# DRAM Failure Prediction in AIOps: Empirical Evaluation, Challenges and Opportunities

arXiv: https://arxiv.org/abs/2104.15052 · HTML: https://mlsanity.com/paper/2104.15052/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 30 Apr 2021.

## Builds on (most cited first, 6 of 6)

- [COPOD: Copula-Based Outlier Detection](https://mlsanity.com/paper/2009.09463/) (2020)
- [XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning](https://mlsanity.com/paper/1912.00290/) (2019)
- [An Empirical Analysis of Feature Engineering for Predictive Modeling](https://mlsanity.com/paper/1701.07852/) (2017)
- [Homophily Outlier Detection in Non-IID Categorical Data](https://mlsanity.com/paper/2103.11516/) (2021)
- [RECol: Reconstruction Error Columns for Outlier Detection](https://mlsanity.com/paper/2102.02791/) (2021)
- [Workload-Aware DRAM Error Prediction using Machine Learning](https://mlsanity.com/paper/2003.12448/) (2020)

## Built on by (most cited first, 1 of 1)

- [Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection](https://mlsanity.com/paper/2207.12201/) (2022)

## Abstract (arXiv)

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 multi-source 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 multi-class classification task and exhaustively evaluate seven popular/state-of-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.