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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao IEEE/ACM Transaction...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
IEEE/ACM Transactions on Networking
Article . 2007 . Peer-reviewed
License: IEEE Copyright
Data sources: Crossref
https://doi.org/10.1109/infcom...
Article . 2004 . Peer-reviewed
Data sources: Crossref
DBLP
Conference object . 2017
Data sources: DBLP
DBLP
Article . 2016
Data sources: DBLP
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Maximizing throughput for optical burst switching networks

Authors: Jikai Li; Chunming Qiao; Jinhui Xu 0001; Dahai Xu;

Maximizing throughput for optical burst switching networks

Abstract

In optical burst switching (OBS) networks, a key problem is to schedule as many bursts as possible on wavelength channels so that the throughput is maximized and the burst loss is minimized. Most of the current research on OBS has been concentrated on reducing burst loss in an ldquoaverage-caserdquo sense, and little effort has been devoted to understanding the worst case performance. Since OBS itself is an open-loop control system, it may exhibit a worst case behavior when adversely synchronized. On the other hand, most commercial systems require an acceptable worst case performance. In this paper, we use competitive analysis to analyze the worst case performance of a large set of scheduling algorithms, called best-effort online scheduling algorithms, for OBS networks and establish a number of interesting upper and lower bounds on the performance of such algorithms. Our analysis shows that the performance of any best-effort online algorithm is closely related to a few factors, such as the range of offset time, maximum-to-minimum burst-length ratio, and the number of data channels. A surprising discovery is that the worst case performance of any best-effort online scheduling algorithm is primarily determined by the maximum-to-minimum burst-length ratio, followed by the range of offset time. Furthermore, if all bursts have the same burst length and offset time, all best-effort online scheduling algorithms generate the same optimal solution, regardless of how different they may look. Our analysis can also be extended to some non-best-effort online scheduling algorithms, such as the well-known Horizon algorithm, and establish similar bounds. Based on the analytic results, we give guidelines for several widely discussed OBS problems, including burst assembly, offset time setting, and scheduling algorithm design, and propose a new channel reservation protocol called virtual fixed offset-time (VFO) to improve the worst case performance. Our simulation shows that VFO can also reduce the average burst loss rate.

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
38
Average
Top 10%
Top 10%
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