DC-Prophet: Predicting Catastrophic Machine Failures in DataCenters
This work addresses reliability issues in industrial datacenters by enabling preventive actions to avoid information loss and degradation.
The paper tackles the problem of predicting catastrophic machine failures in datacenters using a large dataset of 104 million events from 12,500 machines, achieving an AUC of 0.93 and an F3-score of 0.88 with a 39.45% improvement over other methods.
When will a server fail catastrophically in an industrial datacenter? Is it possible to forecast these failures so preventive actions can be taken to increase the reliability of a datacenter? To answer these questions, we have studied what are probably the largest, publicly available datacenter traces, containing more than 104 million events from 12,500 machines. Among these samples, we observe and categorize three types of machine failures, all of which are catastrophic and may lead to information loss, or even worse, reliability degradation of a datacenter. We further propose a two-stage framework-DC-Prophet-based on One-Class Support Vector Machine and Random Forest. DC-Prophet extracts surprising patterns and accurately predicts the next failure of a machine. Experimental results show that DC-Prophet achieves an AUC of 0.93 in predicting the next machine failure, and a F3-score of 0.88 (out of 1). On average, DC-Prophet outperforms other classical machine learning methods by 39.45% in F3-score.