Capturing Network Traffic Dynamics - Rice...

Capturing Network Traffic DynamicsSmall Scales

Stochastic Systems and Modelling in Networking and FinancePart II

Dependable Adaptive Systems and Mathematical ModelingKaiserslautern, August 2006

Rolf Riedi

Dept of Statistics

Rudolf Riedi Rice University stat.rice.edu/~riedi

Model and Physical Reality

PhenomenonPhysical System

StatisticalModel Physical

Model

StochasticModel

Convergence of ON-OFF to fBm

LRDSelf-similarity

Queuing predictionEstimation of LRD

User responsiblefor bursts at large scale

Large scaleswell understood


Failure of classical prediction

• Interarrival times – Not exponential– Not independent

Paxson-Floyd, 1995


Multiscale

Hurst


Measured Data• Time series (Ak,Zk) collected at gateway of LAN

– k= number of data packet– Ak= arrival time of packet– Bk= size of packet

• Work load until time t:

••Working arrival per m time units


Long Range Dependence

• High variability at large scales caused by correlation

• Auto-covariance function

• LRD– Slowly decaying auto-covariance

• Cox: for ½ < H < 1 (presence of LRD)

– Var-time-plot: simple first diagnostics for LRD


ON-OFF: Physical Traffic Model

(Taqqu Levy 1986)


fBm Historic facts• Brown (1820): observes particle motion• Markov (1900+): Markov chains • Einstein (1905): Heat equ for Brownian motion• Wiener (1923): continuous Markov process• Kolmogorov (1930’s): theory of stochastic processes• Kolmogorov (1940): fBm• Levy, Lamperti: H-sssi processes (1962)• Mandelbrot & VanNess: integral representation (1965)

• Adler: fractal path properties of fBm• Samorodnitsky & Taqqu:

self-similar stable motion


Connection-level Analysis and

Modeling of Network Traffic


Aggregate Traffic at small scales• Trace:

– Time stamped headers– Sender-Receiver IP !!

• Large scales– Gaussian– LRD (high variability)

• Small scales– Non-Gaussian– Positive process– Burstiness

Objective :– Origins of bursts

Auckland Gateway (2000)Aggregate Bytes per time

Gaussian: 1%Real traffic: 3%

Kurtosis - Gaussian : 3- Real traffic: 5.8

Mean

99%

0 0.5 1 1.5 2 2.5 3x 105

0

50

100

150

200

250

300

350

400

450histogram

Gaussian: 1%Real traffic: 3%

0 2000 4000 60000

0.5

1

1.5

2

2.5

3x 105

time (1 unit=500ms)nu

mbe

r of b

ytes

Mean

99%


• ON/OFF model – Superposition of sources– Connection level model

• Explains large scale variability: – LRD, Gaussian– Cause: Costumers– Heavy tailed file sizes !!

Bursts in the ON/OFF framework

• Small scale bursts:– Non-Gaussianity– Conspiracy of sources ??– Flash crowds ??

(dramatic increase of active sources)


Non-Gaussianity: A Conspiracy?

• The number of active connections is close to Gaussian; provides no indication of bursts in the load

• Indication for:- No conspiracy of sources- No flash crowds

Load: Bytes per 500 ms

Number of active connections

Mean

99%

Mean

99%


Non-Gaussianity: a case studyTypical bursty arrival(500 ms time slot)

Histogram of load offered in same time bin per connection:One connection dominates

150 Kb

Typical Gaussian arrival(500 ms time slot)

Histogram of load offered in same time bin per connection:Considerable balanced “field” of connections

10 Kb


Non-Gaussianity and Dominance

• Dominant connections correlate with bursts

Circled in Red: Instances where one connections contributes over 50% of load(resolution 500 ms)

Mean

99%


Non-Gaussianity and Dominance

Systematic study: time series separation• For each bin of 500 ms:

remove packets of the ONE strongest connection• Leaves “Gaussian” residual traffic

Overall traffic Residual traffic1 Strongest connection

= +Mean

99%


Separation on Connection Level

Definition:• Alpha connections:

