Transcript Network Simulation and Testing Polly Huang EE NTU

Network Simulation and Testing
Polly Huang
EE NTU
http://cc.ee.ntu.edu.tw/~phuang
[email protected]
1
Traffic Papers
•
•
•
•
V. Paxson, and S. Floyd, Wide-Area Traffic: The Failure of Poisson Modeling.
IEEE/ACM Transactions on Networking, Vol. 3 No. 3, pp. 226-244, June 1995
W. E. Leland, M. S. Taqqu, W. Willinger, and D. V. Wilson, On the SelfSimilar Nature of Ethernet Traffic. IEEE/ACM Transactions on Networking,
Vol. 2, No. 1, pp. 1-15, Feb. 1995
M. E. Crovella and A. Bestavros, Self-Similarity in World Wide Web Traffic:
Evidence and Possible Causes. IEEE/ACM Transactions on Networking, Vol
5, No. 6, pp. 835-846, December 1997
Anja Feldmann; Anna C. Gilbert; Polly Huang; Walter Willinger, Dynamics of
IP traffic: A study of the role of variability and the impact of control. In the
Proceeding of SIGCOMM '99, Cambridge, Massachusetts, September 1999
2
Paper Selection
Interesting Boring
Easy
Difficult
Failure of
Poisson
SS in
Ethernet
SS in
WWW
IP
Dynamics
3
Identifying Internet Traffic
Failure of Poisson
Self-similar Traffic
Practical Model
4
The Problem
• What is the traffic workload like?
• Call/packet arrival rate as a process
• What kind of process is it?
• Very old problem and a lot of work
5
Because
• Traces are available
• Researchers care about
– The validness of their assumption
– The network traffic being independent Poisson
• Operation people care a lot about
– The amount of buffer/bandwidth to provision for their
networks
– The profit comes from satisfying customers with
minimum infrastructure cost
6
Telephone Network
• Assumptions
– Poisson call arrivals
– Exponential call duration
• Wonderful Property
– Poisson mixing with Poisson is still Poisson
– Average rate well-characterize a call
• The whole queueing theory
7
Data Network?
• Wide-Area Traffic: The Failure of Poisson
Modeling
• V. Paxson, and S. Floyd
• IEEE/ACM Transactions on Networking,
Vol. 3 No. 3, pp. 226-244, June 1995
8
A Study of the Wide-Area Traffic
• Two units of examination
– Connections vs. packets
• A sizeable number of traces
– ~4M connections, ~26M packets
– Different location and different time
• Inter-arrival processes
– TCP connections
– Telnet packets
– FTPDATA connections
• Going self-similar
9
Unit of Observation
• Telephone network
– Circuit-switched
– The unit is circuit, i.e., a call
– People picking up the phone and talk for a while
• Data network
–
–
–
–
Packet-switched
The unit is packet
Another unit is connection, comparable to call
People starting up an FTP connection and send data for
a while
10
Packet  Connection
• Hosts send/receive packets over a channel
at the transport layer
– Reliable: TCP
– Non-reliable: UDP
• Packets from various channels multiplex at
the the network layer
– IP Routers switched on the packets
11
Inter-Arrival Process:
A Little Exercise
Beginning
SYN
ACK&SYN
ACK&Segment 1
?
FIN
ACK&FIN
ACK
End
?
Beginning
12
TCP Connection Arrival Poisson?
Depends
13
Application Dependent
• TELNET
– Users typing ‘telnet cc.ee.ntu.edu.tw’
• FTP
– User typing ‘ftp cc.ee.ntu.edu.tw’
• FTPDATA burst
– User typing ‘mget net-simtest-*.ppt’
• FTPDATA
– Each individual TCP transfer
• NNTP & SMTP
– Machine initiated and/or timer-driven
14
Independent and Poisson?
