End-to-End Network Performance Estimation Using Signal ComplexitySlides

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.

What is the Signal?
•

BLACK BOX:

end to end (e2e) network, parts of network, data centers,
network/data services, applications, etc.
◦ in this paper specifically the
bandwidth (AB)

e2e network, even more specifically available

INPUT: active probing, basically trains of packets thrown at e2e paths
• OUTPUT: the same packet trains on the other side
• OBJECTIVE: to measure/understand/model the black box 02
•

02 myself+1 "Modeling Network Performance of End Hosts" IEICE Trans. vol.E95-D, no.7 (2012)
M.Zhanikeev -- maratishe@gmail.com --

E2E Network Performance Estimation Using Signal Complexity -- http://tinyurl.com/kyutech131113 ---

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What is the Problem?
• it is really really

hard 06 11

tools and methods, all come up with mismatched results [07]
many design tricks, little math [08] [13]

• multiple
•

• too much specificity, no

unified/universal tool

06 M.Jain+1 "End-to-End Available Bandwidth: Dynamics..." IEEE/ACM Trans., vol.11 (2003)
11 C.Dovrolis+2 "What do packet dispersion techniques measure?" INFOCOM, vol.2 (2001)


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The Problem: Specifics (AB)
available bandwidth (AB)

• this paper talks specifically about
◦ free capacity on some end-to-end path
◦ between 6 and 30 hops in real networks

realtime

• it is a
technology!
• we cannot stress the network too much
◦ example: if you want to find out how long some guy can hold his/her breath under
water, it is wrong to drown the person to find out
◦ this paper will call that the Brutal Method



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The Problem: Classification 1
• obvious
•

diagonal grouping

IGI 07 and PathChirp 08 are the two comparison targets
◦

NOTE: so far all three are in the same cell

Low Precision
IGI, PathChirp
Short trains
This Method
Long Trains

High Precision

iPerf
PathLoad

07 N.Hu+1 "Evaluation and Characterization of AB ... Techniques" IEEE JSAC, vol.21, no.6 (2003)
08 V.Ribeiro+4 "pathChirp: Efficient Available Bandwidth Estimation for Paths" PAM Workshop (2003)


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The Problem: Classification 2
distinction

• this is where the proposed method finds its
• the basic idea: simple trains, complex analysis
◦ this paper uses

Permutation Entropy (PE) 09

Simple Design
Short trains This Method
Long Trains

Intricate Design
IGI, PathChirp

iPerf
PathLoad

09 C.Bandt+1 "Permutation Entropy: A Natural Complexity Measure for Time Series" Physical Review Letters, Vol.88(17) (2002)


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PathChirp 08

exponentially shrinking
trains will self-load (temporarily)

• idea:

• the upper plot is the expected trend

Departure gap

• this is

Arrival gap Departure gap

Things that Don't Work (1)
8

Breaking point

1

2

3

4

7
5

6

1
2

3
4

5

Exponential
curve
6

7

8

Time/packet sequence

08 V.Ribeiro+4 "pathChirp: Efficient Available Bandwidth Estimation for Paths" PAM Workshop (2003)


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Things that Don't Work (2)

PathChirp in
real life

• this is

Mixed normalized values

pathchirp (diff)
pathchirp (departure)

pathchirp (arrival)
back-to-back (arrival)

1
0.6
0.2
-0.2
0

20
40
60
80
Probe packet/time sequence

01 myself "e2eprobe: source code of probing methods" https://github.com/maratishe/e2eprobe (2013)


100

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Other Things that Don't Work
groping by making small adjustment to packet space, like in IGI 07
different combinations of packet size in the same probe
basic packet pair (just two packets) cannot measure AB, either 11

• any kind of
•
•

• ... good comparative study at 07
• ...

source code for IGI, PathChirp and the proposal at 01

11 C.Dovrolis+2 "What do packet dispersion techniques measure?" INFOCOM, vol.2 (2001)


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The Solution: Observations
.
The most frequent pattern is ...
.
... that packets experience
network
.

extreme states of being affected by the

• see the

PathChirp in action a couple of slides ago

• the pattern is found for most train designs, single packets, packet pairs, etc.

.
The Solution therefore is...
.
...to analyze data as a combination of
basically)
.


binary states (zeros and ones,


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The Solution: PE (algorithm)
{
}
• arrival packet
•

gaps x(i), i = 1, 2, ..

embed into m-dimensional space:
Xi = [x(i), x(i + d), ..., x(i + (m − 1)d)]

◦ for each i, there are multiple is
◦ warping is allowed to maintain same size in all vectors
◦ d is delay, the paper uses d = 1, so only neighbors -- supported by practice 02

Xi turned into the vector [0, 1, ..., m − 1] where value is the other, i.e.
0th, 1th in Xi .
m! permutations for each vector, each becomes a symbol
signal is then simply m-d space using in total i symbols for each permutation
result: normalized entropy of the set of symbols
◦ in this paper the best m (smallest entropy) is found using 10

• each
•
•
•

02 myself+1 "Modeling Network Performance of End Hosts" IEICE Trans. vol.E95-D, no.7 (2012)
09 C.Bandt+1 "Permutation Entropy: A Natural Complexity Measure for Time Series" Physical Review Letters, Vol.88(17) (2002)
10 A.Brandmaier "Permutation Distribution Clustering and Structural Equation Model Trees" PhD Diss. (2012)


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The Solution: Actual Methods
•

static method (BC: bottleneck capacity) (r: tuning parameter = 0.8)
AB = BC − rBC(1 − PE).

