Competitive Router Scheduling with Structured Data
We consider the task of transmitting structured information over bounded-capacity links. Our information model is a stream of basic units called superpackets that are broken into k packets each. To model the possible structure and redundancy of the superpackets, we assume that for each superpacket there is a collection of minimal subsets of packets whose delivery makes the superpacket useful. This very general model encompasses, for example, MPEG streams, where one can think of a group of pictures (GoP) as a superpacket. The fundamental difficulty is that networks can forward only the primitive packets, but applications can use only superpackets, and thus if no minimal subset is delivered, the whole superpacket becomes useless. Our aim is to maximize goodput (number of useful superpackets) in the face of overloaded communication links, where we are forced to drop some packets.
Specifically, we assume that an arbitrary stream of packets arrives at a router with multiple bounded-capacity outgoing links. An on-line algorithm needs to decide, for each superpacket, which outgoing link to use (all packets of the same superpacket must use the same link) and, in case of an overload at a link, which packets to drop and which to transmit so as to maximize goodput. We analyze a simple randomized competitive algorithm to the general case and provide a nearly matching lower bound on the competitive ratio of any randomized on-line algorithm.
KeywordsService Level Input Sequence Competitive Ratio Online Algorithm Outgoing Link
Unable to display preview. Download preview PDF.
- 1.International Organization for Standardization: MPEG-2 standard, ISO/IEC 13818-2:2000 (2000)Google Scholar
- 2.World Wide Web Consortium: Extensible markup language (XML) 1.0. W3C Recommendation (November 2008), http://www.w3.org/TR/REC-xml/
- 3.Emek, Y., Halldórsson, M.M., Mansour, Y., Patt-Shamir, B., Radhakrishnan, J., Rawitz, D.: Online set packing and competitive scheduling of multi-part tasks. In: 29th Annual ACM Symposium on Principles of Distributed Computing (2010)Google Scholar
- 4.Mansour, Y., Patt-Shamir, B., Rawitz, D.: Overflow management with multipart packets. In: IEEE INFOCOM (2011)Google Scholar
- 5.Mansour, Y., Patt-Shamir, B., Lapid, O.: Optimal smoothing schedules for real-time streams. In: 19th Annual ACM Symposium on Principles of Distributed Computing, pp. 21–30 (2000)Google Scholar
- 6.Kesselman, A., Lotker, Z., Mansour, Y., Patt-Shamir, B., Schieber, B., Sviridenko, M.: Buffer overflow management in QoS switches. In: 33rd Annual ACM Symposium on Theory of Computing, pp. 520–529 (2001)Google Scholar
- 8.Kesselman, A., Patt-Shamir, B., Scalosub, G.: Competitive buffer management with packet dependencies. In: 23rd IPDPS, pp. 1–12 (2009)Google Scholar
- 9.Alon, N., Spencer, J.H.: The Probabilistic Method. 3rd edn. Wiley Interscience (2008)Google Scholar
- 14.Hazan, E., Safra, S., Schwartz, O.: On the Complexity of Approximating k-Dimensional Matching. In: Arora, S., Jansen, K., Rolim, J.D.P., Sahai, A. (eds.) RANDOM 2003 and APPROX 2003. LNCS, vol. 2764, pp. 83–97. Springer, Heidelberg (2003)Google Scholar