Dynamic Sharding with AI-Driven Load Balancing for Blockchain Networks

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Experimental results show dynamic sharding improves transaction throughput by 2.7x, suggesting better scalability and load balancing.

The dynamic sharding mechanism achieves a 2.7x improvement in transaction throughput compared to static methods.
It reduces cross-shard transactions by 43% and overall network latency by 38%, enhancing efficiency.
Reinforcement learning algorithms, DQN and PPO, are utilized for real-time transaction allocation across shards.
The framework upholds decentralization while improving performance, beneficial for enterprise-level applications.

Devi et al. (2025) studied this question.

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View Full Paper

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Experimental results show dynamic sharding improves transaction throughput by 2.7x, suggesting better scalability and load balancing.

The dynamic sharding mechanism achieves a 2.7x improvement in transaction throughput compared to static methods.
It reduces cross-shard transactions by 43% and overall network latency by 38%, enhancing efficiency.
Reinforcement learning algorithms, DQN and PPO, are utilized for real-time transaction allocation across shards.
The framework upholds decentralization while improving performance, beneficial for enterprise-level applications.

Devi et al. (2025) studied this question.

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