Authors:
Diego Rivera
1
;
Ana R. Cavalli
1
;
Natalia Kushik
1
and
Wissam Mallouli
2
Affiliations:
1
Université Paris-Saclay, France
;
2
Montimage, France
Keyword(s):
Quality of Experience (QoE), Business Model, QoE Evaluation, Extended Finite State Machine (EFSM).
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Applications and Software Development
;
Artificial Intelligence
;
Model Execution and Simulation
;
Model-Driven Software Development
;
Models
;
Paradigm Trends
;
Service Invocation, Interaction, Monitoring
;
Services
;
Software Engineering
Abstract:
The expansion of Internet-based services has increased the need to ensure a good quality on them. In this context,
a preliminary work we developed exposes a Quality of Experience (QoE) evaluation framework based
on the mathematical formalism of EFSMs, which includes business-related variables into the prediction analysis.
In this paper, we present an implementation of this QoE evaluation framework using the Montimage
Monitoring Tool (MMT). The implementation presented in this paper is based on three main algorithms: (1)
generation of the traces of a given length of the EFSM-based OTT model, (2) computation of the QoE for
each trace using a suitable QoE model, and (3) computation of the number of configurations reachable from
the initial state of the EFSM. We use this implementation to calculate the amount of configurations captured
by the model of a real OTT service, analyzing how this value varies with respect to the depth (trace length) of
the analysis and which is the distribution
of the QoE values of the computed configurations. This information
will enable the service provider to characterize the QoE of all possible scenarios and to introduce changes if
required, in order to maximize the revenues provided by the chosen business model and the QoE of end-users.
(More)