Learning Representations on Logs for AIOps
Pranjal Gupta Harshit Kumar Debanjana Kar Karan Bhukar Pooja Aggarwal Prateeti Mohapatra
AI for IT Operations (AIOps) is a powerful platform that Site Reliability
Engineers (SREs) use to automate and streamline operational workflows with
minimal human intervention. Automated log analysis is a critical task in AIOps
as it provides key insights for SREs to identify and address ongoing faults.
Tasks such as log format detection, log classification, and log parsing are key
components of automated log analysis. Most of these tasks require supervised
learning; however, there are multiple challenges due to limited labelled log
data and the diverse nature of log data. Large Language Models (LLMs) such as
BERT and GPT3 are trained using self-supervision on a vast amount of unlabeled
data. These models provide generalized representations that can be effectively
used for various downstream tasks with limited labelled data. Motivated by the
success of LLMs in specific domains like science and biology, this paper
introduces a LLM for log data which is trained on public and proprietary log
data. The results of our experiments demonstrate that the proposed LLM
outperforms existing models on multiple downstream tasks. In summary, AIOps
powered by LLMs offers an efficient and effective solution for automating log
analysis tasks and enabling SREs to focus on higher-level tasks. Our proposed
LLM, trained on public and proprietary log data, offers superior performance on
multiple downstream tasks, making it a valuable addition to the AIOps platform.