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

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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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Title: Alpha-Beta Network Model Author: Riedi Description: Actual given talk, submitted version: RiceOct2001.ppt Last modified by: Rice University – PowerPoint PPT presentation

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


1
Connection-level Analysis and Modeling of
Network Trafficunderstanding the cause of
burstscontrol and improve performancedetect
changes of network state
2
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
3
Non-Gaussianity and Dominance
  • Connection level separation
  • remove packets of the ONE strongest connection
  • Leaves Gaussian residual traffic
  • Traffic components
  • Alpha connections high rate (gt ½ bandwidth)
  • Beta connections all the rest



Overall traffic
Residual traffic
1 Strongest connection
4
CWND or RTT?
Colorado State University trace, 300,000 packets
1/RTT (1/s)
peak-rate (Bps)
Correlation coefficient0.68
Short RTT correlates with high rate
Challenge estimation of RTT and CWND/rate from
trace / at router
5
Impact Performance
  • Beta Traffic rules the small Queues
  • Alpha Traffic causes the large Queue-sizes
  • (despite small Window Size)

Queue-size overlapped with Alpha Peaks
Total traffic
Alpha connections
6
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

7
Modeling Alpha Traffic
  • ON/OFF model revisited
  • High variability in connection rates (RTTs)

Low rate beta
High rate alpha





stable Levy noise
fractional Gaussian noise
8
Alpha-Beta Model of Traffic
  • Model assumptions
  • Total traffic Alpha component Beta component
  • Alpha and Beta are independent
  • Betafractional Brownian motion
  • Alpha traffic two scenarios
  • Flow control through thin or busy end-hosts
  • ON-OFF Burst model
  • Congestion control allowing large CWND
  • Self-Similar Burst model
  • Methods of analysis
  • Self-similar traffic
  • Queue De-multiplexing
  • Variable service rate

9
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

Beta (top) Alpha
10
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
  • Queue-tail Weibull (unaffected) unless
  • rate of alpha traffic larger than
  • capacity average beta arrival
  • and duration of alpha ON period heavy tailed

Beta (top) Alpha
Variable Service Rate
11
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
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