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Design of a Hybrid Genetic Algorithm for Time-Sensitive Networking

  • Anna ArestovaEmail author
  • Kai-Steffen Jens Hielscher
  • Reinhard German
Conference paper
  • 88 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12040)

Abstract

With Time-Sensitive Networking (TSN), the IEEE 802.1 Task Group is extending the Ethernet standard by time-sensitive capabilities to establish a common ground for real-time communication systems via Ethernet. The Time-Sensitive Networking Task Group introduces a time-triggered transmission approach in IEEE 802.1Qbv to enable a deterministic transmission of time-critical network traffic, which requires scheduling strategies. Genetic algorithms are qualified to solve these scheduling problems in Time-Sensitive Networks. The difficulty is to design the genetic algorithm to find an optimal or a near-optimal solution for different complex problems taking performance and quality of the schedule into account. The complexity of schedules for TSN depends on the decision space of a network designer comprising the possibility to combine a variable number of network participants, a variable number of TSN flows, as well as assuming fixed or flexible routes for the flows. In this paper, we discuss a design approach for a hybrid genetic algorithm including chromosome representation for the routing and scheduling problems in TSN, the choice of genetic operators, and a neighborhood search to find a near-optimal solution. Additionally, we introduce an approach to compress the resulting schedules. Our evaluations show that the proposed hybrid genetic algorithm is able to compete with the well-adapted NEH algorithm in terms of schedule quality, and it outperforms the NEH algorithm regarding the computing time.

