Learning Representations on Logs for AIOps

TL;DR

This paper introduces a specialized Large Language Model trained on log data to improve automated log analysis in AIOps, outperforming existing models and aiding SREs in operational tasks.

Contribution

It presents a novel LLM trained on log data for AIOps, demonstrating superior performance on downstream log analysis tasks compared to existing models.

Findings

The proposed LLM outperforms existing models on multiple downstream tasks.

Training on both public and proprietary log data enhances model effectiveness.

The LLM enables more efficient and accurate automated log analysis in AIOps.

Abstract

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.