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Analysis of target indicators of the model of automated and continuous monitoring of the quality of the service «Internet access»

DOI 10.18127/j19997493-201803-03

Keywords:

J.C. Gonsales Gusev – Post-graduate Student, Department «Information Processing and Control Systems»,
Bauman Moscow State Technical University
E-mail: jcgonzalezgusev@gmail.com


This paper is dedicated to the analysis of the main models for measuring the quality of services from the end users’ point of view (QoE – Quality of Experience). The first part presents the main drawbacks of the existing model, which uses statistical analysis of the main quality parameters collected on the telecommunications operators’ devices and systems.
Among the existing shortcomings, there is an absence of technically and dynamically updated target quality value, as well as a low correlation between the QoE and other controlled parameters.
The description of the full cycle of the proposed approach for automatic collection and continuous analysis of the QoE assessment made it possible to identify the main parameters of the target process and perform its simulation modeling using GPSS. The results showed that the proposed approach could eliminate the drawbacks of the existing model, allowing to permanently monitoring the variation of the quality QoE KPI and including the existing real quality problems of controlled users. The proposed approach allows us to determine the empirical threshold value of quality, which also has a strong relationship with other controlled parameters.
Simulation modeling of the proposed automated process allowed detecting some of its implicit characteristics. Thus, for example, it has been shown that the overall QoE quality index depends to a lesser extent on the number of users and the probability of providing a poor level of quality to its customers, but is more dependent on the time for which the operator corrects the identified problems of specific users. Adherence to this approach (minimizing the time for correcting quality problems) will improve the overall quality score by up to 20%, even with a constant increase of the controlled users.

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