Cooperative End-Edge-Cloud Computing and Resource Allocation for Digital Twin Enabled 6G Industrial IoT

IF 8.7 1区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Journal of Selected Topics in Signal Processing Pub Date : 2023-12-20 DOI:10.1109/JSTSP.2023.3345154
Yuao Wang;Jingjing Fang;Yao Cheng;Hao She;Yongan Guo;Gan Zheng
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Abstract

End-edge-cloud (EEC) collaborative computing is regarded as one of the most promising technologies for the Industrial Internet of Things (IIoT). It offers effective solutions for managing computationally intensive and delay-sensitive tasks efficiently. Indeed, achieving intelligent manufacturing in the context of 6G networks requires the development of efficient resource scheduling schemes. However, improving the quality of service and resource management in the face of challenges like time-varying physical operating environments of IIoT, task heterogeneity, and the coupling of different resource types is undoubtedly a complex task. In this work, we propose a digital twin (DT) assisted EEC collaborative computing scheme, where DT is utilized to monitor the physical operating environment in real-time and determine the optimal strategy, and the potential deviation between the real values and DT estimates is also considered. We aim to minimize the system cost by optimizing device association, offloading mode, bandwidth allocation, and task split ratio. Our optimization is constrained by the maximum tolerable latency of the task while considering both latency and energy consumption. To solve the collaborative computation and resource allocation (CCRA) problem in the EEC, we propose an algorithm with DT based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG), where each user end (UE) in DT operates as an independent agent to determine the optimum offloading decision autonomously. Simulation results demonstrate the effectiveness of the proposed scheme, which can significantly improve the task success rate compared to benchmark schemes, while reducing the latency and energy consumption of task offloading with the assistance of DT.
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面向支持数字孪生的 6G 工业物联网的端到端云协同计算和资源分配
端边云(EEC)协同计算被认为是工业物联网(IIoT)中最有前途的技术之一。它为高效管理计算密集型和延迟敏感型任务提供了有效的解决方案。事实上,在 6G 网络背景下实现智能制造需要开发高效的资源调度方案。然而,面对 IIoT 的时变物理运行环境、任务异构性和不同资源类型的耦合等挑战,提高服务质量和资源管理无疑是一项复杂的任务。在这项工作中,我们提出了一种数字孪生(DT)辅助 EEC 协同计算方案,利用 DT 实时监控物理运行环境并确定最优策略,同时还考虑了真实值与 DT 估计值之间的潜在偏差。我们的目标是通过优化设备关联、卸载模式、带宽分配和任务分割比例,最大限度地降低系统成本。我们的优化受限于任务的最大可容忍延迟,同时考虑延迟和能耗。为了解决 EEC 中的协同计算和资源分配(CCRA)问题,我们提出了一种基于多代理深度确定性策略梯度(MADDPG)的 DT 算法,其中 DT 中的每个用户端(UE)都作为独立代理自主决定最佳卸载决策。仿真结果证明了所提方案的有效性,与基准方案相比,它能显著提高任务成功率,同时在 DT 的协助下减少任务卸载的延迟和能耗。
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来源期刊
IEEE Journal of Selected Topics in Signal Processing
IEEE Journal of Selected Topics in Signal Processing 工程技术-工程:电子与电气
CiteScore
19.00
自引率
1.30%
发文量
135
审稿时长
3 months
期刊介绍: The IEEE Journal of Selected Topics in Signal Processing (JSTSP) focuses on the Field of Interest of the IEEE Signal Processing Society, which encompasses the theory and application of various signal processing techniques. These techniques include filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals using digital or analog devices. The term "signal" covers a wide range of data types, including audio, video, speech, image, communication, geophysical, sonar, radar, medical, musical, and others. The journal format allows for in-depth exploration of signal processing topics, enabling the Society to cover both established and emerging areas. This includes interdisciplinary fields such as biomedical engineering and language processing, as well as areas not traditionally associated with engineering.
期刊最新文献
Front Cover Table of Contents IEEE Signal Processing Society Information Introduction to the Special Issue Near-Field Signal Processing: Algorithms, Implementations and Applications IEEE Signal Processing Society Information
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