Vol.60, No.3, 2019, pp.913-946, doi:10.32604/cmc.2019.07675
Failure Prediction, Lead Time Estimation and Health Degree Assessment for Hard Disk Drives Using Voting Based Decision Trees
  • Kamaljit Kaur1, *, Kuljit Kaur2
Kamaljit Kaur, Department of Computer Engg & Technology, Guru Nanak Dev University, Amritsar, Punjab, 143005, India.
Kuljit Kaur, Department. of Computer Science, Guru Nanak Dev University, Amritsar, Punjab, 143005, India.
* Corresponding Author: Kamaljit Kaur. Email: .
Hard Disk drives (HDDs) are an essential component of cloud computing and big data, responsible for storing humongous volumes of collected data. However, HDD failures pose a huge challenge to big data servers and cloud service providers. Every year, about 10% disk drives used in servers crash at least twice, lead to data loss, recovery cost and lower reliability. Recently, the researchers have used SMART parameters to develop various prediction techniques, however, these methods need to be improved for reliability and real-world usage due to the following factors: they lack the ability to consider the gradual change/deterioration of HDDs; they have failed to handle data unbalancing and biases problem; they don’t have adequate mechanisms for health status prediction of HDDs. This paper introduces a novel voting-based decision tree classifier to cater failure prediction, a balance splitting algorithm for the data unbalancing problem, an advanced procedure for lead time estimation and R-CNN based approach for health status estimation. Our system works robustly by considering a gradual change in SMART parameters. The system is rigorously tested on 3 datasets and it delivered benchmarks results as compared to the state of the art.
Hard disk drive, lead time, health status, N-splitting algorithm, machine learning, deep learning, data storage, unbalancing problem
Cite This Article
. , "Failure prediction, lead time estimation and health degree assessment for hard disk drives using voting based decision trees," Computers, Materials & Continua, vol. 60, no.3, pp. 913–946, 2019.
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