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Reduced state fair queuing for edge and core routers
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Source International Workshop on Network and Operating System Support for Digital Audio and Video archive
Proceedings of the 14th international workshop on Network and operating systems support for digital audio and video table of contents
Cork, Ireland
SESSION: Congestion control & scheduling table of contents
Pages: 100 - 105  
Year of Publication: 2004
ISBN:1-58113-801-6
Authors
Ramana Rao Kompella  University of California, San Diego, CA
George Varghese  University of California, San Diego, CA
Sponsors
SIGMULTIMEDIA: ACM Special Interest Group on Multimedia
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Despite many years of research, fair queuing still faces a number of implementation challenges in high speed routers. In particular, in spite of proposals such as DiffServ, the state needs for even simple schedulers are still large for heavily channelized core routers and for edge routers. An earlier proposal, Stochastic Fair Queuing, reduces state but at the expense of added unfairness between certain flows. Another earlier scheme, Core Stateless Fair Queuing, requires header changes and does not address the state needs of edge routers. By contrast, our paper proposes a randomization technique that removes the need to store deficit counters per flow in Deficit Round Robin and its variants. Even without the counters, we show, using both analysis and simulation, that randomized technique preserves throughput fairness properties of DRR. This randomization technique introduced in this paper can be used to considerably reduce the state requirements of high speed schedulers in edge and core routers, making hardware designs feasible. The randomization idea in this paper can also be applied to other round robin schedulers as well as potentially in entirely different scenarios wherever deficits need to be tracked over time explicitly.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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Collaborative Colleagues:
Ramana Rao Kompella: colleagues
George Varghese: colleagues