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ISP Backbone Traffic Inference Methods to Support Traffic Engineering. Olivier Goldschmidt Senior Network Consultant. 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
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ISP Backbone Traffic Inference Methods toSupport Traffic Engineering Olivier Goldschmidt Senior Network Consultant
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
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.
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
Problem Description Objective of traffic inference is to "guess" end to end aggregate traffic using limited information.
Inputs to the model Deterministic Information Measured Information Usage Information
DETERMINISTIC INFORMATION Network Topology Types of routers and links Routing paths between end points
MEASURED INFORMATION Baselining Information on network interfaces using SNMP Partial RMON/RMON2 information using selective probes (NetFlow or IP account.)
USAGE INFORMATION Data that can be correlated with the traffic on the network Allows to derive additional constraints on the network traffic.
WAN Link Ingress-Egress points Internal routers
3 3 Assume that reading are symmetric. 3 3 Interface flow reading
3 3
1 2 1 2
3 2 1 1 2 3 3 3
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
10 10 10 EXAMPLE Assume these are the true traffic demands Notice that all interface flows are equal to 20
0 20 20 20 0 0 If all objective coefficients are equal to 1 If objective function is maximized If objective function is minimized
10 2 10 10 1 1 But if coefficient are equal to the number of hops of demand route Is a solution whether objective function is maximized or minimized
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.
Test Results NETWORK • 60 Routers • 114 WAN Links • 529 Traffic demands • Bandwidth from 0 to 256 Kbps
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
Conclusions Objective coefficients in LP need to be scaled Turning NetFlow on a few selected interfaces can greatly improve the traffic estimates.