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Seamless Scheduling for NFV-Enabled 5G-TSN Network: A Full-Path AoI Based Method

计算机科学 网络功能虚拟化 调度(生产过程) 路径(计算) 计算机网络 工程类 云计算 操作系统 运营管理
作者
Yajing Zhang,Qimin Xu,Cailian Chen,Mingyan Li,Xinping Guan,Tony Q. S. Quek
出处
期刊:IEEE Transactions on Industrial Informatics [Institute of Electrical and Electronics Engineers]
卷期号:20 (12): 13513-13525 被引量:1
标识
DOI:10.1109/tii.2024.3396299
摘要

Driven by the demand of Industry 4.0, the integration of 5G and time-sensitive networking (TSN) is proposed to provide ubiquitous connection and deterministic transmission. However, the heterogeneous access mechanisms and scheduling resolutions between 5G and TSN make it still intractable to schedule 5G and TSN resources jointly. To address this issue, we develop the network function virtualization-enabled 5G-TSN framework to offer unified resource management, where flows are scheduled by network slicing and virtual network function embedding, respectively. Specifically, a novel full-path age of information (FP-AoI) model is proposed as a new metric of the 5G-TSN integrated scheduling by innovatively encapsulating the 5G system as the sampling process of the virtual TSN network. To tackle the long latency tail brought by 5G, the 5G and TSN scheduling is formulated by a risk-aware FP-AoI minimization problem. Then, a decomposition and augmentation-based joint scheduling (DAS) algorithm is proposed to solve this NP-hard problem by decomposing it into three subproblems. The first two subproblems are proved to be convex. For the third subproblem, i.e., TSN scheduling, a FP-AoI-driven TSN scheduling scheme (FvQI) is designed by constructing an augmented logical topology according to constraints of service function chain and TSN characteristics. It realizes the TSN scheduling with low complexity. Simulation results demonstrate that our algorithms offer higher reliability, efficiency, and service acceptance ratio than benchmarks. Moreover, the DAS algorithm achieves a better tradeoff between the performance of time cost and AoI violation ratio with a small optimality gap.
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