Enhancing Throughput in Sharded Blockchain via Joint Convex Optimization of System Parameters and Resource Allocation

Sharding is a promising technique for improving the throughput and scalability of blockchain systems. However, the imbalanced distribution of transactions across shards poses a major challenge: overloaded shards lead to congestion, while underutilized shards result in wasted resources, both of which hinder system performance. To obtain high throughput, it is crucial to adjust system parameters and allocate resources efficiently. In this paper, we formulate the problem, and propose a joint convex optimization framework that optimizes system parameters and resource allocation to enhance throughput in sharded blockchain systems. Unlike previous studies that focus solely on only one of these aspects, our approach integrates both. By leveraging the block coordinate descent method from convex optimization theory, our proposed approach iteratively solves two interdependent subproblems, system parameter tuning and resource allocation, leading to a near-optimal solution with guaranteed convergence. Extensive experiments demonstrate that our joint optimization algorithm achieves a near-optimal solution within 5 seconds and improves throughput by 3.32% to 14.56% compared to benchmark methods. These results validate the effectiveness of our approach in enhancing both the throughput and scalability of sharded blockchain systems. © 2026 IEEE.