DockerPulse

Inspiration

DockerPulse was inspired by products like k8sgpt.ai to make orchestration monitoring tool but mostly It was inspired by researches by Tien, Chin‐Wei, Zou, Zhuping who proposed techniques for anomaly detection in docker containers

What it does

DockerPulse is a powerful tool designed to monitor and manage log data from multiple Docker containers. It processes log data from these containers, parses them using Drain and Spell parsers, detects anomalies using a BERT-based neural network called LogBERT, and generates solutions for detected anomalies with GPT-3.5-Turbo. Additionally, DockerPulse can send all updates and notifications to a connected Slack workspace app. This entire system is wrapped in a user-friendly command-line interface (CLI) application, making it easy to schedule and automate log monitoring tasks with the help of cron jobs.

How we built it

Log Collection: DockerPulse collects logs from multiple Docker containers, making it easy to centralize and analyze your application's log data.
Log Parsing: It uses the Drain and Spell parsers to extract structured information from log data, enabling more meaningful analysis.
Anomaly Detection: DockerPulse employs LogBERT, a BERT-based neural network, to detect anomalies in the log data, allowing you to identify issues in real-time.
Anomaly Solution Generation: When an anomaly is detected, DockerPulse leverages GPT-3.5-Turbo to generate solutions or recommendations to address the detected issue.
Slack Integration: Connect DockerPulse to your Slack workspace app to receive notifications and updates about log data and anomalies directly in your communication platform.
CLI Interface: DockerPulse offers a user-friendly CLI for easy interaction and scheduling of log monitoring tasks using cron jobs.

Challenges we ran into

Challenges we faced are as follows:

Availability of time to research various techniques and their efficiencies
Implementation of New Research related to version of BERT tailored for LogAnomaly