Skip to content

Latest commit

 

History

History
65 lines (44 loc) · 3.4 KB

FINAL_README.md

File metadata and controls

65 lines (44 loc) · 3.4 KB

Here is the rewritten README file:

Optimizing Readahead Feature of Linux Page Cache using Machine Learning 📊💻

This repository provides a comprehensive implementation of optimizing the Readahead feature of the Linux Page Cache under varying workloads using machine learning techniques.

Setup 💻

Prerequisites

  • Python version: 3.x
  • Libraries: scikit-learn, numpy, pandas, etc.
  • Installation instructions: pip install -r requirements.txt

Environment Setup

  • Create a virtual environment: python -m venv env
  • Activate the virtual environment: source env/bin/activate

Implementing Machine Learning Components 🤖

Feature Importance Analysis

  • Brief description: Random Forest Classifier was used to analyze feature importance, and non-important features were removed.
  • Code snippet or example: [Insert code snippet]
  • Explanation of the component's functionality: This component is used to identify the most important features that affect the Readahead size.

Dimensionality Reduction

  • Brief description: T-SNE was used to visualize the data in 2D.
  • Code snippet or example: [Insert code snippet]
  • Explanation of the component's functionality: This component is used to reduce the dimensionality of the data and visualize it in 2D.

Model Training 🚀

  • Neural Network
    • Brief description: MLPClassifier was used with hidden layers of 64 and 32 neurons.
    • Code snippet or example: [Insert code snippet]
    • Explanation of the component's functionality: This component is used to train a neural network model to classify workload types and suggest optimal Readahead sizes.
  • Decision Tree
    • Brief description: DecisionTreeClassifier was used.
    • Code snippet or example: [Insert code snippet]
    • Explanation of the component's functionality: This component is used to train a decision tree model to classify workload types and suggest optimal Readahead sizes.
  • Random Forest
    • Brief description: RandomForestClassifier was used with 100 estimators.
    • Code snippet or example: [Insert code snippet]
    • Explanation of the component's functionality: This component is used to train a random forest model to classify workload types and suggest optimal Readahead sizes.

Results and Performance Analysis 📊

Model Comparison

Model Accuracy Notes
Decision Tree 100.00% Simple, interpretable, perfect accuracy
Neural Network 99.85% High accuracy, complex model with slight variability in precision
Random Forest 100.00% Combines multiple trees for perfect accuracy and generalization

Performance Comparison

  • The results show that both the Decision Tree and Random Forest models achieved perfect accuracy, while the Neural Network model had a slightly lower accuracy.

Summary 📚

This project provides a comprehensive implementation of optimizing the Readahead feature of the Linux Page Cache under varying workloads using machine learning techniques, demonstrating the effectiveness of machine learning techniques in optimizing the Readahead feature under varying workloads. The results show that the Random Forest model stands out for its combination of accuracy and interpretability, making it a strong candidate for real-time systems that require dynamic adjustment of Readahead sizes based on current workloads.