SNMP - Simple Network Measurements Please!
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Transcript SNMP - Simple Network Measurements Please!
An Information-Theoretic
Approach to Traffic Matrix
Estimation
Yin Zhang, Matthew Roughan, Carsten Lund – AT&T Research
David Donoho – Stanford
Shannon Lab
AT&T Labs - Research
Problem
Have link traffic measurements
Want to know demands from source to destination
B
C
A
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TM
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AT&T Labs - Research
x A, B
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x A ,C
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Approach
Principle *
“Don’t try to estimate something
if you don’t have any information about it”
Maximum Entropy
Entropy is a measure of uncertainty
More information = less entropy
To include measurements, maximize entropy subject to the
constraints imposed by the data
Impose the fewest assumptions on the results
Instantiation: Maximize “relative entropy”
Minimum Mutual Information
AT&T Labs - Research
Results – Single example
±20% bounds for larger flows
Average error ~11%
Fast (< 5 seconds)
Scales:
O(100) nodes
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Other experiments
Sensitivity
Very insensitive to lambda
Simple approximations work well
Robustness
Missing data
Erroneous link data
Erroneous routing data
Dependence on network topology
Via Rocketfuel network topologies
Additional information
Netflow
Local traffic matrices
AT&T Labs - Research
Conclusion
We have a good estimation method
Robust, fast, and scales to required size
Accuracy depends on ratio of unknowns to measurements
Derived from principle
Approach gives some insight into other methods
Why they work – regularization
Should provide better idea of the way forward
Implemented
Used in AT&T’s NA backbone
Accurate enough in practice
AT&T Labs - Research