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Backbone Traffic
Inference
ISP Backbone Traffic Inference
Methods to
Support Traffic Engineering
Olivier Goldschmidt
Senior Network
Consultant
ISMA 2000
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MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY
Backbone Traffic
Inference
Outline
1. Problem Description
2. Inputs to the Models
3. Constraints of the Models
4. Inference Methods:
Pseudo-Inverse Method
Linear Programming
5. Test Results
6. Conclusion and Open Issues
ISMA 2000
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MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY
Backbone Traffic
Inference
RATIONALE
A major headache for Internet Service Providers
is to estimate the end-to-end traffic volumes on
their backbone network.
Reliable traffic estimates between ingress and
egress points are essential to traffic engineering
purposes such as ATM PVC or LSP layout and
sizing.
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MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY
Backbone Traffic
Inference
Problem Description
An "easy" solution is to turn on NetFlow
or IP-Accounting on all ingress and egress
interfaces.
But such solution is
- Costly
- Impractical
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MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY
Backbone Traffic
Inference
Problem Description
Objective of traffic inference is
to "guess" end to end aggregate
traffic using limited information.
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MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY
Backbone Traffic
Inference
Inputs to the model
Deterministic Information
Measured Information
Usage Information
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MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY
Backbone Traffic
Inference
DETERMINISTIC INFORMATION
Network Topology
Types of routers and links
Routing paths between end points
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MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY
Backbone Traffic
Inference
MEASURED INFORMATION
Baselining Information on
network interfaces using SNMP
Partial RMON/RMON2
information using selective
probes (NetFlow or IP account.)
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MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY
Backbone Traffic
Inference
USAGE INFORMATION
Data that can be correlated
with the traffic on the network
Allows to derive additional
constraints on the network
traffic.
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MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY
Backbone Traffic
Inference
WAN Link
Ingress-Egress points
Internal routers
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MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY
Backbone Traffic
Inference
3
3
Assume that reading are
symmetric.
3
3
Interface flow reading
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MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY
Backbone Traffic
Inference
3
3
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MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY
Backbone Traffic
Inference
1
2
1
2
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Inference
CONSTRAINTS
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Backbone Traffic
Inference
3
3
2
1
1
3
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Inference
PSEUDO-INVERSE METHOD
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MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY
Backbone Traffic
Inference
LINEAR PROGRAMMING METHOD
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MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY
Backbone Traffic
Inference
OBJECTIVE FUNCTION
COEFFICIENTS
Choice of coefficients for the objective function will determine
the precision of the end to end traffic estimates.
Obvious choice is to set all coefficients to 1 and to maximize
or to minimize the objective function
But this choice is not neutral
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Backbone Traffic
Inference
EXAMPLE
10
10
10
Assume these are the true traffic demands
Notice that all interface flows are equal to 20
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MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY
Backbone Traffic
Inference
If all objective coefficients are equal to 1
0
20
20
If objective function is maximized
20
0
0
If objective function is minimized
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MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY
Backbone Traffic
Inference
But if coefficient are equal to the number
of hops of demand route
2
1
10
10
1
10
Is a solution whether objective function
is maximized or minimized
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MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY
Backbone Traffic
Inference
Another advantage of the LP method
Allows to add constraints that represent usage information.
For instance constraint the very unlikely end-to-end traffic to
be close to zero.
Also known traffic from NetFlow or IP accounting readings
can be included as constraints in the linear program.
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Backbone Traffic
Inference
Test Results
NETWORK
• 60 Routers
• 114 WAN Links
• 529 Traffic demands
• Bandwidth from 0 to 256 Kbps
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Backbone Traffic
Inference
Test Results
1. Route the demands
2. Compute the resulting interface flows
3. Apply the Linear Programming method to
estimate the end-to-end traffic demands
4. Compare those estimates with the original
traffic demands in % of absolute difference
|estimate-true value|/true value
The following charts show % of demands with
given relative error
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MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY
Backbone Traffic
Inference
Objective coefficients = number of hops
% demands
12
10
8
6
4
2
0
5
20
35
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65
80
95
0
11
5
12
0
14
5
15
0
17
5
18
0
20
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MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY
Backbone Traffic
Inference
Objective coefficients = number of hops
% demands (cumul)
120
100
80
60
40
20
0
5
20
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50
65
80
95
0
1
1
5
2
1
0
4
1
5
5
1
0
7
1
5
8
1
0
0
2
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MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY
Backbone Traffic
Inference
All objective coefficient = 1
% demands
70
60
50
40
30
20
10
0
5
20
35
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65
80
95
0
11
5
12
0
14
5
15
0
17
5
18
0
20
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MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY
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Inference
Netflow turned on on five random routers
% demands
35
30
25
20
15
10
5
0
5
20
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50
65
80
95
0
11
5
12
0
14
5
15
0
17
5
18
0
20
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MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY
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Inference
Netflow turned on on five most used routers
% demands
70
60
50
40
30
20
10
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0
18
5
17
0
15
5
14
0
12
5
11
0
95
80
65
50
35
20
5
0
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Inference
Netflow turned on on ten random routers
% demands
70
60
50
40
30
20
10
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0
18
5
17
0
15
5
14
0
12
5
11
0
95
80
65
50
35
20
5
0
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Inference
Comparison of different results
70
60
50
40
30
20
10
0
5
10
15
20
25
30
35
40
45
coeff 1
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# hops
55
60
rand net
65
70
75
80
85
90
95
100
opt net
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MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY
Backbone Traffic
Inference
Conclusions
Objective coefficients in LP need to be scaled
Turning NetFlow on a few selected interfaces can
greatly improve the traffic estimates.
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