Affiliation:
1. School of Communication and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710061, China
2. State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China
Abstract
Time-sensitive networking (TSN) is considered an ideal solution to meet the transmission needs of existing industrial production methods. The traffic scheduling problem of TSN is an NP-hard problem. The traditional traffic scheduling algorithms can lead to issues such as significant computational time consumption and traffic congestion, which are not conducive to the rapid and high-quality deployment of TSN. To simplify the complexity of TSN schedule table generation, the paper studies the scheduling problem of mixed critical traffic in TSN. Using a combinatorial packing traffic scheduling algorithm based on unoccupied space (CPTSA-US), a scheduling table for TSN traffic transmission is generated, proving the feasibility of transforming the TSN traffic scheduling problem into a packing problem. In addition, the initial packing algorithm and traditional traffic scheduling algorithm can cause traffic accumulation, seriously affecting network performance. This paper proposes a mixed-critical traffic scheduling algorithm based on free time domain (MCTSA-FTD), which further partitions the packing space transformed by the time domain. And performs multiple packing of traffic based on the partitioned packing space and generates the TSN schedule table according to the reverse transformation of the packing results. The simulation results show that compared to the CPTSA-US and the traditional traffic scheduling solution algorithm SMT (Satisfiability Modulo Theory), the schedule table generated by the MCTSA-FTD significantly improves the delay, jitter, and packet loss of BE flows in the network while ensuring the transmission requirements of TT flows. This can effectively enhance the transmission performance of the network.
Funder
National Natural Science Foundation of China
Natural Science Basic Research Program of Shaanxi Province
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