Autonomous Infrastructure Learning: Behavioral Pattern‑Driven Optimization

Project Overview

This repository implements Autonomous Behavioral Pattern-Driven Optimization for Sustainable and Socially Responsible ML Infrastructure using CRE-inspired event-sequence analysis.

Research Questions

1. How can behavioral pattern recognition identify resource waste patterns in ML cluster environments?
2. What event-sequence analysis methods can predict resource inefficiencies?
3. Can autonomous recommendations improve resource allocation without manual intervention?

Dataset

Primary focus on Alibaba Cluster GPU Traces:

• cluster-trace-gpu-v2020: 6.5K+ GPUs, 2-month ML workload trace
• cluster-trace-gpu-v2023: 6.2K+ GPUs, fragmentation analysis
• cluster-trace-v2018: 4K machines, general workload patterns

Repository Structure

├── data/                    # Data storage
│   ├── raw/                # Original datasets
│   ├── processed/          # Cleaned and transformed data
│   └── external/           # External reference data
├── notebooks/              # Jupyter notebooks
│   ├── exploration/        # Initial data exploration
│   ├── analysis/          # Pattern analysis notebooks
│   └── visualization/     # Results visualization
├── src/                   # Source code
│   ├── data/              # Data processing modules
│   ├── analysis/          # Behavioral pattern analysis
│   ├── models/            # ML models and CRE framework
│   └── visualization/     # Plotting utilities
├── configs/               # Configuration files
├── scripts/               # Automation scripts
└── results/               # Output artifacts

Quick Start

1. Install dependencies

   pip install -r requirements.txt

2. (Optional) Generate synthetic logs

   python scripts/generate_synthetic_logs.py --out data/raw/synthetic/logs.csv --n 5000

3. Explore datasets

   jupyter notebook notebooks/exploration/02_alibaba_quick_peek.ipynb
   jupyter notebook notebooks/exploration/03_logs_dataset_and_problem.ipynb

Key Features

• CRE-Inspired Pattern Detection: Temporal event sequence analysis
• Behavioral Classification: User and application behavior clustering
• Resource Usage Prediction: ML models for optimization
• Privacy-Preserving Analytics: Federated learning approaches

Research Methodology

1. Data Exploration: Understand cluster behavioral patterns
2. Pattern Recognition: Apply CRE-inspired temporal analysis
3. Behavioral Modeling: Develop predictive models
4. Autonomous Optimization: Real-time recommendation system
5. Validation: Performance improvement measurement

Contributing

This research project follows academic collaboration principles. Please see CONTRIBUTING.md for guidelines.

License

Academic research use only. See LICENSE for details.

Contact

For questions about this research, please file an issue or contact the research team.