The window distribution of multiple TCPs with random loss queues
Two approximate techniques for analyzing the window size distribution of TCP flows sharing a RED-like bottleneck queue are presented. Both methods presented first use a fixed point algorithm to obtain the mean window sizes of the flows, and the mean queue length in the bottleneck buffer. The simpler...
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1999
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sg-smu-ink.sis_research-17202019-02-19T07:22:08Z The window distribution of multiple TCPs with random loss queues MISRA, Archan OTT, Teunis BARAS, John Two approximate techniques for analyzing the window size distribution of TCP flows sharing a RED-like bottleneck queue are presented. Both methods presented first use a fixed point algorithm to obtain the mean window sizes of the flows, and the mean queue length in the bottleneck buffer. The simpler of the two methods then uses the ‘square root formula’ for TCP; the other method is more complicated. More often than not, the simpler method is slightly more accurate; this is probably due to the fact that window sizes of the different flows are negatively correlated. 1999-12-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/721 info:doi/10.1109/GLOCOM.1999.832455 https://ink.library.smu.edu.sg/context/sis_research/article/1720/viewcontent/WindowDistnMultipleTCP_Globecom99.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University TCP multiple distribution RED queues Software Engineering |
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TCP multiple distribution RED queues Software Engineering |
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TCP multiple distribution RED queues Software Engineering MISRA, Archan OTT, Teunis BARAS, John The window distribution of multiple TCPs with random loss queues |
| description |
Two approximate techniques for analyzing the window size distribution of TCP flows sharing a RED-like bottleneck queue are presented. Both methods presented first use a fixed point algorithm to obtain the mean window sizes of the flows, and the mean queue length in the bottleneck buffer. The simpler of the two methods then uses the ‘square root formula’ for TCP; the other method is more complicated. More often than not, the simpler method is slightly more accurate; this is probably due to the fact that window sizes of the different flows are negatively correlated. |
| format |
text |
| author |
MISRA, Archan OTT, Teunis BARAS, John |
| author_facet |
MISRA, Archan OTT, Teunis BARAS, John |
| author_sort |
MISRA, Archan |
| title |
The window distribution of multiple TCPs with random loss queues |
| title_short |
The window distribution of multiple TCPs with random loss queues |
| title_full |
The window distribution of multiple TCPs with random loss queues |
| title_fullStr |
The window distribution of multiple TCPs with random loss queues |
| title_full_unstemmed |
The window distribution of multiple TCPs with random loss queues |
| title_sort |
window distribution of multiple tcps with random loss queues |
| publisher |
Institutional Knowledge at Singapore Management University |
| publishDate |
1999 |
| url |
https://ink.library.smu.edu.sg/sis_research/721 https://ink.library.smu.edu.sg/context/sis_research/article/1720/viewcontent/WindowDistnMultipleTCP_Globecom99.pdf |
| _version_ |
1770570690710208512 |