Proactive Discrepancy Detection in Distributed IT Systems
Serkan Altun
Department of Computer Science, Ankara University
Ezgi Bayraktar
Department of Computer Science, Sabancı University
Keywords: Kubernetes, Prometheus, Grafana, Elasticsearch, Apache Kafka, Docker
Abstract
This research paper explores proactive discrepancy detection in distributed IT systems, which are vital for modern computing applications like cloud services and enterprise solutions. Traditional discrepancy detection methods face significant challenges, including the complexity of distributed environments and the vast volume of data generated. This study aims to develop a comprehensive framework leveraging machine learning, artificial intelligence, and real-time data analytics to enhance the accuracy and efficiency of discrepancy detection. By employing a mixed-methods approach, combining qualitative and quantitative data collection techniques such as interviews, focus groups, and surveys, the research seeks to address the limitations of current methods and evaluate the proposed framework's effectiveness in various distributed system environments. The findings aim to significantly improve system reliability and performance, particularly in mission-critical applications like finance, healthcare, and industrial control systems, while offering broad insights into effective discrepancy detection mechanisms for the field of distributed computing.
Author Biographies
Serkan Altun, Department of Computer Science, Ankara University
Ezgi Bayraktar, Department of Computer Science, Sabancı University
