BFTBrain: Adaptive BFT Consensus with Reinforcement Learning
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916355274440704 |
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| author | Wu, Chenyuan Qin, Haoyun Amiri, Mohammad Javad Loo, Boon Thau Malkhi, Dahlia Marcus, Ryan |
| author_facet | Wu, Chenyuan Qin, Haoyun Amiri, Mohammad Javad Loo, Boon Thau Malkhi, Dahlia Marcus, Ryan |
| contents | This paper presents BFTBrain, a reinforcement learning (RL) based Byzantine fault-tolerant (BFT) system that provides significant operational benefits: a plug-and-play system suitable for a broad set of hardware and network configurations, and adjusts effectively in real-time to changing fault scenarios and workloads. BFTBrain adapts to system conditions and application needs by switching between a set of BFT protocols in real-time. Two main advances contribute to BFTBrain's agility and performance. First, BFTBrain is based on a systematic, thorough modeling of metrics that correlate the performance of the studied BFT protocols with varying fault scenarios and workloads. These metrics are fed as features to BFTBrain's RL engine in order to choose the best-performing BFT protocols in real-time. Second, BFTBrain coordinates RL in a decentralized manner which is resilient to adversarial data pollution, where nodes share local metering values and reach the same learning output by consensus. As a result, in addition to providing significant operational benefits, BFTBrain improves throughput over fixed protocols by $18\%$ to $119\%$ under dynamic conditions and outperforms state-of-the-art learning based approaches by $44\%$ to $154\%$. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_06432 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BFTBrain: Adaptive BFT Consensus with Reinforcement Learning Wu, Chenyuan Qin, Haoyun Amiri, Mohammad Javad Loo, Boon Thau Malkhi, Dahlia Marcus, Ryan Distributed, Parallel, and Cluster Computing This paper presents BFTBrain, a reinforcement learning (RL) based Byzantine fault-tolerant (BFT) system that provides significant operational benefits: a plug-and-play system suitable for a broad set of hardware and network configurations, and adjusts effectively in real-time to changing fault scenarios and workloads. BFTBrain adapts to system conditions and application needs by switching between a set of BFT protocols in real-time. Two main advances contribute to BFTBrain's agility and performance. First, BFTBrain is based on a systematic, thorough modeling of metrics that correlate the performance of the studied BFT protocols with varying fault scenarios and workloads. These metrics are fed as features to BFTBrain's RL engine in order to choose the best-performing BFT protocols in real-time. Second, BFTBrain coordinates RL in a decentralized manner which is resilient to adversarial data pollution, where nodes share local metering values and reach the same learning output by consensus. As a result, in addition to providing significant operational benefits, BFTBrain improves throughput over fixed protocols by $18\%$ to $119\%$ under dynamic conditions and outperforms state-of-the-art learning based approaches by $44\%$ to $154\%$. |
| title | BFTBrain: Adaptive BFT Consensus with Reinforcement Learning |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2408.06432 |