2002
DOI: 10.1016/s0166-5316(02)00136-0
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A mean-field model for multiple TCP connections through a buffer implementing RED

Abstract: Active queue management schemes like RED (Random Early Detection) have been suggested when multiple TCP sessions are multiplexed through a bottleneck buffer. The idea is to detect congestion before the buffer overflows and packets are lost. When the queue length reaches a certain threshold RED schemes drop/mark incoming packets with a probability that increases as the queue size increases. The objectives are an equitable distribution of packet loss, reduced delay and delay variation and improved network utiliz… Show more

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Cited by 105 publications
(159 citation statements)
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“…The result in Corollary 3 coincides with [3], which is expected since letting C approach ∞ is equivalent to considering random transmission losses only.…”
Section: Corollary 2 (Approximation To Mean Tcp Throughout)supporting
confidence: 62%
“…The result in Corollary 3 coincides with [3], which is expected since letting C approach ∞ is equivalent to considering random transmission losses only.…”
Section: Corollary 2 (Approximation To Mean Tcp Throughout)supporting
confidence: 62%
“…From (2) and (5), we have (12) We assume that at the end of a TD-period , flows experiencing loss in that TDP have the window size , and other flows that experience loss in the previous loss event have the window size , etc. The mean value of the window size of the cumulative flow is (13) From (11)- (13), we have (14) From (10), assuming that a loss occurs independently distributed over the size of the cumulative window in a loss round, hence , we have (15) and including (6), (8), (11), (12), and (14) (16) Solving this equation for , we get (17), shown at the bottom of the page, and including (14), we get (18), shown at the bottom of the page. From (1), (2), (6), (8), and (17), we have (19), shown at the bottom of the page.…”
Section: Modelmentioning
confidence: 99%
“…Kelly's model adopts a fluid model, and the feedback signal generated by a resource is a function of users' instantaneous rates. A fluid model is shown to be a good approximation when the number of flows is large and resource capacities are high [2], [13], [14], [15]. In practice, however, an instantaneous rate through a resource is difficult to measure accurately; in order to obtain a good estimate of the aggregate rate, a resource may need to rely on an average rate over a period.…”
Section: Introductionmentioning
confidence: 99%