Online Packet Scheduling Under Adversarial Jamming

  • Tomasz JurdzinskiEmail author
  • Dariusz R. Kowalski
  • Krzysztof Lorys
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8952)


We consider the problem of scheduling packets of different lengths via a directed communication link prone to jamming errors. Dynamic packet arrivals and errors are modelled by an adversary. We focus on estimating competitive throughput of online scheduling algorithms. We design an online algorithm for scheduling packets of arbitrary lengths, achieving optimal competitive throughput in \((1/3,1/2]\) (the exact value depends on packet lengths). Another algorithm we design makes use of additional resources in order to achieve competitive throughput \(1\), that is, it achieves at least as high throughput as the best schedule without such resources, for any arrival and jamming patterns. More precisely, we show that if the algorithm can run with double speed, i.e., with twice higher frequency, then its competitive throughput is \(1\). This demonstrates that throughput of the best online fault-tolerant scheduling algorithms scales well with resource augmentation. Finally, we generalize the first of our algorithms to the case of any \(f\ge 1\) channels and obtain competitive throughput \(1/2\) in this setting in case packets lengths are pairwise divisible (i.e., any larger is divisible by any smaller).


Packet scheduling Adversarial jamming Online algorithms Competitive throughput Resource augmentation 


  1. 1.
    Ajtai, M., Aspnes, J., Dwork, C., Waarts, O.: A theory of competitive analysis for distributed algorithms. In: Proceedings of the FOCS, pp. 401–411 (1994)Google Scholar
  2. 2.
    Anantharamu, L., Chlebus, B.S., Kowalski, D.R., Rokicki, M.A.: Online parallel scheduling of non-uniform tasks: trading failures for energy. In: Proceedings of the INFOCOM, pp. 146–150 (2010)Google Scholar
  3. 3.
    Andrews, M., Zhang, L.: Scheduling over a time-varying user-dependent channel with applications to high-speed wireless data. J. ACM 52(5), 809–834 (2005)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Anta, A.F., Georgiou, C., Kowalski, D.R., Widmer, J., Zavou, E.: Measuring the impact of adversarial errors on packet scheduling strategies. In: Moscibroda, T., Rescigno, A.A. (eds.) SIROCCO 2013. LNCS, vol. 8179, pp. 261–273. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  5. 5.
    Anta, A.F., Georgiou, C., Kowalski, D.R., Zavou, E.: Online parallel scheduling of non-uniform tasks: trading failures for energy. In: Gasieniec, L., Wolter, F. (eds.) FCT 2013. LNCS, vol. 8070, pp. 145–158. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  6. 6.
    Anta, A.F., Georgiou, C., Kowalski, D.R., Zavou, E.: Asymptotic Competitive Analysis of Task Scheduling Algorithms on Fault-prone Machines. Manuscript (2014)Google Scholar
  7. 7.
    Awerbuch, B., Kutten, S., Peleg, D.: Competitive distributed job scheduling. In: Proceedings of the STOC, pp. 571–580 (1992)Google Scholar
  8. 8.
    Jurdzinski, T., Kowalski, D.R., Lorys, K.: Online packet scheduling under adversarial jamming. CoRR (2013)Google Scholar
  9. 9.
    Meiners, C.R., Torng, E.: Mixed criteria packet scheduling. In: Kao, M.-Y., Li, X.-Y. (eds.) AAIM 2007. LNCS, vol. 4508, pp. 120–133. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  10. 10.
    Pinedo, M.L.: Scheduling: Theory, Algorithms, and Systems. Springer, Berlin (2012)CrossRefGoogle Scholar
  11. 11.
    Pruhs, K., Sgall, J., Torng, E.: Online Scheduling, pp. 115–124. CRC Press, Boca Raton (2003) Google Scholar
  12. 12.
    Richa, A., Scheideler, C., Schmid, S., Zhang, J.: Competitive throughput in multi-hop wireless networks despite adaptive jamming. In: Distributed Computing, pp. 1–13 (2012)Google Scholar
  13. 13.
    Sleator, D.D., Tarjan, R.E.: Amortized efficiency of list update and paging rules. Commun. ACM 28(2), 202–208 (1985)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Tomasz Jurdzinski
    • 1
    Email author
  • Dariusz R. Kowalski
    • 2
  • Krzysztof Lorys
    • 1
  1. 1.Institute of Computer ScienceUniversity of WrocławWrocławPoland
  2. 2.Department of Computer ScienceUniversity of LiverpoolLiverpoolUK

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