ML Optimization
Pakit uses five machine learning models to optimize storage and network performance. The MLDAGIntegration layer provides hints to the DAG backend — ML recommendations guide compression, deduplication, prefetching, peer selection, and network parameters without overriding the core storage engine.
Optimization Models
CompressionPredictor
Three-layer MLP (128→64→32→4) built with PyTorch. Selects the optimal compression algorithm per block with 97% accuracy and sub-millisecond inference latency.
DeduplicationOptimizer
SimHash 64-bit fingerprints combined with Locality-Sensitive Hashing across 16 tables. 0.85 similarity threshold with under 10ms lookup for near-duplicate detection.
PrefetchEngine
Two-layer LSTM with 64 hidden units predicting top-5 most likely next blocks. Improves cache hit rates by 15–25% through anticipatory data loading.
PeerSelector
Thompson Sampling with Contextual Bandit for optimal peer routing. Reduces data transfer latency by 10–20% by selecting the fastest and most reliable peers.
NetworkOptimizer
Q-Learning agent providing adaptive fanout and TTL parameters. Saves 15–30% bandwidth by dynamically tuning gossip protocol parameters based on network conditions.
Multi-Algorithm Compression
Supports ZSTD, LZ4, Brotli, LZMA, BZ2, and ZLIB with an AUTO mode that delegates to the CompressionPredictor. Quantum compression placeholder via Kinich integration.