Peak rate > mean arrival rate + 1 std dev

• Beta connections: Residual traffic

• Findings are similar for different time series– Auckland (2000+2001), Berkeley, Bellcore, DEC– 500ms, 50ms, 5ms resolution


Alpha Traffic Component

• There are few Alpha connections – < 1% (AUCK 2000: 427 of 64,087 connections)

– 3% of load

Alpha is extremely bursty

Beta is little bursty

Overall traffic is quite bursty

• Alpha connections cause bursts:

Multifractal spectrum:Wide spectrum means bursty

Balanced (50% alpha) very bursty


Multifractal spectrum: Microscope for Bursts

α=.7 α=.9 α=.8

• Collect points t with same α :

aLarge Deviation type result


Beta Traffic Component

• Constitutes main load• Governs LRD properties of overall traffic• Is Gaussian at sufficient utilization (Kurtosis = 3)

• Is well matched by ON/OFF model

Beta traffic Number of connections = ON/OFF

Variance time plot

Mean

99%


Simple Connection Taxonomy

Careful analysis on connection level shows : this is the onlysystematic reason

Bursts arise from largetransfers over fast links.

RTTrate

bandwidth =But:


Cwnd or RTT?

Correlation coefficient=0.68

RTT has strong influence on bandwidth and dominance.

103

104

105

106

10-1

100

101

102

peak-rate (Bps)

1/R

TT (1

/s)

Correlation coefficient=0.01

103

104

105

10610

2

103

104

105

peak-rate (Bps)

cwnd

(B)

Colorado State University trace, 300,000 packets

cwnd 1/RTT

ratepeak →

Beta Alpha Beta Alpha


Examples of Alpha/Beta Connections

0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4

-200

0

200

400

600

800

1000

1200

1400

one beta connection

packet arrival time (second)

pack

et s

ize

(byt

es)

forward direction

reverse direction

41.3 41.35 41.4 41.45

-200

0

200

400

600

800

1000

1200

1400

one alpha connection (96078, 196, 80, 59486)

packet arrival time (second)

pack

et s

ize

(byt

es)

forward direction

reverse direction

Alpha connections burst because of short round trip time, not large rate

Notice the different time scales


Modeling Network Traffic

Physical Model• Traffic (user): superposition of ON/OFF

sources requesting files with heavy tailed size• Network: heterogeneous bandwidth

variable sending-rates (fixed per ON/OFF source)

• Explains properties of traffic:–LRD: heavy tailed transfer of beta sources (crowd)

–Bursts: few large transfers of few alpha sources

Mathematical Model•…accommodate this insight within ON-OFF?


Modeling of Alpha Traffic• ON/OFF model revisited:

High variability in connection rates (RTTs)

Low rate = beta High rate = alpha

fractional Gaussian noise Non-Gaussian limit??

+

=

+

+

=





fractional Gaussian noise Non-Gaussian limit

0 2000 4000 60000

0.5

1

1.5

2

x 105

time (1 unit=500ms)

num

ber o

f byt

es

0 2000 4000 60000

0.5

1

1.5

2

2.5

3x 105

time (1 unit=500ms)

num

ber o

f byt

es


Towards mathematical models

Renewal reward processes


Stable distributions• Parameters:

• Equivalent definitions:– Stable– Limit of iid sums

• Known special cases:– Gaussian– Cauchy

• Characteristic fct


High Multiplex vs Large Scale


Different Limits for ON-OFF model

• Recall limitsof ON-OFF sourcesmultiplexed

• Possible limits of renewal reward aggregateWillinger Paxson R Taqqu


ON-OFF traffic model

…revisited





fractional Gaussian noise stable Levy noise

+

=

+

+

=


Impact: Simulation

• Simulation: ns topology to include alpha links

Simple: equal bandwidth Realistic: heterogeneousend-to-end bandwidth

• Congestion control• Design and management


Inpact: Understanding• LRD

– Large time scales – approx. Gaussian – Client behavior– Bandwidth over Buffer

• Multifractal– Small time scale– Network topology– Control at flow level– Simulation

Structure: Multiplicative AdditiveModel: hybrid tree

Mixture of Gaussian - Stable

packetscheduling

sessionlifetime

networkmanagement

round-triptime

< 1 msec msec-sec minutes hours


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


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


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


On-off parameters

• Free parameters in on-off model?– File size = duration * rate : these variables are dependent– Assuming two of them are independent leads to following models:

• Simulation: same “behavior” for entire traffic results in poor match. Different models (power/patience) for alpha and beta?