References

  1. 1.
    P802.1AS-Rev - Timing and Synchronization for Time-Sensitive Applications. https://1.ieee802.org/tsn/802-1as-rev/. Accessed 25 Oct 2019
  2. 2.
    Time-Sensitive Networking (TSN) Task Group. https://1.ieee802.org/tsn/. Accessed 25 Oct 2019
  3. 3.
    IEEE Standard for Local and metropolitan area networks- Timing and Synchronization for Time-Sensitive Applications in Bridged Local Area Networks. IEEE Std 802.1AS-2011 pp. 1–292, March 2011Google Scholar
  4. 4.
    IEEE Standard for Local and metropolitan area networks - Bridges and Bridged Networks - Amendment 25: Enhancements for Scheduled Traffic. IEEE Std 802.1Qbv-2015 (Amendment to IEEE Std 802.1Q-2014 as amended by IEEE Std 802.1Qca-2015, IEEE Std 802.1Qcd-2015, and IEEE Std 802.1Q-2014/Cor 1–2015) pp. 1–57, March 2016Google Scholar
  5. 5.
    Ak, B., Koc, E.: A guide for genetic algorithm based on parallel machine scheduling and flexible job-shop scheduling. Proc. - Soc. Behav. Sci. 62, 817–823 (2012). http://www.sciencedirect.com/science/article/pii/S1877042812035793. World Conference on Business, Economics and Management (BEM-2012), May 4–6 2012, Antalya, TurkeyCrossRefGoogle Scholar
  6. 6.
    Anand, E., Panneerselvam, R.: A study of crossover operators for genetic algorithm and proposal of a new crossover operator to solve open shop scheduling problem. Am. J. Ind. Bus. Manage. 06, 774–789 (2016)Google Scholar
  7. 7.
    Buttelmann, M., Lohmann, B.: Optimierung mit genetischen algorithmen und eine anwendung zur modellreduktion (optimization with genetic algorithms and an application for model reduction). At-automatisierungstechnik - AT-AUTOM 52, 151–163 (2004)CrossRefGoogle Scholar
  8. 8.
    Chen, H., Ihlow, J., Lehmann, C.: A genetic algorithm for flexible job-shop scheduling. In: Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C), vol. 2, pp. 1120–1125, May 1999Google Scholar
  9. 9.
    Craciunas, S.S., Oliver, R.S., Chmelík, M., Steiner, W.: Scheduling real-time communication in IEEE 802.1Qbv time sensitive networks. In: Proceedings of the 24th International Conference on Real-Time Networks and Systems, RTNS 2016, pp. 183–192. ACM, New York (2016)Google Scholar
  10. 10.
    Craciunas, S.S., Serna Oliver, R.: An overview of scheduling mechanisms for time-sensitive networks. In: Proceedings of the Real-time summer school L’École d’Été Temps Réel (ETR) (2017)Google Scholar
  11. 11.
    Davis, L.: Applying adaptive algorithms to epistatic domains. In: Proceedings of the 9th International Joint Conference on Artificial Intelligence, IJCAI 1985, vol. 1, pp. 162–164. Morgan Kaufmann Publishers Inc., San Francisco (1985). http://dl.acm.org/citation.cfm?id=1625135.1625164
  12. 12.
    De Jong, K.: An analysis of the behavior of a class of genetic adaptive systems (1975). https://books.google.de/books?id=4b9bNQcL6wMC
  13. 13.
    Demir, Y., İşleyen, S.K.: An effective genetic algorithm for flexible job-shop scheduling with overlapping in operations. Int. J. Prod. Res. 52(13), 3905–3921 (2014).  https://doi.org/10.1080/00207543.2014.889328CrossRefGoogle Scholar
  14. 14.
    Dürr, F., Nayak, N.G.: No-wait packet scheduling for IEEE time-sensitive networks (TSN). In: Proceedings of the 24th International Conference on Real-Time Networks and Systems, RTNS 2016, pp. 203–212. ACM, New York (2016).  https://doi.org/10.1145/2997465.2997494
  15. 15.
    Falkenauer, E., Bouffouix, S.: A genetic algorithm for job shop. In: Proceedings of the 1991 IEEE International Conference on Robotics and Automation, pp. 824–829 (1991)Google Scholar
  16. 16.
    Goldberg, D.E., Deb, K.: A comparative analysis of selection schemes used in genetic algorithms. In: Foundations of Genetic Algorithms, vol. 1, pp. 69–93. Elsevier (1991). http://www.sciencedirect.com/science/article/pii/B9780080506845500082
  17. 17.
    Goldberg, D.E., Lingle Jr., R.: Alleles, loci and the traveling salesman problem. In: Proceedings of the 1st International Conference on Genetic Algorithms, pp. 154–159. L. Erlbaum Associates Inc., Hillsdale (1985). http://dl.acm.org/citation.cfm?id=645511.657095
  18. 18.
    González Fernández, M.N., Vela, C., Arias, R.: A new hybrid genetic algorithm for the job shop scheduling problem with setup times, pp. 116–123, January 2008Google Scholar
  19. 19.
    Kopetz, H., Ademaj, A., Grillinger, P., Steinhammer, K.: The time-triggered ethernet (TTE) design. In: Eighth IEEE International Symposium on Object-Oriented Real-Time Distributed Computing (ISORC 2005), pp. 22–33, May 2005.  https://doi.org/10.1109/ISORC.2005.56
  20. 20.
    Lozano, M., Herrera, F., Cano, J.R.: Replacement strategies to preserve useful diversity in steady-state genetic algorithms. Inform. Sci. 178(23), 4421–4433 (2008). http://www.sciencedirect.com/science/article/pii/S0020025508002867. Including Special Section: Genetic and Evolutionary ComputingCrossRefGoogle Scholar
  21. 21.
    Moghadam, A.M., Wong, K.Y., Piroozfard, H.: An efficient genetic algorithm for flexible job-shop scheduling problem. In: 2014 IEEE International Conference on Industrial Engineering and Engineering Management, pp. 1409–1413 (2014)Google Scholar
  22. 22.
    Murata, T., Ishibuchi, H., Tanaka, H.: Genetic algorithms for flowshop scheduling problems. Comput. Ind. Eng. 30(4), 1061–1071 (1996). http://www.sciencedirect.com/science/article/pii/0360835296000538CrossRefGoogle Scholar
  23. 23.
    Nawaz, M., Enscore, E.E., Ham, I.: A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega 11(1), 91–95 (1983). EconPapers.repec.org/RePEc:eee:jomega:v:11:y:1983:i:1:p:91-95CrossRefGoogle Scholar
  24. 24.
    Nayak, N., Dürr, F., Rothermel, K.: Routing algorithms for IEEE802.1Qbv networks. ACM SIGBED Rev. 15, 13–18 (2017)CrossRefGoogle Scholar
  25. 25.
    Nie, L., Gao, L., Li, P., Li, X.: A GEP-based reactive scheduling policies constructing approach for dynamic flexible job shop scheduling problem with job release dates. J. Intell. Manuf. 24(4), 763–774 (2013).  https://doi.org/10.1007/s10845-012-0626-9CrossRefGoogle Scholar
  26. 26.
    Pahlevan, M., Obermaisser, R.: Genetic algorithm for scheduling time-triggered traffic in time-sensitive networks. In: 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA), vol. 1, pp. 337–344, September 2018Google Scholar
  27. 27.
    Potvin, J.Y.: Genetic algorithms for the traveling salesman problem. Ann. Oper. Res. 63(3), 337–370 (1996).  https://doi.org/10.1007/BF02125403CrossRefzbMATHGoogle Scholar
  28. 28.
    Reeves, C.R.: Genetic algorithms and neighbourhood search. In: Fogarty, T.C. (ed.) AISB EC 1994. LNCS, pp. 115–130. Springer, Heidelberg (1994).  https://doi.org/10.1007/3-540-58483-8_10CrossRefGoogle Scholar
  29. 29.
    Reeves, C.R.: A genetic algorithm for flowshop sequencing. Comput. Oper. Res. 22(1), 5–13 (1995). http://www.sciencedirect.com/science/article/pii/0305054893E0014K. Genetic AlgorithmsCrossRefGoogle Scholar
  30. 30.
    Ruiz, R., Maroto, C., Alcaraz, J.: Two new robust genetic algorithms for the flowshop scheduling problem. Omega 34, 461–476 (2006)CrossRefGoogle Scholar
  31. 31.
    Syswerda, G.: Schedule optimization using genetic algorithms (1991)Google Scholar
  32. 32.
    Wang, X., Gao, L., Zhang, C., Shao, X.: A multi-objective genetic algorithm based on immune and entropy principle for flexible job-shop scheduling problem. Int. J. Adv. Manuf. Technol. 51(5), 757–767 (2010).  https://doi.org/10.1007/s00170-010-2642-2CrossRefGoogle Scholar
  33. 33.
    Werner, F.: Genetic algorithms for shop scheduling problems: a survey. Preprint Ser. 11, 1–66 (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Anna Arestova
    • 1
    Email author
  • Kai-Steffen Jens Hielscher
    • 1
  • Reinhard German
    • 1
  1. 1.Department of Computer Science 7, Computer Networks and Communication SystemsUniversity of Erlangen-NürnbergErlangenGermany

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