Intelligent ML-Driven Cache System (Offline + Online)

A high-performance hybrid caching system that combines traditional policies (LRU, LFU, MFU) with both offline-trained and online-learning machine learning models to approximate Belady's Optimal Replacement Policy.

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Overview

This project explores intelligent caching by:

• Generating realistic Zipfian (skewed) access patterns
• Simulating Belady’s optimal replacement using full and partial lookahead
• Training ML models (Logistic Regression, XGBoost, CatBoost, LightGBM) to predict cache-worthiness
• Implementing a real-time Online ML Cache using the river library
• Benchmarking all strategies against LRU, LFU, MFU

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Benchmark Results (Offline ML Cache)

Cache Type	Hit Rate
LRU	0.3402
LFU	0.4776
MFU	0.0960
ML (Best Offline Model)	0.4805

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Online Learning Results

Metric	Value
Streaming Hit Rate	0.6611
Prediction Accuracy	0.9509

The online ML cache adapts to access patterns in real time, significantly outperforming all static policies.

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Features

• Custom implementation of LRU, LFU, and MFU
• Training and selection of best model among LogReg, XGBoost, CatBoost, LightGBM
• Belady’s optimal replacement simulation (full and partial lookahead)
• Real-time OnlineMLCache (built with river) that trains on-the-fly
• Hit rate + accuracy tracking and full benchmarking

Setup & Usage

1. Install all dependencies

pip install -r requirements.txt

2. Generate labeled data (offline Belady)

python data_generator.py --num_requests 100000 --unique_items 1000 --capacity 100 --output data/labeled_requests.csv

3. Train all ML models

python train_models.py

4. Run offline ML cache

python test_cache.py

5. Benchmark all strategies (LRU, LFU, MFU, ML)

python compare_cache.py

6. Run online ML cache (real-time learning)

python test_online_cache.py

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Sample Output

=== Offline Cache Hit Rates ===
LRU : 0.3402
LFU : 0.4776
MFU : 0.0960
ML  : 0.4805

--- Streaming Cache ---
Streaming ML-Cache Hit Rate: 0.6611
Prediction Accuracy:       0.9509

License

MIT License.

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👤 Author

Tanishq Parihar Built as a systems + ML hybrid project to explore intelligent infrastructure and predictive caching

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