Inductive knowledge acquisition: a case study
Proceedings of the Second Australian Conference on Applications of expert systems
C4.5: programs for machine learning
C4.5: programs for machine learning
End-to-end routing behavior in the Internet
IEEE/ACM Transactions on Networking (TON)
On the constancy of internet path properties
IMW '01 Proceedings of the 1st ACM SIGCOMM Workshop on Internet Measurement
BGP routing stability of popular destinations
Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment
End-to-end WAN service availability
IEEE/ACM Transactions on Networking (TON)
Measuring the effects of internet path faults on reactive routing
SIGMETRICS '03 Proceedings of the 2003 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
The Temporal and Topological Characteristics of BGP Path Changes
ICNP '03 Proceedings of the 11th IEEE International Conference on Network Protocols
Locating internet routing instabilities
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
A measurement framework for pin-pointing routing changes
Proceedings of the ACM SIGCOMM workshop on Network troubleshooting: research, theory and operations practice meet malfunctioning reality
Providing end-to-end service level agreements across multiple ISP networks
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Internet economics: Pricing and policies
Identifying BGP routing table transfers
Proceedings of the 2005 ACM SIGCOMM workshop on Mining network data
A measurement study on the impact of routing events on end-to-end internet path performance
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
BGP routing dynamics revisited
ACM SIGCOMM Computer Communication Review
Finding a needle in a haystack: pinpointing significant BGP routing changes in an IP network
NSDI'05 Proceedings of the 2nd conference on Symposium on Networked Systems Design & Implementation - Volume 2
iPlane: an information plane for distributed services
OSDI '06 Proceedings of the 7th symposium on Operating systems design and implementation
Studying black holes in the internet with Hubble
NSDI'08 Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation
iPlane Nano: path prediction for peer-to-peer applications
NSDI'09 Proceedings of the 6th USENIX symposium on Networked systems design and implementation
Internet optometry: assessing the broken glasses in internet reachability
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Predicting prefix availability in the internet
INFOCOM'10 Proceedings of the 29th conference on Information communications
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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.