Authors:
Natalia Kushik
1
;
Nina Yevtushenko
2
;
Ana Cavalli
3
;
Wissam Mallouli
4
and
Jeevan Pokhrel
5
Affiliations:
1
Tomsk State University and TELECOM SudParis, Russian Federation
;
2
Tomsk State University, Russian Federation
;
3
TELECOM SudParis, France
;
4
Montimage, France
;
5
TELECOM SudParis and Montimage, France
Keyword(s):
Web Service, Quality, QoE, Logic Network/Circuit.
Related
Ontology
Subjects/Areas/Topics:
Internet Technology
;
Web Information Systems and Technologies
;
Web Services and Web Engineering
Abstract:
This paper is devoted to the problem of evaluating the quality of experience (QoE) for a given web service
based on the values of service parameters (for instance, QoS indicators). Different self-learning algorithms
can be used to reach this purpose. In this paper, we propose to use self-learning logic networks, called also
circuits, for evaluating the QoE of web services, since modern software tools can efficiently deal with very
large logic networks. As usual, for machine learning techniques, statistics are used to design the initial
circuit that accepts service parameter values as inputs and produces the QoE value as an output. The circuit
is self-adaptive, i.e., when a new end-user provides a feedback of the service satisfaction the circuit is
resynthesized in order to behave properly (if needed). Such resynthesis (circuit learning) can be efficiently
performed using a number of tools for logic synthesis and verification.