•

(1)

adaptive method: reference frame via calibration (max/min AB and
PE values)
AB = ABmax −


(ABmax − ABmin )(PE − PEmin )
.
(PEmax − PEmin )


(2)

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Experiments: Probing Network
another location in the city
another lab
lab

P1

Probing
point 1

Traffic
dump


P2

Probing
point 2

Probing
target

P3

Probing
point 3


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• using software in 01 (IGI,

PathChirp, PE)

noisy,
so catch when diagonal line

• IGI and PathChirp are

is crossed (simple heuristic)
• PE methods (0.8 and adaptive)

are from the previous slide -no probe design

Receiving rate (Mbps)

Experiments: Methods
igi

100

pathchirp

Detection point

90
80
70
60
50
40
40

50
60
70
80
90
Sending rate (Mbps)



100

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Experiments: One Run
• real probing in the wild
• prefer to run all the methods roughly at
•

same time
PE and Brute AB are based on
back-to-back probe

Probing
stream

1 Back-to-back probe
2 Pathchirp probe

Feedback

3 IGI probe * 5 gaps

Pick random
psize,
probesize

Run
the probing
client

To the
next run


Store
data on
server side

the

var psize; // random [100, 1000]
var probesize; // random [50, 350]
// open TCP client to remote IP and port
var client = new Client( rip, rport);
// run back-to-back probe
// … and wait for RX throughput
runBackToBackTX( psize, probesize);
var thru = client->waitForStatus();
// pathchirp probe
var low = 0.5 * thru;
var high = 5 * thru;
var alpha = pickAlpha( low, high);
runPathChirpTX( psize, low, high, alpha);
// IGI probe
var low = 0.5 * thru;
var high = 1.5 * thru;
var step = ( high - low) / 5;
for ( var rate = low; rate <= high; rate += step) {
runIgiTX( psize, probesize, rate);
}


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Results (1)

result: 0.8

adaptive is the best!

30

P1

Error

30

igi

igi
50

70
Brute AB

40
pathchirp
30 igi
20 adaptive
0.8
10

90

70 84
AB


adaptive

60
40
20

P2
20

igi

0.8

0
56

80

98

50
40

igi
pathchirp
40

60
80
Brute AB
igi
0.8

adaptive
20
pathchirp
igi
0

adaptive
0.8

30 50 70 90
AB

adaptive

0.8

90

0.8

70

50 adaptive
igi
30
pathchirp
10 P3
10

60
Error

50

0.8

0.8

0.8

Error

70

100

Estimated AB

pathchirp
adaptive
0.8

90

Estimated AB

Estimated AB

•
is better on average, but
• performance varies with network distance

30

50
70
90
Brute AB
pathchirp

0.8

40

pathchirp
igi
adaptive

igi

igi
0.8

adaptive
30 50 70 90
AB


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Results (2) : CCF Analysis
0.6

0.4

0.53

CCF

0.2
-0.2
-0.3
-0.6

-0.58
P
vs
Brute AB


Brute AB
vs
Brute PE

Brute AB
vs
Pathchirp AB

Brute AB
vs
IGI AB


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.

Wrapup
•

PE helps by

1. using dumb probes with simple designs
2. reducing analysis to complexity of binary response

• the method can be used for any metric, not just AB
◦ jitter
◦ tomography (sensing of topology)
◦ load or utilization of a system
◦ etc....
• will cover all these in


future work ...


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That’s all, thank you ...



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[01] myself (2013)
e2eprobe: source code of probing methods
https://github.com/maratishe/e2eprobe
[02] myself+1 (2012)
Modeling Network Performance of End Hosts
IEICE Trans. vol.E95-D, no.7
[03] 1+myself+1 (2006)
Rate-Based and Gap-Based Available Bandwidth Estimation...
Springer LNCS, vol.4238
[04] 1+myself+1 (2006)
ABshoot: A Reliable and Efficient Scheme for End-to-End AB...
IEEE TENCON
[05] 1+M.Zhanikeev (2009)
Active Network Measurement: Theory, Methods, and Tools
ITU Japan


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[06] M.Jain+1 (2003)
End-to-End Available Bandwidth: Dynamics...
IEEE/ACM Trans., vol.11
[07] N.Hu+1 (2003)
Evaluation and Characterization of AB ... Techniques
IEEE JSAC, vol.21, no.6
[08] V.Ribeiro+4 (2003)
pathChirp: Efficient Available Bandwidth Estimation for Paths
PAM Workshop
[09] C.Bandt+1 (2002)
Permutation Entropy: A Natural Complexity Measure for Time Series
Physical Review Letters, Vol.88(17)
[10] A.Brandmaier (2012)
Permutation Distribution Clustering and Structural Equation Model Trees
PhD Diss.


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[11] C.Dovrolis+2 (2001)
What do packet dispersion techniques measure?
INFOCOM, vol.2
[13] J.Strauss+2 (20s03)
A Measurement Study of Available Bandwidth Estimation Tools
3rd ACM SIGCOMM



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End-to-End Network Performance Estimation Using Signal ComplexitySlides

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