Scheduling Multipacket Frames with Frame Deadlines
We consider scheduling information units called frames, each with a delivery deadline. Frames consist of packets, which arrive on-line in a roughly-periodic fashion, and compete on allocation of transmission slots. A frame is deemed useful only if all its packets are delivered before its deadline. Using standard techniques, one can derive polylog-competitive algorithms for this model; in this paper we study special cases which allow for better results. Specifically, we present constant-competitive algorithms for two important cases: in one, the value of a frame is proportional to its size and all frames have (roughly) the same period, and in the other, each frame may have its own period but all frames have the same value and size. The former result also implies better polylog-competitive algorithm for the general case.
KeywordsTime Slot Competitive Ratio Online Algorithm Earliest Deadline First Active Frame
Unable to display preview. Download preview PDF.
- 1.Awerbuch, B., Bartal, Y., Fiat, A., Rosén, A.: Competitive non-preemptive call control. In: Proc. of the 5th Annual ACM-SIAM Symp. on Discrete Algorithms (SODA), pp. 312–320 (1994)Google Scholar
- 2.Dürr, C., Jeż, Ł., Thang, N.K.: Online scheduling of bounded length jobs to maximize throughput. J. Scheduling 15(5), 653–664 (2012). Also appeared in Proc. of the 7th Workshop on Approx. and Online Algorithms (WAOA), pp. 116–127 (2009)Google Scholar
- 3.Emek, Y., Halldórsson, M.M., Mansour, Y., Patt-Shamir, B., Radhakrishnan, J., Rawitz, D.: Online set packing. SIAM J. Comput 41(4), 728–746 (2010). Also appeared in Proc. of the 29th ACM Symp. on Principles of Distributed Comput. (PODC), pp. 440–449 (2010)Google Scholar
- 4.Epstein, L., Jeż, Ł., Sgall, J., van Stee, R.: Online Scheduling of Jobs with fixed start times on related machines. In: Gupta, A., Jansen, K., Rolim, J., Servedio, R. (eds.) APPROX/RANDOM 2012. LNCS, vol. 7408, pp. 134–145. Springer, Heidelberg (2012), To appear in Algorithmica: http://dx.doi.org/10.1007/s00453-014-9940-2
- 5.Kalyanasundaram, B., Pruhs, K.: Speed is as powerful as clairvoyance. J. ACM 47(4), 617–643 (2000). Also appeared in Proc. of the 36th Symp. on Foundations of Comp. Sci (FOCS), pp. 214–221 (1995)Google Scholar
- 6.Kalyanasundaram, B., Pruhs, K.: Maximizing job completions online. J. Algorithms 49(1), 63–85 (1998). Also appeared in Proc. of the 6th European Symp. on Algorithms (ESA), pp. 235–246 (1998)Google Scholar
- 7.Kesselman, A., Patt-Shamir, B., Scalosub, G.: Competitive buffer management with packet dependencies. Theor. Comput. Sci. 489-489, 75–87 (2013). Also appeared in 23rd IEEE Int. Parallel and Distributed Processing Symp. (IPDPS), pp. 1–12 (2009)Google Scholar
- 8.Mansour, Y., Patt-Shamir, B., Rawitz, D.: Overflow management with multipart packets. Computer Networks 56(15), 3456–3467 (2011). Also appeared in Proc. of the 30th IEEE Int. Conf. on Computer Communications (INFOCOM), pp. 2606–2614 (2011)Google Scholar
- 9.Markovitch, M., Scalosub, G.: Bounded delay scheduling with packet dependencies. In: Proc. of the IEEE INFOCOM Workshops, pp. 257–262 (2014)Google Scholar
- 10.Scalosub, G., Marbach, P., Liebeherr, J.: Buffer management for aggregated streaming data with packet dependencies. IEEE Trans. Parallel Distrib. Syst. 24(3), 439–449 (2010). Also appeared in Proc. of the 29th IEEE Int. Conf. on Computer Communications (INFOCOM), pp. 241–245 (2010)Google Scholar