The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Rethinking the design of the Internet: the end-to-end arguments vs. the brave new world
ACM Transactions on Internet Technology (TOIT)
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
King: estimating latency between arbitrary internet end hosts
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FDNA '03 Proceedings of the ACM SIGCOMM workshop on Future directions in network architecture
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Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
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ACM SIGCOMM Computer Communication Review
Meridian: a lightweight network location service without virtual coordinates
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
Geographic locality of IP prefixes
IMC '05 Proceedings of the 5th ACM SIGCOMM conference on Internet Measurement
Predicting short-transfer latency from TCP arcana: a trace-based validation
IMC '05 Proceedings of the 5th ACM SIGCOMM conference on Internet Measurement
A machine learning approach to TCP throughput prediction
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Support vector regression for link load prediction
Computer Networks: The International Journal of Computer and Telecommunications Networking
An internet protocol address clustering algorithm
SysML'08 Proceedings of the Third conference on Tackling computer systems problems with machine learning techniques
End-to-end quality of service seen by applications: A statistical learning approach
Computer Networks: The International Journal of Computer and Telecommunications Networking
A machine learning approach to TCP throughput prediction
IEEE/ACM Transactions on Networking (TON)
Predicting network throughput for grid applications on network virtualization areas
Proceedings of the first international workshop on Network-aware data management
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We examine the ability to exploit the hierarchical structure of Internet addresses in order to endow network agents with predictive capabilities. Specifically, we consider Support Vector Machines (SVMs) for prediction of round-trip latency to random network destinations the agent has not previously interacted with. We use kernel functions to transform the structured, yet fragmented and discontinuous, IP address space into a feature space amenable to SVMs. Our SVM approach is accurate, fast, suitable to on-line learning and generalizes well. SVM regression on a large, randomly collected data set of 30,000 Internet latencies yields a mean prediction error of 25ms using only 20% of the samples for training. Our results are promising for equipping end-nodes with intelligence for service selection, user-directed routing, resource scheduling and network inference. Finally, feature selection analysis finds that the eight most significant IP address bits provide surprisingly strong discriminative power.