{"title":"使用异构 CPU 和 GPU 的工业云边缘系统的高能效和延迟感知任务卸载","authors":"Jiahui Zhai;Jing Bi;Haitao Yuan;Jia Zhang;Rajkumar Buyya","doi":"10.1109/JIOT.2025.3559690","DOIUrl":null,"url":null,"abstract":"The unprecedented prosperity of the Industrial Internet of Things (IIoT) has significantly driven the transition from traditional manufacturing to intelligent one. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm to reduce latency and energy consumption for IEs. However, the increasing number of IEs in industrial settings relies on heterogeneous platforms integrated with different processing units, i.e., CPUs and GPUs. To address this challenge, we propose a software-defined networking-based equipment-edge-cloud architecture with three-stage heterogeneous computing. This architecture accurately models the multitask processing of both scientific and concurrent workflows in real industrial environments. We formulate a joint optimization problem to simultaneously minimize task completion time and energy consumption for IEs. To solve this problem, we design an improved two-stage multiobjective evolutionary algorithm (IT-MOEA). IT-MOEA employs a novel multiobjective grey wolf optimizer based on manta ray foraging and associative learning to accelerate convergence in the early evolution stages and adopts a diversity-enhancing immune algorithm to enhance diversity in the later stages. Simulation results with various benchmarks demonstrate that IT-MOEA outperforms several state-of-the-art single-objective optimization algorithms by an average of 24.7% and multiobjective algorithms by 41.0% in terms of delay and energy consumption.","PeriodicalId":54347,"journal":{"name":"IEEE Internet of Things Journal","volume":"12 13","pages":"25757-25772"},"PeriodicalIF":8.9000,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Energy-Efficient and Latency-Aware Task Offloading for Industrial Cloud-Edge Systems With Heterogeneous CPUs and GPUs\",\"authors\":\"Jiahui Zhai;Jing Bi;Haitao Yuan;Jia Zhang;Rajkumar Buyya\",\"doi\":\"10.1109/JIOT.2025.3559690\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The unprecedented prosperity of the Industrial Internet of Things (IIoT) has significantly driven the transition from traditional manufacturing to intelligent one. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm to reduce latency and energy consumption for IEs. However, the increasing number of IEs in industrial settings relies on heterogeneous platforms integrated with different processing units, i.e., CPUs and GPUs. To address this challenge, we propose a software-defined networking-based equipment-edge-cloud architecture with three-stage heterogeneous computing. This architecture accurately models the multitask processing of both scientific and concurrent workflows in real industrial environments. We formulate a joint optimization problem to simultaneously minimize task completion time and energy consumption for IEs. To solve this problem, we design an improved two-stage multiobjective evolutionary algorithm (IT-MOEA). IT-MOEA employs a novel multiobjective grey wolf optimizer based on manta ray foraging and associative learning to accelerate convergence in the early evolution stages and adopts a diversity-enhancing immune algorithm to enhance diversity in the later stages. Simulation results with various benchmarks demonstrate that IT-MOEA outperforms several state-of-the-art single-objective optimization algorithms by an average of 24.7% and multiobjective algorithms by 41.0% in terms of delay and energy consumption.\",\"PeriodicalId\":54347,\"journal\":{\"name\":\"IEEE Internet of Things Journal\",\"volume\":\"12 13\",\"pages\":\"25757-25772\"},\"PeriodicalIF\":8.9000,\"publicationDate\":\"2025-04-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Internet of Things Journal\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10960708/\",\"RegionNum\":1,\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Internet of Things Journal","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10960708/","RegionNum":1,"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}
Energy-Efficient and Latency-Aware Task Offloading for Industrial Cloud-Edge Systems With Heterogeneous CPUs and GPUs
The unprecedented prosperity of the Industrial Internet of Things (IIoT) has significantly driven the transition from traditional manufacturing to intelligent one. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm to reduce latency and energy consumption for IEs. However, the increasing number of IEs in industrial settings relies on heterogeneous platforms integrated with different processing units, i.e., CPUs and GPUs. To address this challenge, we propose a software-defined networking-based equipment-edge-cloud architecture with three-stage heterogeneous computing. This architecture accurately models the multitask processing of both scientific and concurrent workflows in real industrial environments. We formulate a joint optimization problem to simultaneously minimize task completion time and energy consumption for IEs. To solve this problem, we design an improved two-stage multiobjective evolutionary algorithm (IT-MOEA). IT-MOEA employs a novel multiobjective grey wolf optimizer based on manta ray foraging and associative learning to accelerate convergence in the early evolution stages and adopts a diversity-enhancing immune algorithm to enhance diversity in the later stages. Simulation results with various benchmarks demonstrate that IT-MOEA outperforms several state-of-the-art single-objective optimization algorithms by an average of 24.7% and multiobjective algorithms by 41.0% in terms of delay and energy consumption.
期刊介绍:
The EEE Internet of Things (IoT) Journal publishes articles and review articles covering various aspects of IoT, including IoT system architecture, IoT enabling technologies, IoT communication and networking protocols such as network coding, and IoT services and applications. Topics encompass IoT's impacts on sensor technologies, big data management, and future internet design for applications like smart cities and smart homes. Fields of interest include IoT architecture such as things-centric, data-centric, service-oriented IoT architecture; IoT enabling technologies and systematic integration such as sensor technologies, big sensor data management, and future Internet design for IoT; IoT services, applications, and test-beds such as IoT service middleware, IoT application programming interface (API), IoT application design, and IoT trials/experiments; IoT standardization activities and technology development in different standard development organizations (SDO) such as IEEE, IETF, ITU, 3GPP, ETSI, etc.