Computer Science ›› 2024, Vol. 51 ›› Issue (11A): 240300085-7.doi: 10.11896/jsjkx.240300085

• Network & Communication • Previous Articles     Next Articles

Study on Dynamic Redundancy Mechanism of Time Sensitive Networks Based on Segmented Frame Copy and Elimination

ZHANG Hao1, GUO Oufan2, ZHOU Feifei2, MA Tao2, HE Yingli2, YAO Subin3   

  1. 1 State Grid Shandong Electric Power Company,Jinan 250001,China
    2 State Grid Electric Power Research Institute,Nari Group Co.,Ltd.,Nanjing 211106,China
    3 School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
  • Online:2024-11-16 Published:2024-11-13
  • About author:ZHANG Hao,born in 1985,senior engineer.His main research interests include power communication networks and systems,time-sensitive networking technology and so on.
    ZHOU Feifei,born in 1986,maser,se-nior engineer.His main research intere-sts include new types of power communication systems,FlexE flexible Ethernet technology,and time-sensitive networking technology.
  • Supported by:
    “Development and Application of Power Time Sensitive Network Switching Chips” project supported by the Technology Institute of State Grid Corporation of China(5108-202218280A-2-170-XG).

Abstract: To address the issue of how to achieve reliable transmission of replicated frames in the IEEE 802.1CB protocol for time sensitive networks,as well as the waste of network resources caused by end-to-end reliability protection,this paper proposes a dynamic redundancy mechanism for time sensitive networks based on segmented frame replication and elimination.This mechanism utilizes a reliability probability model to deploy different redundant paths for each data flow based on its priority,and utilizes the idea of segmented protection to compress network redundancy,effectively compressing network redundancy while ensuring high reliability of data transmission.This algorithm first filters the client stream based on its priority,ensuring redundancy only for data streams with priority greater than or equal to 4.Secondly,genetic algorithm is used to calculate the optimal main path between the source node and the destination node for data transmission,and the reliability probability model is used to determine whether the expected reliability has been achieved.If not,segmented frame replication and elimination methods will be used to confirm redundant paths and the number of FERE-NODES that need to be deployed.Finally,through continuous iteration and updating,the optimal solution for deploying FERE-CODE and the optimal redundant path strategy are obtained.Through simulation experiments on the NeSTiNg platform,the results show that compared with the shortest path algorithm and the minimum cost algorithm based on Lagrangian relaxation delay constraint(DCLC),the proposed redundant algorithm reduces packet loss rates by 0.15% and 0.23% respectively,and reduces average latency by 9.33% and 7.35% respectively.In comparison with two end-to-end redundancy mechanisms,ETE-FRER and ONE-FRER,the proposed redundancy algorithm reduces bandwidth consumption by 35.0% and 12.4% respectively under the 99.999% reliability requirement,fully verifying that this algorithm can effectively reduce network redundancy consumption while ensuring high network reliability.

Key words: Time sensitive network, Reliability, Frame replication and elimination, Dynamic redundancy

CLC Number: 

