  Identifying the failure modes of cloud computing systems is a difficult and
time-consuming task, due to the growing complexity of such systems, and the
large volume and noisiness of failure data. This paper presents a novel
approach for analyzing failure data from cloud systems, in order to relieve
human analysts from manually fine-tuning the data for feature engineering. The
approach leverages Deep Embedded Clustering (DEC), a family of unsupervised
clustering algorithms based on deep learning, which uses an autoencoder to
optimize data dimensionality and inter-cluster variance. We applied the
approach in the context of the OpenStack cloud computing platform, both on the
raw failure data and in combination with an anomaly detection pre-processing
algorithm. The results show that the performance of the proposed approach, in
terms of purity of clusters, is comparable to, or in some cases even better
than manually fine-tuned clustering, thus avoiding the need for deep domain
knowledge and reducing the effort to perform the analysis. In all cases, the
proposed approach provides better performance than unsupervised clustering when
no feature engineering is applied to the data. Moreover, the distribution of
failure modes from the proposed approach is closer to the actual frequency of
the failure modes.
