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BPO-CBS: A Data-Driven Blockchain Performance Optimization Framework for Cloud Blockchain Services
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AI
DOI：10.1109/tcc.2026.3677471.png)
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Abstract
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Recently, blockchain has been widely used in important scenarios (e.g., finance and auditing). To fully meet the needs of various business scenarios and reduce deployment costs, cloud blockchain services (CBS) are now being offered by cloud computing providers. However, in high-frequency and large-scale transaction scenarios, blockchain performance faces serious challenges, limiting its further application. Therefore, blockchain performance optimization (BPO) has become a key field. Recent BPO methods that adjust blockchain configuration parameters like block size, offer benefits such as low cost and easy deployment. However, these methods face challenges including unsuitability for dynamic environments, high optimization overhead, and failure to consider marginal utility (MU) in BPO. MU describes the decreasing effectiveness of BPO as transaction arrival rates increases, eventually leading to limited BPO benefits. This paper proposes a data-driven BPO framework (BPO-CBS) for CBS. First, a blockchain performance prediction model is trained using ensemble learning. Second, a performance scoring and adjustment mechanism is designed to identify optimal configuration parameters and adjust them to enhance BPO. Finally, extensive quantitative and qualitative comparisons with related works show that BPO-CBS achieves more effective BPO with low optimization overhead.
Keywords:
Blockchain
BPO
CBS
cloud computing
hyperledger fabric
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