1 A Framework for Measuring and Predicting the Impact of Routing Changes Ying Zhang Z. Morley Mao Jia Wang

2 Internet routing changes  Various causes  Link failures, configuration changes, topology changes, etc.  Direct influence on the data plane  Transient data-plane disruption  Packet loss, increased delay, forwarding loops Internet C BR C C Destination Source Old path New path C BR C C C C

Motivation  Frequent routing dynamics can cause transient disruption in the data plane  Inconsistent routes during convergence  Real-time applications can be affected  Predicting performance impact can assist more intelligent route selection 3

Measuring and predicting the impact  Comprehensively measure the impact of routing changes  Characterize the properties of routing changes that cause traffic disruption  Search for pattern to help prediction 4

Outline  Motivation  Methodology  Characterization of data-plane failures  Failure prediction model 5

Methodology  Data collection  Control plane: local real-time BGP updates  Data plane: ping and traceroute probes for each update  A light weight active probing methodology  A coarse-grained performance metric: reachability  Destination reachable: any ping reply  Scalable to many destinations with live IPs  Measurement-based approach  No simplifying assumptions  Empirical evidence 6

Our approach  Focus: measure data-plane failures caused by routing changes  Coarse-grained performance metrics  Methodology: light-weight active probing  Triggered by locally observed routing updates  Probing target of a live IP within the prefix 7 Prefix P Old path New path C BR AS C Update Prefix: P, AS path: A D B C BR AS B AS A C BR AS D Measurement Framework Internet

Our approach  Focus: measure data-plane failure caused by routing changes  Methodology: light-weight active probing  Triggered by locally observed routing updates  Probing target of a live IP within the prefix 8 Live IP 1 within Prefix P Old path New path C BR AS C Ping C BR AS B AS A C BR AS D Measurement Framework Internet Traceroute Ping, traceroute

Probing control  Background probing  Identifying persistent failures  Verifying live IP’s response  Resource control  Ignoring updates due to table transfers  Imposing maximum probing duration  Accuracy control  Impose maximum waiting duration 9

Outline  Motivation  Methodology  Characterization of data-plane failures  Failure prediction model 10

Characterization of data-plane failures  Failure types  Reachability failure  Ping reply is not received due to network problems  Forwarding loops  A subset of reachability failures  Transient loops observed in the path  Failure properties  Affected networks  Failure duration  Failure predictability 11

Overall reachability failure statistics 12 IncidencePrefixAS Unreachable Loop6%23%33% Other36%72%38% All42%73%63% Reachable57%83%98% Internet experiments for 11 weeks

Affected network locations  Understanding the networks affected by routing changes  Most Ases are near the edge and in foreign countries  Small fraction of destinations experiencing many unreachable incidences 13

Failure durations  Short duration  Most last less than 300 seconds  Transient routing failure, convergence delay  10% incidences with longer duration  Configuration errors or path failures 14

Failure predictability  Destination prefix information  Appearance probability  Probability of an unreachable incidence for prefix D  Destination prefix and AS path segments  Conditional probability on AS path segments  Probability of an unreachable event occurring given a particular AS path segment  Responsible AS  Where traceroute stops 15

Outline  Motivation  Methodology  Characterization of data plane failure  Failure prediction model 16

Prediction model  Prefix and AS segment information  The data plane failure likelihood ratio  P(Y=1|R;D): the conditional probability of data-plane failure given a routing update R for prefix D  Assuming the failure on each AS is independent x i is the responsible AS in history data x i is the responsible AS in history data 17

Evaluation  The trade-off between selectivity and sensitivity  is the decision threshold which determines false positives and false negative route  Receiver operating characteristic  Evaluation results  60% detection rate with 18% false positives 18

Conclusion  Developed an efficient framework for measuring and predicting data-plane failures caused by routing changes  Identified patterns to accurately predict data-plane failures  Provided suggestions for more intelligent route selections 19