Real-time big data analytics for hard disk drive predictive maintenance. (October 2018)

• Record Type: Journal Article
• Authors: Su, Chuan-Jun; Huang, Shi-Feng
• Is Part Of: Computers & electrical engineering. Volume 71(2018)
• Page Start: 93
• Page End: 101
• Publication Date: 2018-10
• Subjects: Big data -- Predictive maintenance -- Hard Disk Drive (HDD) -- Apache Spark -- Machine learning; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2018.07.025
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Effective and reliable cloud services depend on the quality of service provided by large-scale data centers, and data center equipment reliability issues can cause significant data and financial loss to cloud service clients. Corrective maintenance is a reactive approach that only corrects problems once they occur, resulting in unwanted downtime, while preventative maintenance relies on replacing equipment which may yet still have considerable effective operating lifetime, thus raising maintenance costs. In contrast, predictive maintenance can potentially predict equipment failure in advance, thus reducing unplanned downtime and extending equipment lifetimes, thus reducing maintenance costs while increasing system reliability. This research aims to develop a real-time predictive maintenance system, HDPass, based on Apache Spark for the detection of imminent hard disk drive (HDD) failures in data centers.