Integrated network-computing resource allocation and optimized scheduling for cyber physical production system

IF 4.4 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks Pub Date : 2025-03-20 DOI:10.1016/j.adhoc.2025.103831

Xiaoqian Yu , Changqing Xia , Xi Jin , Chi Xu , Dong Li , Peng Zeng

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引用次数: 0

Abstract

Edge computing plays a crucial role in cyber physical production system (CPPS) by connecting the cloud, thereby enhancing system flexibility, intelligence, and agility. However, current scholarly work predominantly focuses on the tight binding of tasks and platforms to meet the real-time and deterministic requirements of CPPS, but does not fully utilize the flexibility and customization characteristics of CPPS. Therefore, there is an urgent need for intelligent and flexible task platform decoupling in the future to meet various quality of service (QoS) requirements such as flexibility, energy consumption and real-time performance, which also brings the challenge of meeting CPPS requirements. To address this issue, this work studies the decoupled flexible manufacturing dynamical scheduling problem with the aim of jointly optimizing real-time performance and energy consumption. Firstly, a multi-priority feedback queue is designed, which can dynamic adjust priority to ensure real-time performance of tasks. Subsequently, multi-objective optimization models are used to allocate network and computing resources for tasks, taking system latency and energy consumption into account as costs. To narrow the solution space and improve solving speed, a locally optimal resource allocation method is derived. Furthermore, scheduling algorithms for the end-side and edge-side are designed separately. On one hand, considering the energy sensitivity of edge devices, a lightweight scheduling algorithm called terminal double deep Q network (T-DDQN) has been proposed to quickly determine the optimal task execution location. On the other hand, a task offloading strategy named game theory edge device-level task offloading (GTETO) has been introduced to address the load imbalance issues at the edge caused by T-DDQN. Compared to existing algorithms, it can reduce system cost by up to 25.26%, and improve resource utilization of edge devices by 8.34–27.77%.

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网络物理生产系统的综合网络计算资源分配与优化调度

边缘计算通过连接云，在网络物理生产系统（CPPS）中发挥至关重要的作用，从而增强系统的灵活性、智能性和敏捷性。然而，目前的学术工作主要集中在任务和平台的紧密绑定上，以满足CPPS的实时性和确定性要求，而没有充分利用CPPS的灵活性和定制化特点。因此，未来迫切需要智能灵活的任务平台解耦，以满足灵活性、能耗、实时性等各种服务质量（QoS）要求，这也带来了满足CPPS要求的挑战。为了解决这一问题，本文研究了解耦柔性制造动态调度问题，以实现实时性和能耗的共同优化。首先，设计了一个多优先级反馈队列，可以动态调整优先级，保证任务的实时性；然后，采用多目标优化模型为任务分配网络和计算资源，以系统延迟和能耗为代价。为了缩小求解空间，提高求解速度，导出了一种局部最优资源分配方法。在此基础上，分别设计了端侧和边侧的调度算法。一方面，考虑到边缘设备的能量敏感性，提出了一种轻量级调度算法——终端双深Q网络（T-DDQN），以快速确定任务的最优执行位置；另一方面，为了解决由T-DDQN引起的边缘负载不平衡问题，提出了一种名为博弈边缘设备级任务卸载（GTETO）的任务卸载策略。与现有算法相比，该算法可将系统成本降低25.26%，将边缘设备的资源利用率提高8.34-27.77%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Ad Hoc Networks 工程技术-电信学

CiteScore

10.20

自引率

4.20%

发文量

131

审稿时长

4.8 months

期刊介绍： The Ad Hoc Networks is an international and archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in ad hoc and sensor networking areas. The Ad Hoc Networks considers original, high quality and unpublished contributions addressing all aspects of ad hoc and sensor networks. Specific areas of interest include, but are not limited to: Mobile and Wireless Ad Hoc Networks Sensor Networks Wireless Local and Personal Area Networks Home Networks Ad Hoc Networks of Autonomous Intelligent Systems Novel Architectures for Ad Hoc and Sensor Networks Self-organizing Network Architectures and Protocols Transport Layer Protocols Routing protocols (unicast, multicast, geocast, etc.) Media Access Control Techniques Error Control Schemes Power-Aware, Low-Power and Energy-Efficient Designs Synchronization and Scheduling Issues Mobility Management Mobility-Tolerant Communication Protocols Location Tracking and Location-based Services Resource and Information Management Security and Fault-Tolerance Issues Hardware and Software Platforms, Systems, and Testbeds Experimental and Prototype Results Quality-of-Service Issues Cross-Layer Interactions Scalability Issues Performance Analysis and Simulation of Protocols.