SHREC: a SRE Behaviour Knowledge Graph Model for Shell Command Recommendations

TL;DR

This paper introduces SHREC, a knowledge graph model that captures SRE shell command behaviors to provide real-time recommendations, improving operational efficiency and knowledge sharing.

Contribution

The paper presents a novel knowledge graph approach for modeling SRE shell command behaviors and extracting this knowledge from historical data for real-time recommendations.

Findings

SHREC improves SRE operational efficiency.

It enables sharing and re-utilization of SRE knowledge.

Empirical results show effectiveness on real shell command data.

Abstract

In IT system operations, shell commands are common command line tools used by site reliability engineers (SREs) for daily tasks, such as system configuration, package deployment, and performance optimization. The efficiency in their execution has a crucial business impact since shell commands very often aim to execute critical operations, such as the resolution of system faults. However, many shell commands involve long parameters that make them hard to remember and type. Additionally, the experience and knowledge of SREs using these commands are almost always not preserved. In this work, we propose SHREC, a SRE behaviour knowledge graph model for shell command recommendations. We model the SRE shell behaviour knowledge as a knowledge graph and propose a strategy to directly extract such a knowledge from SRE historical shell operations. The knowledge graph is then used to provide shell command recommendations in real-time to improve the SRE operation efficiency. Our empirical study based on real shell commands executed in our company demonstrates that SHREC can improve the SRE operation efficiency, allowing to share and re-utilize the SRE knowledge.