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

IF 8.9 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Jiahui Zhai;Jing Bi;Haitao Yuan;Jia Zhang;Rajkumar Buyya
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引用次数: 0

摘要

工业物联网(IIoT)的空前繁荣,极大地推动了传统制造向智能制造的转变。在工业环境中,资源受限的工业设备往往不能满足大量计算密集型和延迟敏感型任务的多样化需求。移动边缘计算已经成为一种创新的范例,可以减少ie的延迟和能耗。然而,工业环境中越来越多的ie依赖于集成了不同处理单元(即cpu和gpu)的异构平台。为了应对这一挑战,我们提出了一种基于软件定义网络的设备边缘云架构,具有三阶段异构计算。该体系结构准确地模拟了真实工业环境中科学和并发工作流的多任务处理。我们提出了一个联合优化问题,以同时最小化任务完成时间和能量消耗。为了解决这个问题,我们设计了一种改进的两阶段多目标进化算法(IT-MOEA)。IT-MOEA采用了一种基于蝠鲼觅食和联想学习的新型多目标灰狼优化器来加速进化早期的收敛,并采用了一种增强多样性的免疫算法来增强进化后期的多样性。各种基准的仿真结果表明,IT-MOEA在延迟和能耗方面平均优于几种最先进的单目标优化算法24.7%,多目标优化算法41.0%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
IEEE Internet of Things Journal
IEEE Internet of Things Journal Computer Science-Information Systems
CiteScore
17.60
自引率
13.20%
发文量
1982
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.
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