Publication Type
Working Paper
Version
publishedVersion
Publication Date
4-2021
Abstract
DRAM failure prediction is a vital task in AIOps, which is crucial to maintain the reliability and sustainable service of large-scale data centers. However, limited work has been done on DRAM failure prediction mainly due to the lack of public available datasets. This paper presents a comprehensive empirical evaluation of diverse machine learning techniques for DRAM failure prediction using a large-scale multisource dataset, including more than three millions of records of kernel, address, and mcelog data, provided by Alibaba Cloud through PAKDD 2021 competition. Particularly, we first formulate the problem as a multiclass classification task and exhaustively evaluate seven popular/stateof-the-art classifiers on both the individual and multiple data sources. We then formulate the problem as an unsupervised anomaly detection task and evaluate three state-of-the-art anomaly detectors. Further, based on the empirical results and our experience of attending this competition, we discuss major challenges and present future research opportunities in this task.
Keywords
DRAM failure prediction, Data center reliability, Cloud services
Discipline
Databases and Information Systems | Data Storage Systems
Research Areas
Data Science and Engineering
First Page
1
Last Page
11
Citation
WU, Zhiyue; XU, Hongzuo; PANG, Guansong; YU, Fengyuan; WANG, Yijie; JIAN, Songlei; and WANG, Yongjun.
DRAM failure prediction in AIOps: Empirical evaluation, challenges and opportunities. (2021). 1-11.
Available at: https://ink.library.smu.edu.sg/sis_research/7135
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.