(Created page with " == Abstract == Forecasting Internet traffic is receiving an increasing attention from the computer networks domain. Indeed, by improving this task efficient traffic engineer...") |
m (Scipediacontent moved page Draft Content 130790406 to Cortez et al 2007a) |
(No difference)
| |
Revision as of 13:54, 14 October 2020
Abstract
Forecasting Internet traffic is receiving an increasing attention from the computer networks domain. Indeed, by improving this task efficient traffic engineering and anomaly detection tools can be developed, leading to economic gains due to better resource management. This paper presents a Neural Network (NN) approach to predict TCP/IP traffic for all links of a backbone network, using both univariate and multivariate strategies. The former uses only past values of the forecasted link, while the latter is based on the neighbor links of the backbone topology. Several experiments were held by considering real-world data from the UK education and research network. Also, different time scales (e.g. every ten minutes and hourly) were analyzed. Overall, the proposed NN approach outperformed other forecasting methods (e.g. Holt-Winters). R&D Algoritmi centre
Document type: Part of book or chapter of book
Full document
Original document
The different versions of the original document can be found in:
Document information
Published on 01/01/2007
Volume 2007, 2007
DOI: 10.1007/978-3-540-74695-9_46
Licence: CC BY-NC-SA license
Share this document
Keywords
claim authorship
Are you one of the authors of this document?