What Blocks My Blockchain's Throughput? Developing a Generalizable Approach for Identifying Bottlenecks in Permissioned Blockchains
<|reference_start|>What Blocks My Blockchain's Throughput? Developing a Generalizable Approach for Identifying Bottlenecks in Permissioned Blockchains: Permissioned blockchains have been proposed for a variety of use cases that require decentralization yet address enterprise requirements that permissionless blockchains to date cannot satisfy -- particularly in terms of performance. However, popular permissioned blockchains still exhibit a relatively low maximum throughput in comparison to established centralized systems. Consequently, researchers have conducted several benchmarking studies on different permissioned blockchains to identify their limitations and -- in some cases -- their bottlenecks in an attempt to find avenues for improvement. Yet, these approaches are highly heterogeneous, difficult to compare, and require a high level of expertise in the implementation of the underlying specific blockchain. In this paper, we develop a more unified and graphical approach for identifying bottlenecks in permissioned blockchains based on a systematic review of related work, experiments with the Distributed Ledger Performance Scan (DLPS), and an extension of its graphical evaluation functionalities. We conduct in-depth case studies on Hyperledger Fabric and Quorum, two widely used permissioned blockchains with distinct architectural designs, demonstrating the adaptability of our framework across different blockchains. We provide researchers and practitioners working on evaluating or improving permissioned blockchains with a toolkit, guidelines on what data to document, and insights on how to proceed in the search process for bottlenecks.<|reference_end|>
@article{papageorgiou2024what,
    title={What Blocks My Blockchain's Throughput? Developing a Generalizable
  Approach for Identifying Bottlenecks in Permissioned Blockchains},
    author={Orestis Papageorgiou, Lasse B"ortzler, Egor Ermolaev, Jyoti Kumari,
  Johannes Sedlmeir},
    journal={arXiv preprint arXiv:2404.02930},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.02930},
    primaryClass={cs.CR cs.DB}
}