NDLI: A machine learning approach to TCP throughput prediction

Content Provider	ACM Digital Library
Author	Zhu, Xiaojin Barford, Paul Sommers, Joel Mirza, Mariyam
Copyright Year	2011
Abstract	TCP throughput prediction is an important capability for networks where multiple paths exist between data senders and receivers. In this paper, we describe a new lightweight method for TCP throughput prediction. Our predictor uses Support Vector Regression (SVR); prediction is based on both prior file transfer history and measurements of simple path properties. We evaluate our predictor in a laboratory setting where ground truth can be measured with perfect accuracy. We report the performance of our predictor for oracular and practical measurements of path properties over a wide range of traffic conditions and transfer sizes. For bulk transfers in heavy traffic using oracular measurements, TCP throughput is predicted within 10% of the actual value 87% of the time, representing nearly a threefold improvement in accuracy over prior history-based methods. For practical measurements of path properties, predictions can be made within 10% of the actual value nearly 50% of the time, approximately a 60% improvement over history-based methods, and with much lower measurement traffic overhead. We implement our predictor in a tool called Path-Perf, test it in the wide area, and show that PathPerf predicts TCP throughput accurately over diverse wide area paths.
Starting Page	1026
Ending Page	1039
Page Count	14
File Format	PDF
ISSN	10636692
DOI	10.1109/TNET.2009.2037812
Volume Number	18
Issue Number	4
Journal	IEEE/ACM Transactions on Networking (TON)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-08-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	TCP throughput prediction Active measurements Machine learning Support vector regression
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Networks and Communications Software Computer Science Applications

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