Abstract
This chapter studies the PBFT-based sharded permissioned blockchain, which executes in either a local datacenter or a rented cloud platform. In such permissioned blockchain, the transaction (TX) assignment strategy could be malicious such that the network shards may possibly receive imbalanced transactions or even bursty-TX injection attacks. An imbalanced transaction assignment brings serious threats to the stability of the sharded blockchain. A stable sharded blockchain can ensure that each shard processes the arrived transactions timely. Since the system stability is closely related to the blockchain throughput, how to maintain a stable sharded blockchain becomes a challenge. To depict the transaction processing in each network shard, we adopt the Lyapunov Optimization framework. Exploiting drift-plus-penalty (DPP) technique, we then propose an adaptive resource-allocation algorithm, which can yield the near-optimal solution for each network shard while the shard queues can also be stably maintained. We also rigorously analyze the theoretical boundaries of both the system objective and the queue length of shards. The numerical results show that the proposed algorithm can achieve a better balance between resource consumption and queue stability than other baselines. We particularly evaluate two representative cases of bursty-TX injection attacks, i.e., the continued attacks against all network shards and the drastic attacks against a single network shard. The evaluation results show that the DPP-based algorithm can well alleviate the imbalanced TX assignment, and simultaneously maintain high throughput while consuming fewer resources than other baselines.
â’¸ 2022 IEEE https://doi.org/10.1109/TPDS.2022.3141737.
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Acknowledgements
This Work is partially supported by National Key R&D Program of China (No.2020YFB1006005), National Natural Science Foundation of China (61902445), Guangdong Basic and Applied Basic Research Foundation (2019A1515011798), Guangzhou Basic and Applied Basic Research Foundation (202102020613), Pearl River Talent Recruitment Program (No. 2019QN01X130), CCF-Huawei Populus euphratica forest fund (CCF-HuaweiBC2021004), Hong Kong RGC Research Impact Fund (RIF) with the Project No. R5060-19, General Research Fund (GRF) with the Project No. 152221/19E, 152203/20E, and 152244/21E, the National Natural Science Foundation of China (61872310), and Shenzhen Science and Technology Innovation Commission (R2020A045).
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Huang, H. et al. (2023). Elastic Resource Allocation in Sharding-Based Blockchains. In: Chen, W., Zheng, Z., Huang, H. (eds) Blockchain Scalability. Springer, Singapore. https://doi.org/10.1007/978-981-99-1059-5_7
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DOI: https://doi.org/10.1007/978-981-99-1059-5_7
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