|
Title:
|
PROVIDING QUALITY-OF-SERVICE SUPPORT TO LEGACY APPLICATIONS USING MACHINE LEARNING |
|
Author(s):
|
Isara Anantavrasilp , Thorsten Schöler |
|
ISBN:
|
978-972-8924-40-9 |
|
Editors:
|
Jörg Roth, Jairo Gutiérrez and Ajith P. Abraham (series editors: Piet Kommers, Pedro Isaías and Nian-Shing Chen) |
|
Year:
|
2007 |
|
Edition:
|
Single |
|
Keywords:
|
Quality-of-Service, legacy applications, network, data mining, machine learning |
|
Type:
|
Full Paper |
|
First Page:
|
75 |
|
Last Page:
|
83 |
|
Language:
|
English |
|
Cover:
|
|
|
Full Contents:
|
click to dowload 
|
|
Paper Abstract:
|
Quality-of-Service (QoS) support is an essential feature in recent and upcoming networking standards. Applications
running on these networks can specify appropriate service classes for their connection flows so that the flows are treated
accordingly. However, current internet applications (legacy applications) cannot benefit from this facility as they are
designed using the best-effort scheme. Effective QoS support to applications with unspecified service classes can be
provided through our proposed intelligent QoS Manager.
This paper describes the important features that can be used to determine service classes and discusses how machine
learning can be incorporated into a QoS Manager, enabling it to distinguish different types of application flows and
assign them appropriate service classes. Experiments using 10-fold cross-validation (CV), 33% hold-out (HO) and the
learner's specific features lead to the selection of PART (Frank & Witten, 1998) as the classifier of the framework. It
achieves 91.55% and 93.29% prediction correctness in CV and HO experiments respectively. |
|
|
|
|
|
|
