Transient analysis of RED queues: A quantitative analysis of buffer-occupancy fluctuations and relevant time-scales

Rank, Stefan; Schwefel, Hans-Peter

doi:10.1016/j.peva.2005.08.001

Cited by 4 publications

(3 citation statements)

References 25 publications

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“…We set the q w parameter in compliance with the recommendations in the research works [18] and [20]. The parameter q w defines how closely the average estimate of the actual buffer occupancy matches the momentary queue.…”

Section: Methodology For Red Buffer Configurationmentioning

confidence: 99%

See 1 more Smart Citation

Dimensioning Drop-tail and AQM (RED) buffers at access networks for optimal performance with bulk data TCP traffic

Avramova¹,

Vleeschauwer²,

Wittevrongel³

et al. 2010

Computer Communications

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Section: Methodology For Red Buffer Configurationmentioning

confidence: 99%

“…A study on the transient behavior of the RED algorithm is conducted in e.g. [20], but is out of the scope of this paper.…”

Section: Optimal Operation Of Red Buffermentioning

confidence: 99%

Dimensioning Drop-tail and AQM (RED) buffers at access networks for optimal performance with bulk data TCP traffic

Avramova¹,

Vleeschauwer²,

Wittevrongel³

et al. 2010

Computer Communications

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“…On longer time scales TCP aims at stabilizing the load on a congested link close to a full utilization. In fact, the analysis and simulation of TCP congestion control on packet loss is complex and empirical studies seem missing especially for high speed links with a mixture of thousands of flows in parallel having largely different bandwidths and round trip delays [28].…”

Section: Traffic Tracesmentioning

confidence: 99%

Packet Loss in Real-Time Services: Markovian Models Generating QoE Impairments

Hohlfeld

Geib

Hasslinger

2008

2008 16th Interntional Workshop on Quality of Service

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Abstract-Real-time Internet services are gaining in popularity due to rapid provisioning of broadband access technologies. Delivery of high Quality of Experience (QoE) is important for consumer acceptance of multimedia applications.IP packet level errors affect QoE and the resulting quality degradations have to be taken into account in network operation. We derive the second order statistics of the number of packet losses in finite Markov models over several relevant time scales and adapt them to loss processes visible in wired and wireless transmission channels.Higher order Markov chains offer a large set of parameters to be exploited by complex fitting procedures. We experience that the 2-state Gilbert-Elliott model already captures a wide range of observed loss pattern appropriately and discuss how such models can be used to examine the quality degradations caused by packet losses.

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