1. Scalable Construction of Resilient Overlays using Topology Information Mukund Seshadri (mukunds@cs.berkeley.edu) Dr. Randy Katz

2. Background BGP can take several minutes to recover from failures [Labovitz et al.] Partly due to sub-optimal configuration of hold parameters Partly inherent, due to exploration of a large number of paths Can take up to 20 min to recover Overlay Networks have been proposed to route around failures quickly Resilient Overlay Networks (RON) [MIT] claims ~20s recovery Maintains and routes through complete overlay graph One intermediate hop enough to route around BGP failures Not scalable (~50 nodes)

3. Goal Construct overlays with low maintenance overhead (=>lower no. of overlay edges), and still recover quickly from route failures. Scale to ~1000 nodes One intermediate hop If the default route between A and B fails, how many intermediate hop choices do we really need? Can using topology information help?

4. Focus Only consider AS-level failures and topology. Intra-AS convergence known to be much faster than BGP Assume single link failure (AS or inter-AS). Assume that, for a given source node, the probability of requiring a route to any destination overlay node is equally likely.

5. Approach Node A joining the overlay selects a set of other nodes (peering set S) of size K. No overlay-level routes are distributed If default route fails route through one of the K nodes Try the elements of K in series or in parallel. At least one of the nodes in K should serve as a successful intermediate hop, with high probability (1-P f ). K has to be small, and should be selected to minimize the failure probability P f. Or, solve the dual problem

6. Random Peer Selection Assume that S knows all nodes in the overlay Peering set is chosen randomly Consider a naïve topology: 1000 nodes, 2 independent symmetric paths (i.e. two physical first-hops) from each node, default routes to all destinations from S evenly distributed across the two first hops Random selection should reduce failure probability below 1% with around 15 peers. But the actual BGP topology does not fit these parameters neatly.

7. Static Simulation Construct AS-level topologies and default paths from BGP Routeviews data. Inaccuracies due to symmetry assumption and hidden edges Sample ~1000 prefixes, and assume each prefix represents an overlay node. Evaluation Criteria: K, given P f ; P f given K. Graphs depict normalized values of P f

8. Random Selection Performance Acceptable for moderate failure probabilities (K~50, P f =30%) Requires much larger K for lower values of P f Since distribution of default routes over available routes is uneven.

9. Using Diversity Diversity-based selection Assumes complete knowledge of underlying paths Using BGP table dumps or traceroutes Requires n^2 traceroutes or BGP advertisements for prefixes containing the n overlay node. Greedy selection: add peer p to S such that such that Σ B Route(A->p->B) adds the most number of edges not covered by set S. Motivated by set covering in the “naïve” example earlier.

10. Greedy Selection Results For a given probability, greedy selection yields much lower set size. Still difficult achieve very low P f Nodes where the default route from almost all destinations contain a particular edge pose a problem

11. Improvements Greedy with “minimal intersection” criterion. The greedy method requires complete topology information – not scalable if using traceroutes Hybrid method: random selection followed by greedy refinement If “Random” has small set size, further refinement doesn’t help If “Random” has a large set size (i.e., lower desired P f ) this is more effective; E.g. “random” set 50 reduced by refinement to ~40.

12. Future Work More investigation of hybrid selection Can nodes share their peering sets? Study load balance issues here More accurate topologies Multi-path application over diverse paths Practical methods of obtaining topology information Which is better: BGP dumps.vs. Traceroute

13. Feedback? Failure models/probabilities for ASes or inter-AS links Accurate inter-AS topology estimation