Y/N
TELNET
FTP
FTPDATA
FTPDATA burst
SMTP
NNTP
15
Quick Summary
• TELNET and FTP
– Independent and Poisson
– Both the 1-hour and 10-min scales
• FTPDATA bursts and SMTP
– At the 10-min interval
– Not ‘terribly far’ from Poisson
– SMTP inter-arrival is not independent
• FTPDATA, NNTP
– Clearly not Poisson
16
Before One Can Explain
• Human-initiated process
– Independent and Poisson
• Non-human-initiated process
– Well, who knows
17
Explanations I
• TELNET and FTP
– User initiated
– Users typing ‘telnet cc.ee.ntu.edu.tw’
– User typing ‘ftp cc.ee.ntu.edu.tw’
• FTPDATA bursts
– User typing ‘mget net-simtest-*.ppt’
– Actually, taking the closely-spaced connections… (<= 4
sec)
• FTPDATA
– TCP connections
18
Explanations II
• NNTP
– Flooding to propagate network news
– Arrival of news trigger another
– Periodical and implementation/configuration
dependent
• SMTP
– Mailing list
– Timer effects from the DNS queries
19
TELNET Packets Poisson?
No, heavy-tailed!
20
Show in 4 Ways
• Distribution of packet inter-arrival time
– Exponential processes ramp up significantly slower
• Packet arrival pattern in seconds and 10 seconds
– Exponential processes are smoother at the 10sec scale
• Variance-time plot
– Change of variance to time scale
– Var of exponential processes decays quickly
• Packet arrival rate process in seconds
– By the sole visual effect
– Exponential processes are less spiky
21
Full TELNET model?
Poisson connection arrival
Heavy-tailed packet arrival within a connection
22
FTPDATA
• Connection arrival is not Poisson
– Clustered in bursts
• Burst sizes in bytes is quite heavy-tailed
– A 0.5% of bursts contribute to 50% of the
traffic volume
23
OK. We know it’s not Poisson.
But what?
24
Going Self-Similar
• Well, since other evidences suggest so
• And it’s the next good thing
• Go straight into producing self-similar
traffic
25
Producing Self-Similar Traffic
• ON/OFF sources
– Fix ON period rate
– ON/OFF period length heavy-tailed
• M/G/
– Customer arrival being Poisson
– Service time being heavy-tailed with infinite variance
• Authors’ own model
– Pseudo-self-similar
– Not long-range dependent though
26
Performance Implication
• Low-priority traffic starvation
– Shall the high-priority traffic being long-range
dependent (bursty)
• Admission control based on recent traffic
failing
– ‘Congestions haven’t happened for a long
while’ does not mean it won’t happen now
27
The Real Message
Poisson is no longer sufficient!
28
Identifying Internet Traffic
Failure of Poisson
Self-similar Traffic
Practical Model
29
Self-Similar What?
• On the Self-Similar Nature of Ethernet
Traffic
• Will E. Leland; Murad S. Taqqu; Walter
Willinger; Daniel V. Wilson
• IEEE/ACM Transactions on Networking,
Vol. 2, No. 1, pp. 1-15, Feb. 1995
30
This One Easier
• Self-similarity in World Wide Web Traffic:
Evidence and Possible Causes
• Mark E. Crovella; Azer Bestavros
• IEEE/ACM Transactions on Networking,
Vol 5, No. 6, pp. 835-846, December 1997
31
Self-Similar Process
Serpgask Triangles
32
Definition
• X: a stationary time series
• X(m): the m-aggregates
– Summing the time series over non-overlapping
blocks of m
• X is H-self-similar if
– X (m) has the same distribution for all positive m
33
Same Distribution?
• Same autocorrelation function
– r(k) = E[(Xt - )(Xt+k - )]/2
• r(k) ~ k-
–k
–0<<1
34
Significance of
-
k
• Long-range dependence
– Just another way of characterizing the same thing
• Power-law decay
– Slower than exponential decay
– Therefore traffic does not smooth up
• <1
– r(k) does not converge
– Sum of r(k) infinite, I.e., variance infinite
35
Just FYI
• The Hurst parameter: 1- /2
36
Tests for Self-Similarity
• Variance-time plot
– A line with slope - > -1
• R/S plot
– Rescaled range grows as the number points included
– A line with slope H an the log-log scale
• Periodogram
– Power spectrum to frequency
– A line with slope  - 1 at the log-log scale
• Whittle estimator
– Confidence to a form
– FGN or Fractional ARIMA
37
Pareto Review
• Exponential
– f(x) = ce-cx
• Heavy-tailed
– F(x) ~ x-c, 0 < c < 2
– Hyperbolic
• Pareto
– f(x) = ckc x-c-1
– F(x) = 1- (k/x)c
– A line at the log-log scale of F(x) plot
38
In Addition to the Theory
• A HUGE volume of Ethernet traces
• Show consistency of being self-similar in
all sorts of tests
• Implication to traffic engineering
• A bombshell!
