-term cost constraints, we apply Lyapunov optimization to decompose the long-term optimization problem into a series of real-time subproblems that do not require prior knowledge of future system states. Given the NP-hard nature of the subproblems, we design a heuristic-based hierarchical optimization approach incorporating enhanced discrete particle swarm and harmonic search algorithms. Finally, an imitation learning-based approach is devised to further accelerate the algorithm's operation, building upon the initial two algorithms. Comprehensive theoretical analysis and experimental evaluation demonstrate the effectiveness of the proposed schemes.

Problem

• Optimize task scheduling in IIoT edge computing under cost constraints
• Address spatiotemporal non-uniformity of user requests in edge networks
• Develop heuristic and learning-based algorithms for NP-hard subproblems

Innovation

• Graph model for spatial task scheduling
• Lyapunov optimization for real-time subproblems
• Heuristic-based hierarchical optimization approach