Comparative Analysis of Efficient Load Balancing Techniques in Split-Join Blockchain Architecture
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Abstract
The demand for blockchain technology is growing rapidly in several application areas such as finance, healthcare, and cross-border payment systems. Despite its potential in offering security, the technology is facing limitations related to scalability and performance. The Split-Join blockchain architecture is introduced to address the scalability challenges through enabling parallel block processing and using parallel chains called split-chains, each with a dedicated memory pool. While conducting the performance study of the split-join platform, it is observed that there is a bottleneck with load distribution among the memory pools. This issue is addressed in our previous work by introducing the load balancing unit (LBU), which distributes the incoming transactions based on round robin scheduling strategy among the available memory pools. Further to understand the impact of an efficient load balancing strategy within the split-join blockchain framework, this study presents a comparative evaluation of three load balancing strategies: Round Robin, Least-Loaded, and Predictive Assignment. Experiments were conducted using different transaction loads ranging from 100 to 5000, and the results are analysed based on key performance metrics, Transactions Per Second (TPS), and processing time. Results indicate that Predictive Assignment consistently delivers the highest throughput, achieving up to 250 transactions per second, around 13.4 percent improvement over Round Robin strategy with 216 TPS. Least-Loaded Assignment strategy has shown a little improvement up to 226 TPS. These findings signify that the predictive assignment can boost the transaction throughput, especially in a split-join architecture, offering insights into more efficient and scalable network designs.
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