Prediction of Hard Disk Failures in Data Centers: A Comparative Study with Decision Tree-Based Machine Learning Models

Abstract

In the digitalizing world, data centers serve as the backbone of information technologies and represent critical infrastructure worldwide. However, the hard disks used in these centers often experience high failure rates, leading to issues such as data loss, service interruptions, and increased operational costs. This study presents a comparative analysis of decision tree-based machine learning methods for predicting hard disk failures using SMART (Self-Monitoring, Analysis, and Reporting Technology) data. Extensive SMART data from active data centers is gathered, preprocessed for modeling, and evaluated using four machine learning models: decision trees, random forests, XGBoost, and LightGBM. Model performance is assessed using metrics such as accuracy, false alarm rate (FAR), missed alarm rate (MAR), and a general metric (GM). Predictions for long-term (one month in advance) and short-term (one week in advance) horizons are analyzed separately. Following a comprehensive model selection process, the selected LightGBM model achieves 85.2% accuracy and 82.9% GM for short-term predictions, while the selected random forest model demonstrates superior performance for long-term predictions with 88.7% accuracy and 87.6% GM.

Keywords

• failure prediction
• decision trees
• predictive maintenance
• machine learning
• data centers

Project Number

AGTMPR94351

Veri Merkezlerinde Kullanılan Sabit Disklerdeki Arızaların Önceden Kestirimi: Karar Ağacı Tabanlı Makine Öğrenmesi Modelleri ile Karşılaştırmalı Bir Çalışma

Abstract

Dijitalleşen dünyada veri merkezleri, bilgi teknolojilerinin omurgasını oluşturarak kritik altyapılar arasında yer almaktadır. Ancak, veri merkezlerinde kullanılan sabit diskler, yüksek arıza oranları nedeniyle veri kaybı, hizmet kesintisi ve maliyet artışları gibi ciddi sorunlara yol açabilmektedir. Bu çalışma, sabit disk arızalarının SMART (Self-Monitoring, Analysis and Reporting Technology) verileri üzerinden önceden kestirimi için karar ağacı tabanlı makine öğrenmesi yöntemlerini karşılaştırmalı olarak analiz etmektedir. Çalışmada, güncel veri merkezlerinden elde edilen geniş çaplı SMART verileri kullanılmıştır. Veriler, ön işleme adımlarıyla modellenebilir hale getirilmiş ve dört farklı makine öğrenmesi modeli (karar ağaçları, rastgele ormanlar, XGBoost ve LightGBM) kullanılarak değerlendirilmiştir. Modellerin başarımı, doğruluk, yanlış alarm oranı (YAO), kaçırılan alarm oranı (KAO) ve genel başarım ölçevi (GBÖ) gibi ölçevler üzerinden kıyaslanmış; bir ay önceden (uzun vadeli) ve bir hafta önceden (kısa vadeli) yapılan kestirimler ayrı ayrı incelenmiştir. Kapsamlı bir model seçimi sürecinin ardından, kısa vadeli kestirim için seçilen LightGBM modeli %85,2 doğruluk ve %82,9 GBÖ değerine ulaşırken, uzun vadeli kestirim için seçilen rastgele ormanlar modeli %88,7 doğruluk ve %87,6 GBÖ değeri ile daha yüksek bir performans göstermiştir.

Keywords

• arıza kestirimi
• karar ağaçları
• kestirimci bakım
• makine öğrenmesi
• veri merkezleri

Supporting Institution

T.C. Sanayi ve Teknoloji Bakanlığı

Project Number

AGTMPR94351

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