LLM4KUBE

Abstract

Kubernetes, an open-source platform for automating the deployment, scaling, and management of containerized applications, plays a pivotal role in modern software engineering and DevOps. As Kubernetes clusters become complex, troubleshooting of failures is critical for DevOps practitioners. This paper introduces a method that utilizes Large Language Models (LLMs) to analyze Kubernetes logs, aiding in the troubleshooting and identification of bugs in Kubernetes environments. Our proof of concept demonstrates LLMs' potential to automate application monitoring, and we hope the community can build on this insight and leverage LLMs for this task.

Introduction

As organizations adopt Kubernetes for workload deployment, the complexity of Kubernetes clusters increases, necessitating efficient troubleshooting for system reliability. This paper proposes leveraging Kubernetes logs and Large Language Models (LLMs) to automate troubleshooting, expediting the identification of affected services and offering actionable insights.

Challenges

Scarcity of Data: The absence of publicly available data on DevOps tasks necessitated the generation of our own datasets.
Manual Annotation: Annotating data manually proved to be a time-consuming task, susceptible to human error if not executed meticulously.
Simulation Limitations: Due to the constrained size of the architecture utilized, we faced challenges in accurately simulating real-world scenarios, such as payment failures or other user-related issues.

Simulation Setup

Our technique involves teaching LLMs about the architecture and services affected due to the failure of one service. This enables LLMs to efficiently analyze logs, determine the severity of service failures, and identify the services impacted by the failure of a single service.

Architecture

Annotation

Data Generation

In instances where access to company-specific logs are restricted, we innovatively employ LLMs for data augmentation. Leveraging original logs as references, we task LLMs with generating synthetic logs to enrich our dataset.

Experiments

We did a couple of experiments using zero shot, few shot, and few shot chain of thought on different LLMs to compare their performances.

Zero Shot

We fed our architecture asking the LLM to learn it and then asked it questions about our logs.

Few Shot

We gave it a little assistance by giving it an answer for three different scenarios and then asked it questions about our logs and stack trace.

Few Shot Chain of Thought

Alongside doing everything we did for few shot, we asked it to think about the solution step by step.

Results

Teaser

Future Work

Companies generate PetBytes of data each day, and with the right anonymisation we can extend our work. We have explored just one aspect of DevOps; there is scope for this sort of work in many other aspects.

Thank You :)

We appreciate your attention and interest in our work. If you have any further questions or feedback, please feel free to reach out.

LLM4Kube