NDLI: Flow-Level Stability of Channel-Aware Scheduling Algorithms

Content Provider	IEEE Xplore Digital Library
Author	Borst, S. Jonckheere, M.
Copyright Year	2006
Description	Author affiliation: Bell Laboratories, Lucent Technologies, P.O. Box 636, Murray Hill, NJ 07974-0636, USA; CWI, P.O. Box 94079, 1090 GB Amsterdam, The Netherlands; Department of Mathematics & Computer Science, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands (Borst, S.)
Abstract	Channel-aware scheduling strategies provide an effective mechanism for improving the throughput performance in wireless data networks by exploiting channel fluctuations. The performance of channel-aware scheduling algorithms has mainly been examined at the packet level for a static user population, often assuming infinite backlogs. Recently, some studies have also explored the flow-level performance in a scenario with user dynamics governed by the arrival and completion of random service demands over time. Although in certain cases the performance may be evaluated by means of a Processor-Sharing model, in general the flow-level behavior has remained largely intractable, even basic stability properties. In the present paper we derive simple necessary stability conditions, and show that these are also sufficient for a wide class of utility-based scheduling policies. This contrasts with the fact that the latter class of strategies generally fail to provide maximum-throughput guarantees at the packet level.
Starting Page	1
Ending Page	6
File Size	217624
Page Count	6
File Format	PDF
ISBN	0780395492
DOI	10.1109/WIOPT.2006.1666471
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-02-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Fluctuations Stability Processor scheduling Telecommunication traffic Traffic control Throughput Mathematics Multiaccess communication Scheduling algorithm
Content Type	Text
Resource Type	Article

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