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Toward a Fair and Robust Internet Backbone Routing Jerry Chou, UC San Diego Bill Lin, UC San Diego Shubho Sen, AT&T Labs Oliver Spatscheck, AT&T Labs Outline • Problem & Motivation • Current Solutions • Experimental Results • Discussion & Conclusion UC Symposium, August 4, 2006 @ AT&T 2 Same Old Problem • Robustness • Efficiency – Support variable traffic – Fault tolerance • Fairness – High throughput – Minimize network resources – Fairness policy (max-min/ proportion) Internet Backbone Network R ISP R R ISP R R R UC Symposium, August 4, 2006 @ AT&T R R R R R R R ISP R Customer Network 3 Getting Worse… • Traffic is rapidly changing – New applications: P2P applications → Unpredictable traffic – New → Growing traffic Lowcustomers efficiency – New technology: Broadband/optical networks to homes No performance guarantee → move bottleneck toward core network Weak robustness • Routing is still OSPF – Basic approach • Limited computational power on routers • Distributed environment – Topology dependent • Traffic engineered network • Over provision UC Symposium, August 4, 2006 @ AT&T 4 Goals • Classify routing algorithm • Study the design decision of routing • Evaluate and compare routing algorithms • Design a better routing algorithm UC Symposium, August 4, 2006 @ AT&T 5 Outline • Problem & Motivation • Current Solutions • Experimental Results • Discussion & Conclusion UC Symposium, August 4, 2006 @ AT&T 6 Two Problem Formulations • Optimize for a given traffic profile – Use state-of-the-art network measurement techniques to measure traffic conditions – Find at run-time optimal path load-distribution for measured traffic profile – No need to change previous path load-distribution if it can handle current traffic condition – only change when necessary • Optimize for worst-case, assuming all “admissible” traffic profiles are possible – Determine optimal path load-distribution under this worst-case model once offline UC Symposium, August 4, 2006 @ AT&T 7 Optimizing for Specific Traffic Profile • Maximum Concurrent Flow Model – Use a centralized control plane model – e.g. AT&T’s RCP proposal – Periodic traffic measurements and topology changing reported back to central controller – For given traffic matrix T, find optimal path loaddistribution on topology graph G. R R R R R R R R UC Symposium, August 4, 2006 @ AT&T 8 Optimize for Worst-Case Traffic • 2Phase on Hose Model: – Ri = max ingress traffic at node i, – Ci = max egress traffic at node i Ri Ci – Traffic matrix T is admissible iff • Sj tij <= Ri , Sj tji <= Ci , for all i • Set of all such traffic matrices denoted by t ( R , C ) – Routing algorithm • Phase1 splits traffic to all nodes • Phase2 redirects traffic to destination UC Symposium, August 4, 2006 @ AT&T 9 Outline • Problem & Motivation • Current Solutions • Experimental Results • Discussion & Conclusion UC Symposium, August 4, 2006 @ AT&T 10 Routing Algorithm Explore • Open Shortest Path First (OSPF) [1] – Route all flows with the same source and destination on a single shortest path • Load-balanced OSPF (LOSPF) [1] – Evenly distribute the load of a flow in packet level on all shortest paths between its source and destination • Maximum Concurrent Flow Routing [2] – Route flow based on maximum concurrent flow problem formulation for a given traffic matrix and topology • 2PHASE [3] – Phase1 splits flows to all nodes with a fix ratio determined by the access capacity – Phase2 redirects flows to their destination nodes UC Symposium, August 4, 2006 @ AT&T 11 Initial Experimental Results • Testbed: – Large ISP network backbone changes very day – •Topology Degree-based Topology Generator [4] – •Traffic Random Graph Generator changes every hour[5] • Matrix – Link Load: Lambda = 2 traffic load on a link link capacity 50% 40% 1 – Lambda: max link load among all links – Throughput: Total traffic comes out the network Total traffic goes in the network – Cost: Additional capacity required to support a traffic UC Symposium, August 4, 2006 @ AT&T 12 Lambda Lambda 3 MCF LOSPF 2PHASE OSPF 2.5 2 1.5 1 0 4 8 12 16 20 24 28 32 36 40 44 48 Hour (from 06/28/06 00am) • • • • In general: MCF > LOSPF > 2Phase > OSPF Topology is traffic engineered for OSPF 2Phase guarantees ONLY 10% of the worse case traffic 2Phase has higher link load due to long routing path UC Symposium, August 4, 2006 @ AT&T 13 Throughput (%) Throughput 100 95 90 85 80 75 70 65 60 MCF LOSPF 2PHASE OSPF 1 • • • • • 3 5 Load Factor We increase the demand amount per flow by load factor The bar specifies the average throughput in 2days period Error bar is the max and min throughput in 2days period 2Phase has the largest variation MCF always maintains high throughput UC Symposium, August 4, 2006 @ AT&T 14 Future Plan & Discussion • For MCF: – How often to perform optimization? – What is the cost to reconfigure the routing? • For 2Phase: – How to formulate a more strict worse case? • By access capacity, core link capacity, demand amount or something else? • For OSPF: – What’s the general topology for networks? – What’s the impact of topology to those routing algorithm? UC Symposium, August 4, 2006 @ AT&T 15 Reference [1] “Configuring OSPF”, Cisco Systems Prgoduct Doc. [2] G. Karakostas, “Faster Approximation Schemes for Fractional Multicommodity Flow Problems”. [3] M. Kodialam, T. V. Lakshman, J. B. Orlin and S. Sengupta, “A Versatile Scheme for Routing Highly Variable Traffic in Service Overlays and IP Backbones”. [4] C. Jin, Q. Chen, and S. Jamin, “Inet: Internet Topology Generator”, Technical Report CSE-TR443 -00, Department of EECS, University of Michigan, 2000. [5] B. M. Waxman. Routing of multipoint connections. IEEE Journal of Selected Areas in Communications, pages 1617--1622, 1988. UC Symposium, August 4, 2006 @ AT&T 16 Backup Slides UC Symposium, August 4, 2006 @ AT&T 17 Two Phase Routing • Approach – Phase1 splits traffic to all nodes by a fix ratio – The ratio is determined by the access capacity – Phase2 delivers demand to destination Logic Network: R1 • Support all valid traffic matrix – High fault tolerance • Cons: – Long path: 2*network diameter =>Waste network capacity – Access capacity >> link capacity UC Symposium, August 4, 2006 @ AT&T l l=2(r/N) R2 l R3 r • Pros: – Independent of traffic r r Logic Routing Path: r r/N R1 R1 r r R2 R3 R2 r/N R1 R2 R3 R3 Phase1 Phase2 18