Analyze pdf for experimental evaluation code

.gitignore

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+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# Virtual environments
+venv/
+env/
+ENV/
+env.bak/
+venv.bak/
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# Results
+*.png
+*.pdf
+results/
+output/
+
+# Logs
+*.log

GITHUB_SETUP_GUIDE.md

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+# GitHub Repository Setup Guide
+
+## 🚀 Complete Guide to Upload SFC Placement Framework to GitHub
+
+### Method 1: Using GitHub Web Interface (Easiest)
+
+#### Step 1: Create New Repository
+1. Go to [GitHub.com](https://github.com) and sign in
+2. Click the **"+"** button (top right) → **"New repository"**
+3. Fill in repository details:
+   - **Repository name**: `sfc-placement-framework` (or your preferred name)
+   - **Description**: `FPTAS algorithms for resource and delay constrained SFC placement`
+   - **Visibility**: Public or Private (your choice)
+   - ✅ **Add a README file** (uncheck this - we have our own)
+   - ✅ **Add .gitignore** (uncheck this - we have our own)
+   - Click **"Create repository"**
+
+#### Step 2: Upload Files via Web Interface
+1. In your new empty repository, click **"uploading an existing file"**
+2. Upload these files one by one (or drag and drop):
+   - `sfc_placement_framework.py`
+   - `experimental_evaluation.py` 
+   - `test_implementation.py`
+   - `quick_demo.py`
+   - `requirements.txt`
+   - `README.md`
+   - `IMPLEMENTATION_SUMMARY.md`
+   - `.gitignore`
+
+3. For each upload:
+   - Add commit message: "Add [filename]"
+   - Click **"Commit changes"**
+
+### Method 2: Using Git Command Line (Advanced)
+
+If you have git installed locally:
+
+```bash
+# Clone the empty repository
+git clone https://github.com/YOUR_USERNAME/sfc-placement-framework.git
+cd sfc-placement-framework
+
+# Copy all the files to this directory, then:
+git add .
+git commit -m "Initial commit: Complete FPTAS implementation"
+git push origin main
+```
+
+### Method 3: Import This Repository
+
+If you have access to this workspace's git repository:
+
+#### Current Repository Status
+```bash
+Repository: Ready with all files committed
+Branch: cursor/analyze-pdf-for-experimental-evaluation-code-2290
+Files included:
+- sfc_placement_framework.py (25KB) - Core FPTAS algorithms
+- experimental_evaluation.py (22KB) - Evaluation framework  
+- test_implementation.py (8KB) - Test suite
+- quick_demo.py (5KB) - Demo script
+- requirements.txt - Dependencies
+- README.md - Documentation
+- IMPLEMENTATION_SUMMARY.md - Summary
+- .gitignore - Git ignore rules
+```
+
+#### To Push to Your GitHub:
+1. Create a new repository on GitHub (as in Method 1, Step 1)
+2. Copy the remote URL from GitHub
+3. If you have command line access:
+```bash
+git remote add origin https://github.com/YOUR_USERNAME/YOUR_REPO_NAME.git
+git push -u origin main
+```
+
+## 📁 Repository Structure
+
+Your GitHub repo will look like this:
+```
+sfc-placement-framework/
+├── README.md                      # Main documentation
+├── IMPLEMENTATION_SUMMARY.md      # Implementation overview
+├── requirements.txt               # Python dependencies
+├── .gitignore                     # Git ignore rules
+├── sfc_placement_framework.py     # Core FPTAS implementation
+├── experimental_evaluation.py     # Evaluation suite
+├── test_implementation.py         # Test suite  
+├── quick_demo.py                  # Quick demo
+└── GITHUB_SETUP_GUIDE.md         # This guide
+```
+
+## 🎯 Recommended Repository Settings
+
+### Repository Name Suggestions:
+- `sfc-placement-framework`
+- `fptas-sfc-placement` 
+- `cloud-native-sfc-placement`
+- `resource-delay-sfc-algorithms`
+
+### Description Suggestions:
+- "FPTAS algorithms for resource and delay constrained placement of cloud native service function chains"
+- "Complete implementation of approximation schemes for SFC placement with theoretical guarantees"
+- "Polynomial-time algorithms for optimizing cloud-native service function chain deployment"
+
+### Topics to Add:
+```
+sfc, service-function-chaining, fptas, approximation-algorithms, 
+cloud-native, kubernetes, network-functions, optimization, 
+algorithms, computer-science, networking
+```
+
+## 🔗 Making It Professional
+
+### Add These Badges to README.md:
+```markdown
+![Python](https://img.shields.io/badge/python-v3.8+-blue.svg)
+![License](https://img.shields.io/badge/license-MIT-green.svg)
+![Tests](https://img.shields.io/badge/tests-passing-brightgreen.svg)
+![Code Style](https://img.shields.io/badge/code%20style-black-000000.svg)
+```
+
+### Create Release:
+1. Go to **Releases** → **"Create a new release"**
+2. Tag: `v1.0.0` 
+3. Title: `Initial Release - Complete FPTAS Implementation`
+4. Description: Copy from IMPLEMENTATION_SUMMARY.md
+
+## 🚀 Next Steps After Upload
+
+1. **Star your own repo** (shows confidence!)
+2. **Add topics/tags** for discoverability
+3. **Enable GitHub Pages** (if you want a website)
+4. **Set up GitHub Actions** for automated testing
+5. **Add license file** (MIT recommended)
+6. **Share the link** in your paper acknowledgments
+
+## 📧 Getting Help
+
+If you need help with any step:
+1. GitHub has excellent documentation at docs.github.com
+2. The files are ready - just need to be uploaded
+3. The README.md has complete usage instructions
+4. All code is tested and working
+
+**Your repository will be a complete, professional implementation that validates your academic work!**