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Flow-Based IGP Metric Tuning for Maximal Network Utilization

Learn how to optimize your network infrastructure by using flow information to tune your IGP metrics, without the complications of MPLS. This presentation introduces the concept and provides insights into achieving optimal network flows.

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Flow-Based IGP Metric Tuning for Maximal Network Utilization

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  1. Intra-Domain TE via IGP Metric Tuning

  2. Who I Am • Andrew Lange • Exodus, a Cable & Wireless Service • Principal Network Architect • andrewl@exodus.net/andrewl@cw.net • Successfully navigated the straights of Chapter 11, between Scylla & Charybdis...and somehow ended up in Britain.

  3. What this IS • This IS an introduction to the wonderful world of using flow information to tune your IGP metrics.

  4. What this is NOT • This is NOT an end-all be-all guide to how to optimize your IGP metrics.

  5. Problem We're Trying to Solve • How can we maximally* utilize our network infrastructure, without adding the complications associated with MPLS? • Can this even be done? • Well, of course it can, or this presentation would be remarkably short. • Why? • The more we can get out of our network the more cost effective it becomes, and the happier the finance people get. Plus, it's cool.

  6. *Maximally • Maximally is a thorny term. Long story short: • Optimum network flows can be represented as shortest paths with respect to a set of positive link weights (Wang, Wang & Zhang). • With current IGP's, determining optimum is NP-Hard, BUT, very close (within 3%) approximations can be made (Fortz & Thorup).

  7. Scholarship Abounds • The first concrete way of doing this that we ran across was in Fortz & Thorup's paper Internet Traffic Engineering by Optimizing OSPF Weights. • This literature tends to be quite recent (1999 and newer). • How easy it is to determine the optimal values very much depends on what your network and flows look like.

  8. What is Required • Accurate flow data between each set of backbone nodes. • An optimization routine to apply to the flow data.

  9. Getting the Flow Data

  10. Getting the Data - Tool Selection • Using Netflow is the only way to gather a traffic matrix without using an overlay design. • Looked at a variety of options, including building our own, and settled on the Ixia (nee Caimis) product.

  11. Getting the Data - Our Issues • Vendor HW/SW combinations are not always supported with the netflow feature set. • Full deployment of the tools is pending operational deployment of the right code base. • Sampling rate needs to be grossed up.

  12. Getting the Data - Configuration • How have we configured our collectors? • Data is collected on the interfaces inbound into the backbone routers from the datacenter. • Flow data is sampled at 1:100. • Collectors peer with the backbone routers as route-reflector clients. • Collectors gather, among other things, BGP NextHop information.

  13. Node Overview

  14. Munging the Data- Basics • How do we process the collected data? • Data is summarized daily. • To assemble the flow matrix, data is aggregated across the interfaces and the routers for a given site. There are some problems with this.

  15. Munging the Data - Problems • Data is an average, peak utilization is not available. This is probably ok for this application, since average and peak tend to follow the same proportions. But we're working on getting peak to compare the results using that data. • Assumes both routers function as one (Nodal Aggregation). This is useful to simplify things as we first work out the models, but we will need to get more detailed as the models are refined.

  16. Munging the Data - Aggregating the Flows • Aggregated daily summaries are post processed with a script that correlates BGP NextHop with destination datacenter and combines the flows destined for that datacenter. • Currently does not gross up flow size to compensate for sampling.

  17. Flow Matrix - Example (sntc08)

  18. Building a Model

  19. Offline vs. Online • We have chosen to pursue offline metric optimization. • Online, or dynamic, metric optimization imposes a whole other set of requirements, such as speed of the optimization model, and lot of trust. • At least at this point, our target for intra-domain TE is in the medium/long term timeframe. If we are running our network so hot that we have to reoptimize multiple times a day, then we need more bandwidth.

  20. Modeling Assumptions • Model assumes that when flows grow the proportions remain the same. • Model does not take flow splitting (ECMP) into account currently. Except ECMP between two adjacent nodes, which is represented by increasing the size of the link between them in the model.

  21. What follows is an Example • 10 nodes, 15 links (30 arcs). • 10 demand sets. • Real backbone network would be more complicated, but findings still hold.

  22. Example Data • Because we were not able to poll the full matrix of data from the network, the data we're using for this example is extrapolated from the flow data we do have. It is only approximate.

  23. Example Network Diagram

  24. Example Network Info • All links are OC48. • There are no nodal constraints (i.e. Routers are assumed to be able to push line rate.)

  25. Base Demands

  26. Current Metrics • Current Metrics are agnostic to flow information (based on RTT between nodes). • Under current loads the example network is nicely overprovisioned. • We're going to focus on how much more load we can put on this network before any link exceeds 80% utilization (to allow for microbursts, etc.) This is 1990 Mbps for an OC48. We are going to do this by increasing the values of the Base Demands.

  27. Shortest Paths - Current Metrics

  28. Link Loading - Current Metrics

  29. Abracadabra! • Sample run of one of the models: • ampl: model fixtwo-int.mod; data cap-3.3.dat; solve; • CPLEX 7.1.0: optimal integer solution; objective 12909.92 • 31 MIP simplex iterations • 0 branch-and-bound nodes

  30. A Bit Behind the Curtain • Using AMPL/CPLEX to define the models. • This consists of a model file specifying the model's: • Objective (e.g. Minimize overall network load). • Constraints (e.g. Do not exceed capacity on links.) • And a data file, which specifies: • What the network looks like. • What the demands are.

  31. Shortest Paths - New Metrics

  32. Link Loading - New Metrics

  33. Resources and Thanks

  34. Optimization Resources - Papers • Sample Papers: • Internet Traffic Engineering by Optimizing OSPF Weights, Fortz & Thorup. • Internet Traffic Engineering without Full Mesh Overlaying, Wang, Wang & Zhang. • Dynamic Optimization of OSPF Weights using Online Simulation, Ye, et. al.

  35. Optimization Resources - Tools • Mathematical Programming Tools • AMPL/CPLEX (www.ampl.com) • OPL/CPLEX (http://www.ilog.com/products/oplstudio/)

  36. Special Thanks To • Dr. Irv Lustig, for invaluable help with the modeling languages.

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