Power modelFile size and rate independent

Patience model:File size and duration independent

Real trace Real trace

Simulation using observed size and rate independently

Simulation using observed size and duration independently


Duration (s)

Rat

e (b

ytes

per

sec

)

Duration and Rate (Beta)

0.1 10 1000 10

1k

100k

0

10

20

30

40

50

60

70

80

90

Size (bytes)

Rat

e (b

ytes

per

sec

)

Filesize and Rate (Alpha)

10 1k 100k 10M 10

1k

100k

0

2

4

6

8

10

12

14

16

Size (bytes)

Rat

e (b

ytes

per

sec

)

Filesize and Rate (Beta)

10 1k 100k 10M 10

1k

100k

0

10

20

30

40

50

60

70

80

90

Duration (s)

Rat

e (b

ytes

per

sec

)

Duration and Rate (Alpha)

0.1 10 1000 10

1k

100k

0

2

4

6

8

10

12

Free parameters: statistical analysis

Alpha Beta

Duration- Rate

Size- Rate

X

• Beta users: rate determines file size• Alpha users are “free”


0 1000 2000 3000 4000 5000 6000 70000

1

2

3

4

5

6

7

8

9x 10

5 Bytes per time (overall)

Time bin (1 unit = 500ms)

Byt

es

0 1000 2000 3000 4000 5000 6000 70000

1

2

3

4

5

6

7

8

9x 10

5 Bytes per time (Alpha)


Byt

es

0 1000 2000 3000 4000 5000 6000 70000

1

2

3

4

5

6

7

8

9x 10

5 Bytes per time (Beta)


Byt

es

0 2000 4000 60000

2

4

6

8

x 105 Bytes per time (overall)


Byt

es

0 2000 4000 60000

2

4

6

8

x 105 Bytes per time (Alpha)


Byt

es

0 2000 4000 60000

2

4

6

8



Byt

es

Scheme RD:Rate Durationindependent

Total

Scheme SD:Size Durationindependent

Scheme SR: Size Rate

independent

Alpha

Beta

0 2000 4000 60000

0.5

1

1.5

2

2.5

3



Byt

es

0 2000 4000 60000

1

2

3

4

5

6

x 107 Bytes per time (Beta)


Byt

es

0 2000 4000 60000

2

4

6

8

10x 10

6 Bytes per time (Beta)


Byt

es

0 2000 4000 60000

2

4

6

8

x 105 Bytes per time (Alpha)


Byt

es

0 2000 4000 60000

2

4

6

8

x 105 Bytes per time (Beta)


Byt

es

Real Trace

0 2000 4000 60000

2

4

6

8



Byt

es

Free parameters: SIMULATION


Network-User Driven Traffic model

CONCLUSION: • Rates for alpha drawn to be large, beta drawn to be

small but:– Alpha: power model: Rate independent of Size– Beta: patience factor: Rate independent of Duration

• New limiting results needed for novel ON-OFF settings

0 1000 2000 3000 4000 5000 6000 70000

1

2

3

4

5

6

7

8

9x 10



Byt

es

0 1000 2000 3000 4000 5000 6000 70000

1

2

3

4

5

6

7

8

9x 10



Byt

es

Original trace (Bellcore) Alpha (SR) + Beta (RD)


Model and Physical Reality

PhenomenonPhysical System

StatisticalModel

ON-OFF withasympt. regimes. Renewal Reward.

StochasticModel

Two component model(alpha/beta users)

Self-similar limitsare fBm (multiplexed beta)or Levy stable (fast alpha)

Queuing predictionDetection of alpha users User responsible

for bursts at large scale

Small scalessufficiently understood.

Choice of physical model not clear

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