期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 18, 期 7, 页码 4933-4943出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2021.3137349
关键词
Processor scheduling; Computational modeling; Servers; Scheduling; Job shop scheduling; Energy consumption; Delays; 5G edge computing; digital twin (DT); distribution grid; federated learning (FL); security and latency awareness
类别
资金
- Science and Technology Project of State Grid Corporation of China [KJ21-1-56, TII-21-3287]
Digital twin (DT) provides accurate guidance for multidimensional resource scheduling in 5G edge computing-empowered distribution grids by establishing a digital representation of physical entities. This article addresses the critical challenges of DT construction and DT-assisted resource scheduling, proposing a federated learning-based DT framework and a Secure and lAtency-aware dIgital twin assisted resource scheduliNg algoriThm (SAINT) to achieve low-latency, accurate, and secure DT. SAINT supports intelligent resource scheduling by improving the learning performance of deep Q-learning and considers access priority and energy consumption awareness.
Digital twin (DT) provides accurate guidance for multidimensional resource scheduling in 5G edge computing-empowered distribution grids by establishing a digital representation of the physical entities. In this article, we address the critical challenges of DT construction and DT-assisted resource scheduling such as low accuracy, large iteration delay, and security threats. We propose a federated learning-based DT framework and present a Secure and lAtency-aware dIgital twin assisted resource scheduliNg algoriThm (SAINT). SAINT achieves low-latency, accurate, and secure DT by jointly optimizing its total iteration delay and loss function, and leveraging abnormal model recognition (AMR). SAINT enables intelligent resource scheduling by using DT to improve the learning performance of deep Q-learning. SAINT supports access priority and energy consumption awareness due to the consideration of long-term constraints. Compared with state-of-the-art algorithms, SAINT has superior performance in cumulative iteration delay, DT loss function, energy consumption, and access priority deficit.
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