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Euler's Eye

Team Vertex - Track 2


Project Overview

Euler's Eye is a concise and easy-to-use monitoring system for the OpenEuler OS that tracks system vitals in real-time, including CPU usage, Network I/O, and more.

The system is enhanced with a predictive AI model designed to estimate the cache miss rate, aiming to improve user experience by optimizing cache replacement policies.

Our approach treats the cache replacement problem as a Markov Decision Process (MDP) and leverages Deep Reinforcement Learning, specifically a value-based Deep Q Network (DQN), to learn and optimize these strategies.


Features

  • Real-time monitoring of system vitals (CPU, Network I/O, etc.)
  • Predictive modeling of cache miss rates
  • Learning-based cache replacement policies using Deep Q Networks
  • Intuitive web UI for live metric visualization

Team Members

  • Aarav Parin
  • Rory Condict
  • Shashwat Bhatnagar
  • Zeki Kam
  • Zarif Ahmed

Usage Guide

1. Generating Miss Rate Predictions

  • Ensure you have data available in CSV format.
  • Preprocess the data to reduce the number of features.
  • Run the prediction script:
    python3 run_openeuler_filesys.py

About

We are using value based Deep Q-Learning for predicting cache miss.

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