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

{"title":"Integrated network-computing resource allocation and optimized scheduling for cyber physical production system","authors":"Xiaoqian Yu , Changqing Xia , Xi Jin , Chi Xu , Dong Li , Peng Zeng","doi":"10.1016/j.adhoc.2025.103831","DOIUrl":null,"url":null,"abstract":"<div><div>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%.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"173 ","pages":"Article 103831"},"PeriodicalIF":4.4000,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ad Hoc Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1570870525000794","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}

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%.

查看原文本刊更多论文

求助全文

约1分钟内获得全文求助全文

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.