Please use this identifier to cite or link to this item:
|Title:||End-to-end diagnosis of QoS violations with neural network|
|Authors:||Zhou, L. |
|Citation:||Zhou, L.,Chen, L.,Hung, K.P.,Lek, H.N. (2008). End-to-end diagnosis of QoS violations with neural network. Proceedings - Conference on Local Computer Networks, LCN : 530-531. ScholarBank@NUS Repository. https://doi.org/10.1109/LCN.2008.4664223|
|Abstract:||In this paper, we introduce a novel end-to-end approach to QoS management with respect to the diagnosis of QoS violations. We first use a set of end-to-end flow traffic statistics to describe a QoS violation. Subsequently, neural network techniques are engaged to identify and differentiate QoS violations through classification of the collected statistics. Through experiments, we find that our scheme outperforms traditional rule-based methods which require clear margins of QoS parameters in asserting a QoS violation. ©2008 IEEE.|
|Source Title:||Proceedings - Conference on Local Computer Networks, LCN|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 10, 2018
checked on Dec 8, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.