  With the increasing prevalence of scalable file systems in the context of
High Performance Computing (HPC), the importance of accurate anomaly detection
on runtime logs is increasing. But as it currently stands, many
state-of-the-art methods for log-based anomaly detection, such as DeepLog, have
encountered numerous challenges when applied to logs from many parallel file
systems (PFSes), often due to their irregularity and ambiguity in time-based
log sequences. To circumvent these problems, this study proposes ClusterLog, a
log pre-processing method that clusters the temporal sequence of log keys based
on their semantic similarity. By grouping semantically and sentimentally
similar logs, this approach aims to represent log sequences with the smallest
amount of unique log keys, intending to improve the ability of a downstream
sequence-based model to effectively learn the log patterns. The preliminary
results of ClusterLog indicate not only its effectiveness in reducing the
granularity of log sequences without the loss of important sequence information
but also its generalizability to different file systems' logs.
