AI-driven Predictive Shard Allocation for Scalable Next Generation Blockchains

TL;DR

The paper tackles blockchain scalability by addressing shard workload skew through a predictive, security-aware shard allocation framework. It introduces Temporal Workload Forecasting (TWF) integrated with Safe-PPO, under a Deterministic ML Execution Layer (DMEL), to enable multi-block-ahead, atomic shard reconfigurations with strict safety guarantees. The core contributions include a horizon-aware forecasting engine, a constraint-aware reinforcement learning allocator, an on-chain migration protocol (Mig_Batch) with dual inclusion, and a lightweight cross-shard relay, all demonstrated to deliver up to 2x throughput and 35% latency reduction with reduced cross-shard overhead across diverse datasets. The results establish AI-driven, deterministic shard management as a practical approach for next-generation scalable blockchain systems, preserving Byzantine fault tolerance while maintaining low migration overhead. The work also provides a detailed blueprint for implementation, security analysis, and scalability extension to up to 64 shards.

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.

Figures (14)

• Figure 1: Overall architecture of the PSAP protocol.
• Figure 2: Commit sequence for a migration batch: the source shard $S_p$ finalises block $t$, embeds MIG_BATCH in block $t{+}1$, broadcasts it, destination shard $S_q$ verifies and applies; if the timeout elapses, $S_p$ issues a dashed CANCEL_MIG.
• Figure 3: Ground truth vs. LSTM forecast for Ethereum Sepolia, Shard 3. The allocator receives the red dashed series $\hat{x}_3(t+1)$ each slot.
• Figure 4: MAPE for horizons $H=16$ and $H=32$ (all shards, all blocks).
• Figure 5: Throughput and p95 latency versus offered load (Ethereum-Sepolia trace). PSAP sustains higher TPS with sub-second latency until 1.1 × load; static hashing saturates at 0.8 ×.