AI-driven Predictive Shard Allocation for Scalable Next Generation Blockchains

2025-11-18

AI Summary

Static sharding allocation often causes load imbalance, hotspot congestion, and high cross-shard communication overhead, severely limiting blockchain scalability. To address this, we propose the Predictive Sharding Allocation Protocol (PSAP), the first dynamic sharding mechanism that jointly integrates multi-horizon temporal workload forecasting with safety-constrained Proximal Policy Optimization (PPO) reinforcement learning—enabling verifiable, deterministic, and adaptive shard reconfiguration under Byzantine fault tolerance. Key technical contributions include a lightweight temporal prediction model, safety-verification gating, quantized synchronous runtime execution, and atomic cross-shard migration support. Extensive evaluation on real-world Ethereum, NEAR, and Hyperledger Fabric workloads demonstrates that PSAP achieves a 2× throughput improvement, reduces end-to-end latency by 35%, and cuts cross-shard communication overhead by 20%.

Technology Category

• Multiagent Systems: Mechanism Design
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Machine Learning: Hardware-aware ML

Application Category

• Security and Privacy: Privacy-enhancing technologies
• Economics, Online Markets and Human Computation: The sharing economy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Sharding has emerged as a key technique to address blockchain scalability by partitioning the ledger into multiple shards that process transactions in parallel. Although this approach improves throughput, static or heuristic shard allocation often leads to workload skew, congestion, and excessive cross-shard communication diminishing the scalability benefits of sharding. To overcome these challenges, we propose the Predictive Shard Allocation Protocol (PSAP), a dynamic and intelligent allocation framework that proactively assigns accounts and transactions to shards based on workload forecasts. PSAP integrates a Temporal Workload Forecasting (TWF) model with a safety-constrained reinforcement learning (Safe-PPO) controller, jointly enabling multi-block-ahead prediction and adaptive shard reconfiguration. The protocol enforces deterministic inference across validators through a synchronized quantized runtime and a safety gate that limits stake concentration, migration gas, and utilization thresholds. By anticipating hotspot formation and executing bounded, atomic migrations, PSAP achieves stable load balance while preserving Byzantine safety. Experimental evaluation on heterogeneous datasets, including Ethereum, NEAR, and Hyperledger Fabric mapped via address-clustering heuristics, demonstrates up to 2x throughput improvement, 35% lower latency, and 20% reduced cross-shard overhead compared to existing dynamic sharding baselines. These results confirm that predictive, deterministic, and security-aware shard allocation is a promising direction for next-generation scalable blockchain systems.

Problem

• Dynamic shard allocation to prevent workload skew and congestion in blockchains
• Reducing cross-shard communication overhead while maintaining Byzantine safety
• Improving blockchain scalability through predictive account and transaction assignment

Innovation

• Dynamic shard allocation using workload forecasts
• Temporal forecasting with safety-constrained reinforcement learning
• Deterministic inference with synchronized quantized runtime