Перегляд за Автор "Stetsenko, Inna"

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  • Ескіз
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    Petri-object simulation: technique and software
    (National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2020) Stetsenko, Inna; Dyfuchyn, Anton
    Nowadays information systems tend to be including components for transforming data into information applying modelling and simulation. Combined with real-time data, discrete event simulation could create powerful making decision and control systems. For these purposes, simulation software should be concentrated on creation the model as a code which can be easy integrated with other components of software. In this regard, Petri-object simulation technique, the main concept of which is to compose the code of model of complicated discrete event system in a fast and flexible way, simultaneously providing fast running the simulation, is requisite. The behaviour description of the model based on stochastic multichannel Petri net while the model composition is grounded on object-oriented technology. The Petri-object simulation software provides scalable simulation algorithm, graphical editor, correct transformation graphical images into model, correct simulation results. Graphical editor helps to cope with error-prone process of linking elements with each other. For better understanding the technique, the Petri-object model of web information system has been developed. Investigation of the response time has been conducted. The experiment has revealed system parameters impact on the value of response time. Thus, the model can be useful to avoid long-running request.
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    Software for collecting and analyzing metrics in highly loaded applications based on the Prometheus monitoring system
    (National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2024) Stetsenko, Inna; Myroniuk, Anton
    This paper emphasizes the importance of collecting metrics during application operation for early detection of potential problems. The undisputed leader in this area is the Prometheus monitoring system, which, combined with Grafana – a platform for visualizing collected data in numerous graphs – becomes an indispensable tool for programmers and site reliability engineers. However, the average value of a certain metric is often unrepresentative, because it does not reflect a comprehensive picture. Instead, collecting metrics in terms of various quantiles over a long period is useful to identify even single instabilities. Still, the use of standard tools in the Python ecosystem may require a lot of server resources and long preliminary analysis, which can be quite costly for businesses from a financial point of view. That is why the development of a new approach for collecting and analyzing metrics in highly loaded applications based on the Prometheus monitoring system is relevant. The research aims to improve the efficiency of storing metrics across different quantiles, which will create additional opportunities for further analysis. A review of existing approaches for calculating quantile values on large data sets was conducted. Their comparative characteristics in terms of speed and memory usage were also presented. The chosen method was adapted for use with the real-time data stream and implemented as a Python extension for the official Prometheus library. It opens up opportunities for comprehensive monitoring of highly loaded systems in terms of both server resource usage and the quantity and quality of collected useful data. This solution can be easily implemented on large projects requiring continuous tracking of various metrics to ensure stable and uninterrupted service operation.