Abstract
Internet traffic prediction plays a fundamental role in network design, management, control, and optimization. The self-similar and non-linear nature of network traffic makes highly accurate prediction difficult In this paper, we proposed a new boosting scheme, namely W-Boost, for traffic prediction from two perspectives: classification and regression. To capture the non-linearity of the traffic while introducing low complexity into the algorithm, 'stump' and piece-wise-constant function are adopted as weak learners for classification and regression, respectively. Furthermore, a new weight update scheme is proposed to take the advantage of the correlation information within the traffic for both models. Experimental results on real network traffic which exhibits both self-similarity and non-linearity demonstrate the effectiveness of the proposed W-Boost.
Original language | English (US) |
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Title of host publication | Lecture Notes in Computer Science |
Editors | J. Wang, X. Liao, Z. Yi |
Pages | 397-402 |
Number of pages | 6 |
Volume | 3498 |
Edition | III |
State | Published - 2005 |
Externally published | Yes |
Event | Second International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005 - Chongqing, China Duration: May 30 2005 → Jun 1 2005 |
Other
Other | Second International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005 |
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Country/Territory | China |
City | Chongqing |
Period | 5/30/05 → 6/1/05 |
ASJC Scopus subject areas
- Computer Science (miscellaneous)