AI-driven Predictive Shard Allocation for Scalable Next Generation Blockchains
M. Zeeshan Haider, Tayyaba Noreen, M. D. Assuncao, Kaiwen Zhang

TL;DR
This paper introduces PSAP, a predictive and adaptive shard allocation protocol for blockchains that improves scalability and reduces cross-shard communication by forecasting workloads and dynamically reconfiguring shards.
Contribution
The paper presents a novel framework combining workload forecasting and reinforcement learning for proactive, secure shard allocation in blockchain systems.
Findings
Up to 2x throughput improvement
35% lower latency
20% reduction in cross-shard overhead
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…
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Taxonomy
TopicsBlockchain Technology Applications and Security · Cloud Computing and Resource Management · Big Data and Digital Economy
