Prediction models for long-term Internet prefix availability
The Border Gateway Protocol (BGP) maintains inter-domain routing information by announcing and withdrawing IP prefixes. These routing updates can cause prefixes to be unreachable for periods of time, reducing prefix availability observed from different vantage points on the Internet. The observed prefix availability values may not meet the standards promised by Service Level Agreements (SLAs).
In this paper, we develop a framework for predicting long-term availability of prefixes, given short-duration prefix information from publicly available BGP routing databases like RouteViews, and prediction models constructed from information about other Internet prefixes. We compare three prediction models and find that machine learning-based prediction methods outperform a baseline model that predicts the future availability of a prefix to be the same as its past availability. Our results show that mean time to failure is the most important attribute for predicting availability. We also quantify how prefix availability is related to prefix length and update frequency. Our prediction models achieve 82% accuracy and 0.7 ranking quality when predicting for a future duration equal to the learning duration. We can also predict for a longer future duration, with graceful performance reduction. Our models allow ISPs to adjust BGP routing policies if predicted availability is low, and are generally useful for cloud computing systems, content distribution networks, P2P, and VoIP applications.
Prefix availability; Border Gateway Protocol (BGP); Prediction; Machine learning
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