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A Case Study in Understanding OSPFv2 and BGP4 Interactions Using Efficient Experiment Design David Bauer†, Murat Yuksel‡, Christopher Carothers† and Shivkumar Kalyanaraman‡ †Department of Computer Science ‡Department of Electrical, Computer and Systems Engineering Rensselaer Polytechnic Institute Parameter Space: fixed inputs, protocol timers, decision algorithm Design Complexity Problem Statement Computational Complexity Models: BGP4, OSPFv2, TCP-Reno, IPv4 ROSS.Net built and utilized to address both parts of the problem Goal: “good results fast” leading to an understanding of the system under test (make sense of the results) Response Surface OSPFv2 Understand protocol interactions through UPDATE messages generated by and between protocols OO: OSPF caused OSPF Updates BO: BGP caused OSPF Updates INTERACTION BGP4 BB: BGP caused BGP Updates OB: OSPF caused BGP Updates INTERACTION Why Are Feature Interactions Harmful? Network protocol weaknesses are not fully understand until implemented / simulated in the large-scale Are decisions made to efficiently route data within a domain adversely affecting our ability to efficiently route data across the domain? Hot-potato routing: small degree of unstable information affects large portion of traffic Cold potato routing AS 0 AS 1 AS 2 Local Policy: Global Policy:optimize optimizerouting routingbetween within withinAS and ASes (OSPFv2) between (BGP4)ASes Large-scale Simulation Topology from Rocketfuel data Network Hierarchy: – – – – – – EBONE: AS 1755 iBGP: 16,384 Level 0 routers: 9.92 Gb/sec and 1 ms delay Level 1 routers: 2.48 Gb/sec and 2 ms delay Level 2 routers: 620 Mb/sec and 3 ms delay Level 3 routers: 155 Mb/sec and 50 ms delay Level 4 routers: 45 Mb/sec and 50 ms delay Level 5 routers and below: 1.55 Mb/sec and 50 ms delay 12 EXODUS: AS 3967 iBGP: 50,176 OSPFv2: Routers: 438 Links: 1,192 eBGP: 53 OSPFv2: Routers: 688 Links: 2,166 12 ABOVENET: AS 6461 iBGP: 2,500 26 LEVEL 3: AS 3356 iBGP: 7,921 OSPFv2: Routers: 2,064 Links: 8,669 eBGP: 210 12 9 eBGP: 199 Tiscali: AS 3257 iBGP: 441 OPSFv2: Routers: 843 Links: 2,667 11 OSPFv2: Routers: 618 Links: 839 eBGP: Experiment Design and Analysis Three classes of protocol parameters: – OSPF timers, BGP timers, BGP decision RRS was allowed 200 trials to optimize (minimize) response surface – Heuristic search algorithm Applied multiple linear regression analysis on the results Response Plane Intra-domain routing decisions can effect inter-domain behavior, and vice versa. OB Update Destination All updates belong to either of four categories: – – – – OSPF-caused OSPF (OO) update OSPF-caused BGP (OB) update – interaction BGP-caused OSPF (BO) update – interaction BGP-caused BGP (BB) update Link failure or cost increase (e.g. maintenance) 8 10 Response Plane Intra-domain routing decisions can effect inter-domain behavior, and vice versa. All updates belong to either of four categories: – – – – OSPF-caused OSPF (OO) update OSPF-caused BGP (OB) update eBGP connectivity becomes available BGP-caused OSPF (BO) update BGP-caused BGP (BB) update These interactions cause route changes to thousands of IP prefixes, i.e. huge traffic shifts!! BO Update Destination High Level Characterization ~15% improvement when BGP Optimized with respect to OB+BO response surface. timers included in search space BGP timers play the major role, i.e. ~15% improvement in the optimal response. – BGP KeepAlive timer seems to be the dominant parameter.. – in contrast to expectation of MRAI! OSPF timers effect little, i.e. at most 5%. – low time-scale OSPF updates do not effect BGP. Design 1: Mgt Perspectives Minimize total BO+OB to 15-25% Important optimize better than other metrics OSPF Varied response surfaces -- equivalent to a particular management approach. Importance of parameters differ for each metric. OB: ~50% of total updates For minimal total updates: – Local perspectives are 20-25% worse than the global. BO: ~0.1% of total updates For minimal total interactions: – 15-25% worse can happen with other metrics OB updates are more important than BO updates (i.e. ~0.1% vs. ~50%) Global perspective 20-25% better than local perspectives Design 2: Hot- v Cold-Potato Routing Q: Can we use this approach to provide guidance for network routing policies? No major impact regardless of search Performed full factorial of performed RRS searches, turning Hot-, Cold-potato routing ON/OFF Provide quantitative results from which Majority of UPDATEs were qualitative stmts can be made by LOCAL-Pref generated andVerified AT&T and Sprint AS Path length measurements MED was << 1% of UPDATEs Hot Potato was 0.8% Larger question: Which steps in the BGP decision making algorithm are most important? Design 3: Network Robustness Response tied to link stability BGP parameters had greatest impact Q: Can we use this approach to provide network admins with guidance for network configurations? Link status varied with uniform random probability over simulation runtime Link weights varied with uniform random probability over simulation runtime Response: BO + OB, Global Persp, and Default network settings Search consistently provides better results By maximizing link failure detection times, UPDATEs most effectively minimized Conclusions – Number of experiments were reduced by many orders of magnitude in comparison to Full Factorial – Experiment design and statistical analysis enabled rapid elimination of insignificant parameters – Several qualitative statements and system characterizations could be obtained with few experiments. – Provided validation of network measurement community results, and called into question importance of premises – Search algorithms do not always find desired behaviour ! Allowed me to complete my thesis and graduate!