摘要
Deterministic transmission plays a vital role in industrial networks.The time-sensitive network(TSN)protocol family offers a promising paradigm for transmitting time-critical data.To achieve low latency and high Quality of Service(QoS)in TSN,appropriate data flow scheduling is needed under the given network topology and data flow requirements to fully utilize the potential of TSN.Both time-triggered flows and sporadic flows can carry high-priority data and need to be considered jointly to eliminate the effects of each other.To this end,in this work,we investigate the challenging mixed-flow scheduling problem and propose a novel diffusion-based algorithm,DiffTSN,to solve the joint routing and scheduling problem of mixed flows.We transform the sporadic flows into probabilistic flows and design certain mechanisms to fit the nature of these probabilistic flows.For routing,we transform the problem into a diffusion policy and constraint denoising process with a value guide to achieve a better routing policy.For scheduling,we adopt a first-valid-time-slot algorithm to determine the start transmission time of the flows.We train and evaluate DiffTSN in our TSN simulator.Experiments show that DiffTSN outperforms state-of-the-art algorithms in various metrics.
基金
supported by the Guangdong Science and Technology Program under Grant Nos.2024B0101040007 and 2024B0101020004
the Guangdong Basic and Applied Basic Research Foundation under Grant No.2023B1515120058
the National Science Foundation of China under Grant No.62172455
the Guangdong Science and Technology Department Pearl River Talent Program under Grant No.2019QN01X140
the Guangzhou Basic and Applied Basic Research Program under Grant No.2024A04J6367
the Fundamental Research Funds for the Central Universities of China under Grant No.24qnpy138
the Program for Guangdong Introducing Innovative and Entrepreneurial Teams under Grant No.2017ZT07X355
the Department of Science and Technology of Guangdong Province of China under Grant No.2022A0505050028.
