International Journal of Computational and Electronic Aspects in Engineering
Volume 7 · Issue 3 · July 2026 · pp. 135-141
Special Issue of National Conference on Emerging Innovative Trends in Computer Applications
Research Article · Peer Reviewed
Received: June 10, 2026 · Accepted: July 20, 2026 · Published: 31 July 2026
DOI: 10.26706/ijceae.7.4.20260719
Open Access · CC BY 4.0

Predictive Analysis Of Server Failures Using Big Data Log Mining And Machine Learning Techniques

Dr. Madhura Naralkar1*, Nikita Nagfase2, Nikita Selokar3, Pranali Dhumankhede4
1,2,3,4 MCA, Suryodaya College of Engineering and Technology, Nagpur, India.

*Corresponding author: madhura.naralkar@gmail.com
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Abstract

This study examines how organizations can use big data log mining combined with advanced machine learning methods to forecast server failures in large-scale IT environments which major service providers use [1], [2]. The server logs present analysis difficulties because they contain enormous quantities of data which include various types of information that may not follow any fixed structure [11], [12]. The study introduces a scalable framework which uses Apache Spark technology to process and analyze extensive log data [9], [10]. The testing procedure uses a dataset which contains one million log entries that were gathered from 100 different servers [13], [14]. The researchers used three machine learning models - Random Forest, LSTM and XGBoost to discover patterns which could signal impending system failures [5], [7], [8]. The research provides advanced mathematical knowledge about models through detailed explanations which include the gain equation used in XGBoost [6], [8]. The system developed by the researchers achieved 92% accuracy and 85% F1-score during simulated tests while it decreased server downtime by 40% [3], [26]. The study demonstrates how preprocessing techniques and feature extraction methods require researchers to select models carefully when handling log data which contains both noisy elements and unbalanced distributions [18], [19]. The research establishes a practical framework which organizations can use to forecast server failures while maintaining technical depth and actual business results which include data governance and deployment aspects [20], [21].

Keywords

Predictive maintenance big data analytics server log mining machine learning Apache Spark XGBoost Random Forest LSTM

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