1. Network Sensitivity to Hot-Potato Disruptions Renata Teixeira (UC San Diego) http://www-cse.ucsd.edu/~teixeira with Aman Shaikh (AT&T), Tim Griffin(Intel), and Geoff Voelker (UCSD) SIGCOMM’04 – Portland, OR

2. SIGCOMM’04 2 Internet Routing Architecture UCSD Sprint AT&T Verio AOL interdomain routing (BGP) intradomain routing (OSPF,IS-IS) User Web Server End-to-end performance depends on all ASes along the path Changes in one AS may impact traffic and routing in other ASes

3. SIGCOMM’04 3 Hot-Potato Routing San Francisco Dallas New York Hot-potato routing = route to closest egress point when there is more than one route to destination ISP network 9 10 dst multiple connections to the same peer -All traffic from customer to peers -All traffic to customer prefixes with multiple connections

4. SIGCOMM’04 4 Hot-Potato Disruption San Francisco Dallas New York ISP network dst 9 10 - failure - planned maintenance - traffic engineering 11 Routes to thousands of destinations switch exit point!!! 11

5. SIGCOMM’04 5 Consequences of Hot-Potato Disruptions  Transient forwarding instability  Up to three minutes convergence delay  Normal internal changes take a couple of seconds  Traffic shift  Responsible for largest traffic matrix variations  Interdomain routing changes  Around 2 – 5% of a router’s external BGP updates

6. SIGCOMM’04 6 What to do about it?  Engineer network to minimize disruptions  Network operator: operational practices to avoid changes  Network designer: designs that minimize sensitivity  Need a vocabulary and metrics to evaluate impact of internal changes  Compare possible network designs  Identify critical events  Take special care during maintenance or traffic engineering

7. SIGCOMM’04 7 Modeling Hot-Potato Routing  Model of egress selection in backbone networks  Internal topology and link weights  Set of egress routers for each destination prefix  Apply topology changes  Link or router failures  Link weight changes  Evaluate impact of topology changes  For a router what fraction of prefixes shifts  Most critical link failure  …

8. SIGCOMM’04 8 Modeling Egress Selection A B C D G E F 4 5 11 3 9 3 4 10 8 6 8 dst Egress set for a destination prefix (dst) = set of border nodes that learn routes to dst ({A,B}) A B Region of egress node A = nodes that are closer to A than B Region of A Region of B

9. SIGCOMM’04 9 Modeling Topology Changes C D G E F 4 5 11 3 9 3 4 10 8 6 8 Region of A Region of B A B Topology change = edge or node deletion, link weight change dst C D G E F 4 5 11 3 9 3 4 10 8 6 8 Region of A Region of B A B C shifts from region of A to B dst

10. SIGCOMM’04 10 Generalizing to All Prefixes  Fraction of prefixes at a router that change egresses after a single topology change  Routing-shift function (H RM ) A B C D G E F 4 5 11 3 9 3 4 10 6 6 8 A B X (10,000 prefixes) Z (4,000 prefixes) Y (1,000 prefixes) Routing-shift at C when CF is deleted = 10,000/15,000 (i.e. 2/3)

11. SIGCOMM’04 11 All Prefixes, Routers, and Topology Changes routers topology changes C failure of CF fraction of prefixes at C that changes egress after the failure of link CF: 2/3 routing-shift function

12. SIGCOMM’04 12 Node Routing Sensitivity Metrics (  RM ) routers topology changes C  Node routing sensitivity  Expected fraction of route shifts experienced by a node  Worst case  Maximum route shift experienced by a node

13. SIGCOMM’04 13 Routing Impact of a Graph Transformation (  RM )  Impact of graph transformations  Average fraction of route shifts across all nodes  Worst case  Maximum route shift caused by each graph transformation routers topology changes failure of CF

14. SIGCOMM’04 14 Case Study: A Large ISP Backbone Network  Obtaining input for the model  Topology – intradomain routing messages  Egress sets – collection of BGP tables  Set of graph transformations Single link failures Single router failures  Probability distribution for graph transformations Uniform

15. SIGCOMM’04 15 Order failures according to average impact Which failures are most disruptive? routers single router failures fraction of failures Routing Impact of Failures router failures link failures Most failures cause no hot-potato disruptions Operators can focus on most disruptive failures

16. SIGCOMM’04 16 Which routers are most sensitive? routers single router failures Order routers according to average sensitivity router failures link failures fraction of routers Node Routing Sensitivity Very few hot-potato changes on average, but there are many failures that cause no shift High variance among routers

17. SIGCOMM’04 17 What is the largest routing shift for each router? routers single router failures or single link failures Order routers according to worst case sensitivity Worst Case Node Routing Sensitivity fraction of routers Very disruptive failures for some routers

18. SIGCOMM’04 18 Conclusion  Contributions  Model of hot-potato disruptions  Basis for a sensitivity analysis tool  Robustness should be a first-order metric  As important as traditional performance metrics  Network should have small reactions to small changes  Two approaches  Engineer the system: our model  Redesign routing interaction: on-going work

19. SIGCOMM’04 19 Single Link vs. Single Router Failures A B C D E dst 10 1000 10 20

20. SIGCOMM’04 20 Single Link vs. Single Router Failures A B C D E dst 10 1000 10 20

21. SIGCOMM’04 21 Minimizing Disruptions 5 5 10 4  Reconfiguration of routing protocols  Link and node redundancy  Selection of peering locations