Explain bursts

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Connection-level Analysis and Modeling of Network Traffic understanding the cause of bursts control and improve performance detect changes of network state

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Connection-level Analysis and Modeling of Network Traffic understanding the cause of bursts control and improve performance detect changes of network state. Explain bursts. Large scale: Origins of LRD understood through ON/OFF model - PowerPoint PPT Presentation

Transcript of Explain bursts

Page 1: Explain bursts

Connection-level Analysis and Modeling of Network Traffic

understanding the cause of burstscontrol and improve performancedetect changes of network state

Page 2: Explain bursts

R. Riedi Spin.rice.edu

Explain bursts

Large scale: Origins of LRD understood through ON/OFF model Small scale: Origins of bursts poorly understood, i.e.,ON/OFF model with equal sources fails to explain bursts

Load (in bytes): non-Gaussian, bursty

Number of active connections: Gaussian

Page 3: Explain bursts

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Non-Gaussianity and Dominance

Connection level separation:– remove packets of the ONE strongest connection– Leaves “Gaussian” residual traffic

Traffic components:– Alpha connections: high rate (> ½ bandwidth)– Beta connections: all the rest

Overall traffic Residual traffic1 Strongest connection

= +Mean

99%

Page 4: Explain bursts

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CWND or RTT?

Correlation coefficient=0.68

Short RTT correlates with high rate

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peak-rate (Bps)

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Correlation coefficient=0.01

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peak-rate (Bps)cw

nd (

B)

Colorado State University trace, 300,000 packets

cwnd 1/RTTrate

cwnd 1/RTTrate

Beta Alpha Beta Alpha

Challenge: estimation of RTT and CWND/ratefrom trace / at router

Page 5: Explain bursts

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Impact: Performance• Beta Traffic rules the small Queues• Alpha Traffic causes the large Queue-sizes

(despite small Window Size)

Alpha connections

Queue-size overlapped with Alpha PeaksTotal

traffic

Page 6: Explain bursts

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Two models for alpha traffic

Impact of alpha burst in two scenarios:• Flow control at end hosts

– TCP advertised window

• Congestion control at router– TCP congestion window

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Modeling Alpha Traffic• ON/OFF model revisited:

High variability in connection rates (RTTs)

Low rate = beta High rate = alpha

fractional Gaussian noise stable Levy noise

+

=

+

+

=

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Self-similar Burst Model• Alpha component = self-similar stable

– (limit of a few ON-OFF sources in the limit of fast time)

• This models heavy-tailed bursts – (heavy tailed files)

• TCP control: alpha CWND arbitrarily large – (short RTT, future TCP mutants)

• Analysis via De-Multiplexing:– Optimal setup of two individual Queues to come closest to

aggregate Queue

De-Multiplexing:Equal critical time-scales

Q-tail ParetoDue to Levy noise

Beta (top) + Alpha

Page 9: Explain bursts

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ON-OFF Burst Model• Alpha traffic = High rate ON-OFF source (truncated)• This models bi-modal bandwidth distribution• TCP: bottleneck is at the receiver (flow control

through advertised window)• Current state of measured traffic• Analysis: de-multiplexing and variable rate queue

Beta (top) + Alpha Variable Service Rate Queue-tail Weibull (unaffected) unless

• rate of alpha traffic larger than capacity – average beta arrival • and duration of alpha ON period heavy tailed

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Conclusions

• Network modeling and simulation need to include– Connection level detail– Heterogeneity of topology

• Physically motivated models at large• Challenges of inference

– From traces– At the router

• Need for adapted Queuing theory