Performance-Based Classification of Users in a Containerized Stock Trading Application Environment Under Load

Kaydedildi:
Detaylı Bibliyografya
Yayımlandı:Electronics vol. 14, no. 14 (2025), p. 2848-2874
Yazar: Rak Tomasz
Diğer Yazarlar: Drabek, Jan, Charytanowicz Małgorzata
Baskı/Yayın Bilgisi:
MDPI AG
Konular:
Online Erişim:Citation/Abstract
Full Text + Graphics
Full Text - PDF
Etiketler: Etiketle
Etiket eklenmemiş, İlk siz ekleyin!
Diğer Bilgiler
Özet:Emerging digital technologies are transforming how consumers participate in financial markets, yet their benefits depend critically on the speed, reliability, and transparency of the underlying platforms. Online stock trading platforms must maintain high efficiency underload to ensure a good user experience. This paper presents performance analysis under various load conditions based on the containerized stock exchange system. A comprehensive data logging pipeline was implemented, capturing metrics such as API response times, database query times, and resource utilization. We analyze the collected data to identify performance patterns, using both statistical analysis and machine learning techniques. Preliminary analysis reveals correlations between application processing time and database load, as well as the impact of user behavior on system performance. Association rule mining is applied to uncover relationships among performance metrics, and multiple classification algorithms are evaluated for their ability to predict user activity class patterns from system metrics. The insights from this work can guide optimizations in similar distributed web applications to improve scalability and reliability under a heavy load. By framing performance not merely as a technical property but as a determinant of financial decision-making and well-being, the study contributes actionable insights for designers of consumer-facing fintech services seeking to meet sustainable development goals through trustworthy, resilient digital infrastructure.
ISSN:2079-9292
DOI:10.3390/electronics14142848
Kaynak:Advanced Technologies & Aerospace Database