Impact of the leader on the performance of geo-distributed BFT consensus

• Lucca Dornelles Cezar PUCRS
• Fernando Dotti PUCRS
• Fernando Pedone USI

DOI: https://doi.org/10.5753/cblockchain.2026.23172

Abstract

BFT consensus performance in blockchains is severely impacted by wide-area networks with a large number of participants. This paper analyzes the impact of leader selection on consensus efficiency and proposes a generic mechanism to prioritize proponents that optimize system throughput. The proposed solution is Byzantine-resilient and robust against network instability. Experimental results demonstrate that the mechanism achieves 80% of the performance of node-eviction techniques, without requiring node removal, thereby maintaining the protocol’s original fault tolerance.

References

Adya, A., Bolosky, W. J., Castro, M., Cermak, G., Chaiken, R., Douceur, J. R., Howell, J., Lorch, J. R., Theimer, M., and Wattenhofer, R. P. (2002). Farsite: Federated, available, and reliable storage for an incompletely trusted environment. ACM SIGOPS Operating Systems Review, 36(SI):1–14.

Amiri, M. J., Wu, C., Agrawal, D., Abbadi, A. E., Loo, B. T., and Sadoghi, M. (2024). The bedrock of byzantine fault tolerance: A unified platform for BFT protocols analysis, implementation, and experimentation. In 21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24), pages 371–400, Santa Clara, CA.

Bessani, A., Correia, M., Quaresma, B., André, F., and Sousa, P. (2013). Depsky: dependable and secure storage in a cloud-of-clouds. Acm transactions on storage (tos), 9(4):1–33.

Buchman, E. (2016). Tendermint: Byzantine fault tolerance in the age of blockchains. Master’s thesis, University of Guelph.

Castro, M., Liskov, B., et al. (1999). Practical byzantine fault tolerance. In OsDI, volume 99, pages 173–186.

Clement, A., Wong, E., Alvisi, L., Dahlin, M., Marchetti, M., et al. (2009). Making byzantine fault tolerant systems tolerate byzantine faults. In Proceedings of the 6th USENIX symposium on Networked systems design and implementation, pages 153–168. The USENIX Association.

Di Perna, V. P., Bernardo, M., Fabris, F., Amaro, S., Matos, M., and Schiavoni, V. (2025). Impact of network topologies on blockchain performance. In Proceedings of the 19th ACM Int. Conf. on Distributed and Event-based Systems, pages 122–133.

Du, N., Liang, Z., Huang, Y., Guo, Z., Yang, H., and Wang, S. (2020). Performance optimisation method of pbft consensus for supply chain integration svm. In 2020 7th international conference on dependable systems and their applications (DSA), pages 371–377. IEEE.

Hafid, A., Hafid, A. S., and Samih, M. (2020). Scaling blockchains: A comprehensive survey. IEEE Access, 8:125244–125262.

Huang, D., Huang, Y., and Yang, Y. (2023). Improved pbft consensus algorithm based on node evaluation and dynamic management. In Proceedings of the 2023 4th International Conference on Big Data Economy and Information Management, pages 851–855.

Lamport, L. (1978). Time, clocks, and the ordering of events in a distributed system. Communications of the ACM, 21(7):558–565.

Schneider, F. B. (1990). Implementing fault-tolerant services using the state machine approach: A tutorial. ACM Computing Surveys, 22(4):299–319.

Shan, B., Gao, T., Song, L., and Cui, Y. (2025). Grouped byzantine fault-tolerant consensus mechanism based on node behaviour analysis. In 2025 4th International Symposium on Computer Applications and Information Technology (ISCAIT), pages 1890–1895. IEEE.

Wen, X. and Yang, X. (2025). Mapbft: multilevel adaptive pbft algorithm based on discourse and reputation models. The Computer Journal, 68(6):635–648.

Xu, J., Wang, C., and Jia, X. (2023). A survey of blockchain consensus protocols. ACM Comput. Surv., 55(13s).

Yu, L., Wu, Y., Lu, J., and Li, T. (2024). An adaptive reputation update mechanism for primary nodes in pbft. In 2024 IEEE 23rd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), pages 1948–1953. IEEE.