39
Why Self-Similar?
• Theory suggests
– Fix rate ON/OFF process
– Heavy-tailed length
• Looking into the length
– The ON time: transmission time
– The OFF time: silent time
40
Physical Cause
• Heavy-tailed transmission time
– Heavy-tailed file sizes
– Magic of the nature
– E.g., book size in library
41
Identifying Internet Traffic
Failure of Poisson
Self-similar Traffic
Practical Model
42
So, enough Math.
Just tell me what to do!
It depends!
43
Cutting to the Chase
• The structural model
– user level: Poisson arrival and heavy-tailed
duration
– network level: TCP closed-loop feedback
control and ack clocking
– Variability: delay and congestion
• Let simulators track the complex behavior
44
Why not FGN?
• IP Traffic Dynamics: The Role of
Variability and Control
• Anja Feldmann; Anna C. Gilbert; Polly
Huang; Walter Willinger
• In the Proceeding of SIGCOMM '99,
Cambridge, Massachusetts, September 1999
45
Remember Wavelet Analysis?
• FFT
– Frequency decomposition
– fj, Fourier coefficient
– Amount of the signal in frequency j
• WT: wavelet transform
– Frequency (scale) and time decomposition
– dj,k, wavelet coefficient
– Amount of the signal in frequency j, time k
46
Self-similarity
• Energy function
– Ej = Σ(dj,k)2/Nj
– Weighted average of the signal strength at scale
j
• Self-similar process
– Ej = 2j(2H-1) C <- the magic!!
– log2 Ej = (2H-1) j + log2C
– linear relationship between log2 Ej and j
47
‘Shape’ of Self-Similarity
Self-similar
?? RTT
48
Wavelet Example
1
0
-1
00 00 00 00 11 11 11 11
s1
s2
s3
s4 8
00002222
0044
08
00
8
0000
00000000
d1
d2
d3
49
d4
Adding Periodicity
• packets arrive periodically, 1 pkt/23 msec
• coefficients cancel out at scale 4
10 00 00 00 10 00 00 00
s1
s2
s3
s4 2
10001000
1010
11
11
0
1010
10001000
d1
d2
d3
50
d4
Visualization
Adding Periodicity
J=4
51
’Shape' of self-similarity
Self-similar
Yes RTT!
52
Large Scale
• Heavy-tailed connection duration
53
Medium Scale
• TCP close-loop control
RTT
54
TCP Flow Control
source
RTT
sink
RTT
RTT
Time
55
Variability
• Delay and congestion (bandwidth & load)
Simulation
Measurement
56
Internet Traffic is Weird!
• Different properties at different time scales
– Large scales: self-similarity
– Medium scale: periodicity
– Small scale: ??? (possibly multifractal)
57
New Queuing Theory?
• For chaotic Internet traffic
• Only pen and paper
58
NO!
• Probably not in the near future
• Confirmed by the experts
59
A Few Reasons
• Not exactly self-similar (FGN - big no no)
• ’Shape' of self-similarity changes with the
network conditions
• Don't know what self-similar processes add
up to (mathematically intractable)
• Don’t know what those strange small-scale
behavior is exactly
60
Therefore
• The structural model
– User level: Poisson arrival and heavy-tailed
duration
– Network level: TCP closed-loop feedback
control and ack clocking
– Variability: delay and congestion
• Let simulators track the complex behavior
61
Questions?
62
On the Review Forms
• Novelty
– New idea
• Clarity
– The problem
• Reality (practicality)
– Evaluation
• Importance, significance, relevance
– How much impact?
– Would things change?
63
OK for Beginners
• Clarity
– Easiest
– Judging the writing
• Evaluation
– Easy
– Judging the experiments and technical content
64
Challenging for the Advanced
• Novelty
– Hard
– Need to follow/read enough papers in the area
• Importance
– Hardest
– Need to have breadth and know enough
development in the area
65
Show your FreeBSD installation!
66