White Papers
A Machine Learning Approach to TCP Throughput Prediction
Category: Networking and Communications
Overview TCP throughput prediction is an important capability in wide area overlay and multi-homed networks where multiple paths may exist between data sources and receivers. This paper describes a new, lightweight method for TCP throughput prediction that can generate accurate forecasts for a broad range of file sizes and path conditions. The author's method is based on Support Vector Regression modeling that uses a combination of prior file transfers and measurements of simple path properties. This paper calibrates and evaluates the capabilities of the throughput predictor in an extensive set of lab-based experiments where ground truth can be established for path properties using highly accurate passive measurements.
- Publisher
- Association for Computing Machinery
- File Format
- Date Published
- Dec 20, 2007
- Format
- White Papers
- Topics
- TCP - IP
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