  • TP393
[1]SEOL Y,HYEON D,MIN J,et al.Timely Survey of Time-Sensitive Networking:Past and Future Directions[J].IEEE Access,2021,9:142506-142527.
[2]ZHANG L,WANG P P.Survey of traffic shaping and scheduling in time-sensitive network[J].Microelectronics & Compu-ter,2022,39(1):46-53.
[3]BELLO L L,STEINER W.A perspective on IEEE time-sensitivenetworking for industrial communication and automation systems[J].Proceedings of the IEEE,2019,107(6):1094-1120.
[4]FARZANEH M H,KNOLL A.An ontology-based Plug-and-Play approach for in-vehicle Time-Sensitive Networking(TSN)[C]//Information Technology,Electronics & Mobile Communication Conference.IEEE,2016.
[5]LI Z H,YANG S Q,YU J H,et al.State-of-the-art Survey on Deterministic Transmission Technologies in Time-sensitive Networking[J].Journal of Software,2022,33(11):4334-4355.
[6]ASHJAEI,MOHAMMAD,et al.Time-Sensitive Networking in automotive embedded systems:State of the art and research opportunities[J].Journal of Systems Architecture,2021(117):102137.
[7]DENG L B,XIE G Q,LIU H,et al.A Survey of Real-TimeEthernet Modeling and Design Methodologies:From AVB to TSN[J].ACM Computing Surveys(CSUR),2023,55(2):31.1-31.36.
[8]CAI Y P,YAO Z C,LI T C.A survey on time-sensitive networ-king:standards and state-of-the-art[J].Chinese Journal of Computers,2021,44(7):1378-1397.
[9]SONG H Z.Summary on time sensitive network technology[J].Process Automation Instrumentation,2020,41(2):1-9.
[10]ZHU J Y,DUAN S H,ZHANG H S,et al.Analysis on the application necessity of time-sensitive network technology in the field of industrial interconnection[J].Telecommunications Science,2020,36(5):115-124.
[11]STANTON K B.Distributing deterministic,accurate time fortightly coordinated network and software applications:IEEE 802.1as,the TSN profile of PTP[J].IEEE Communications Standards Magazine,2018,2(2):34-40.
[12]ZHANG T,FENG J Q,MA Y Y,et al.Survey on traffic scheduling in time-sensitive networking[J].Journal of Computer Research and Development,2022,59(4):747-764.
[13]TIAN S.Research and design of time-sensitive network system configuration scheme[D].Beijing:Beijing University of Posts and Telecommunications,2021.
[14]IEEE.IEEE standard for local and metropolitan area networks:frame replication and elimination for reliability:IEEEStd 802.1 CB-2017[S].2017.
[15]OJEWALE M A,YOMSI P M.Routing heuristics for load-ba-lanced transmission in TSN-based networks[J].ACM Sigbed Review,2020,16(4):20-25.
[16]SMIRNOV F,REIMANN F,JÜRGEN T,et al.Automatic optimization of redundant message routings in automotive networks[C]//Proceedings of the 21st International Workshop on Software and Compilers for Embedded Systems.2018.
[17]KIRRMANN H,HANSSON M,MURI P.Iec 62439 prp:Bumpless recovery for highly available,hard real-time industrial networks[C]//2007 IEEE Conference on Emerging Technologies and Factory Automation(EFTA 2007).IEEE,2007:1396-1399.
[18]KIRRMANN H,WEBER K,KLEINEBERG O,et al.Seamless and low-cost redundancy for substation automation systems(high availability seamless redundancy,HSR)[C]//2011 IEEE Power and Energy Society General Meeting,IEEE,2011.
[19]GIORGETTI A,CUGINI F,PAOLUCCI F,et al.Performanceanalysis of media redundancy protocol(MRP)[J].IEEE Tran-sactions on Industrial Informatics,2012,9(1):218-227.
[20]IEEE.IEEE P802.1Qbu/03.0 EEE draft standard for local and metropolitan area networks media access control(MAC) bridges and virtual bridged local area networks amendment:frame preemption[S].IEE,2015.
[21]YAO Z C,CAI Y P,LI T C.Multiple cascaded preconfigured cycles for the FRER mechanism in time-sensitive networking[C]//Proceedings of 2021 IEEE International Conference on Communications Workshops(ICC Workshops).Piscataway:IEEE Press,2021:1-6.
[22]ERGENÇ D,FISCHER M.On the reliability of IEEE 802.1CB FRER[C]//Proceedings of IEEE INFOCOM 2021-IEEE Conference on Computer Communications.Piscataway:IEEE Press,2021:1-10.
[23]DESAI D,PUNNEKKAT S.Enhancing Fault Detection in Time Sensitive Networks Using Machine Learning[C]//2020 International Conference on Communication Systems & Networks(COMSNETS).Bengaluru,India:IEEE Press,2020:714-719.
[24]WU K,ZHANG J,JI Y.Redundant Routing Provision in aFlexE-over-WDM Network based on Segment Frame Replication and Elimination[C]//2021 17th International Conference on the Design of Reliable Communication Networks(DRCN).Milan:IEEE Press,2021.
[25]FALK J,HELLMANNS D,CARABELLI B,et al.NeSTiNg:simulating IEEE time-sensitive networking(TSN) in OMNeT++[C]//Proceedings of 2019 International Conference on Networked Systems(NetSys).Piscataway:IEEE Press,2019:1-8.
[26]GUO A,ZHAO C,XU F,et al.LEO satellite routing algorithm in software defined space terrestrial integrated network[C]//2017 17th International Symposium on Communiactions and Information Technologies(ISCIT).Cairns,Australia:IEEE,2017:1-6.
[27]HE T,WANG S P,ZHANG.A QoS routing algorithm based on Lagrange relaxation method [J].Journal of Circuits and Systems,2010(1):4.
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