自适应节点规模的区块链分片可扩展模型

doi:10.19678/j.issn.1000-3428.0066930

摘要/Abstract

摘要：

分片是解决区块链可扩展性问题的核心技术，然而现有分片方案普遍采用预定分片规模的静态分片方式，这与公链开放低门槛的分布式环境不匹配。当网络中的节点数大幅增加时静态分片方式难以及时充分地发挥全部节点的性能，当网络中的节点数大幅减少时又会增加分片内的安全隐患。为此，构建一种自适应节点规模变化的动态分片可扩展模型（DSSM）。在基础分片上建立分层的逻辑分片，通过支持状态归约允许节点在不同层级的分片上进行状态同步。在逻辑与基础分片间建立满二叉树的逻辑关系，通过分片的动态分裂和合并来扩张和收缩分片规模，实现分片规模的自适应调整。实验结果表明，DSSM在节点数量大幅增加时通过自适应扩展分片规模使网络吞吐量得到了近乎翻倍的提升，在节点数量大幅减少时通过自适应收缩分片规模保证了网络的最低安全要求。

关键词: 区块链, 自适应节点规模, 状态分片, 归约节点, 状态冗余

Abstract:

Sharding is one of the core technologies to address the scalability of blockchains. However, the existing sharding schemes generally adopt a static sharding method with a predetermined shards' scale, which does not match the open and low-threshold distributed environment of the public blockchain. When the number of nodes in the network increases significantly, it is difficult for the static sharding method to fully utilize the performance of all nodes in a timely manner, and when the number of nodes in the network is significantly reduced, it will increase the security risks in the shards. Accordingly, a Dynamic Sharding Scalable Model(DSSM) with an adaptive change of nodes' scale is constructed. On the basis of basic shards, this model establishes layered logical shards, supports state reduction, and allows nodes to synchronize the state of shards at different levels. A logical relationship of a full binary tree is established between logical and basic shards. To achieve adaptive adjustment, the scale of the shards is expanded and shrunk through dynamic splitting and merging of the shards. The experiments have demonstrated that when the number of nodes increases significantly, the DSSM can nearly double the throughput by adaptively expanding the scale of shards, and when the number of nodes is significantly reduced, the DSSM guarantees the minimum security requirements of the network by adaptively shrinking the scale of shards.

Key words: blockchain, adaptive node scale, state sharding, reduction node, state redundancy

李宝莹, 李志淮, 王成爱, 杨锋. 自适应节点规模的区块链分片可扩展模型[J]. 计算机工程, 2024, 50(3): 137-147.

Baoying LI, Zhihuai LI, Chengai WANG, Feng YANG. Blockchain Sharding Scalable Model of Adaptive Node Scale[J]. Computer Engineering, 2024, 50(3): 137-147.

https://www.ecice06.com/CN/Y2024/V50/I3/137

图/表 12

图1 分片间的逻辑关系

Fig.1 Logical relationship between shards

图2 DSSM归约模型

Fig.2 Reduction model of DSSM

图3 消息传递规则

Fig.3 Messaging rules

图4 预准备阶段的消息传递路线

Fig.4 Messaging routes of pre-prepare phase

图5 拒绝请求消息传递路线

Fig.5 Reject request messaging routes

图6 节点复用与分片降级

Fig.6 Node reuse and sharding degradation

图7 合谋攻击代价

Fig.7 Cost of collusion attack

图8 吞吐能力在分裂过程中的变化

Fig.8 Changes of throughput capacity during splitting process

图9 吞吐能力在合并过程中的变化

Fig.9 Changes of throughput capacity during merging process

图10 可扩展性对比

Fig.10 Scalability comparison

图11 交易时延对比

Fig.11 Transaction latency comparison

参考文献 26

1	NAKAMOTO S. Bitcoin: a peer-to-peer electronic cash system[EB/OL]. [2023-01-11]. http://bitcoin.org/bitcoin.pdf.
2	JAVARONE M A, WRIGHT C S. From bitcoin to bitcoin cash: a network analysis[C]//Proceedings of the 1st Workshop on Cryptocurrencies and Blockchains for Distributed Systems. New York, USA: ACM Press, 2018: 77-81.
3	WANG Q. Improving the scalability of blockchain through DAG[C]//Proceedings of the 20th International Middleware Conference. New York, USA: ACM Press, 2019: 34-35.
4	高政风, 郑继来, 汤舒扬, 等. 基于DAG的分布式账本共识机制研究. 软件学报, 2020, 31 (4): 1124- 1142. URL
	GAO Z F, ZHENG J L, TANG S Y, et al. State-of-the-art survey of consensus mechanisms on DAG-based distributed ledger. Journal of Software, 2020, 31 (4): 1124- 1142. URL
5	SILVANO W F, MARCELINO R. Iota Tangle: a cryptocurrency to communicate Internet-of-Things data. Future Generation Computer Systems, 2020, 112, 307- 319. doi: 10.1016/j.future.2020.05.047
6	DANG H, DINH T T A, LOGHIN D, et al. Towards scaling blockchain systems via sharding[C]//Proceedings of 2019 International Conference on Management of Data. New York, USA: ACM Press, 2019: 123-140.
7	YU G S, WANG X, YU K, et al. Survey: sharding in blockchains. IEEE Access, 2020, 8, 14155- 14181. doi: 10.1109/ACCESS.2020.2965147
8	POON J, BUTERIN V. Plasma: scalable autonomous smart contracts[EB/OL]. [2023-01-11]. https://www.plasma.io/plasma-deprecated.pdf.
9	DZIEMBOWSKI S, FAUST S, HOSTÁKOVÁ K. General state channel networks[C]//Proceedings of 2018 ACM SIGSAC Conference on Computer and Communications Security. New York, USA: ACM Press, 2018: 949-966.
10	MICALI S, RABIN M, VADHAN S. Verifiable random functions[C]//Proceedings of the 40th Annual Symposium on Foundations of Computer Science. Washington D. C., USA: IEEE Press, 1999: 120-130.
11	GILAD Y, HEMO R, MICALI S, et al. Algorand: scaling Byzantine agreements for cryptocurrencies[C]//Proceedings of the 26th Symposium on Operating Systems Principles. New York, USA: ACM Press, 2017: 51-68.
12	BONEH D, BONNEAU J, BÜNZ B, et al. Verifiable delay functions[C]//Proceedings of the 38th Annual International Cryptology Conference. New York, USA: ACM Press, 2018: 757-788.
13	HackMD. New sharding design with tight beacon and shard block integration[EB/OL]. [2023-01-11]. https://notes.ethereum.org/@dankrad/new_sharding.
14	WANG J, WANG H. Monoxide: scale out blockchains with asynchronous consensus zones[C]//Proceedings of the 16th USENIX Symposium on Networked Systems Design and Implementation. New York, USA: ACM Press, 2019: 95-112.
15	SKIDANOV A, POLOSUKHIN I. Nightshade: near protocol sharding design[EB/OL]. [2023-01-11]. https://near.org/papers/nightshade.
16	CASTRO M, LISKOV B. Practical Byzantine fault tolerance[C]//Proceedings of the 3rd Symposium on Operating Systems Design and Implementation. New York, USA: ACM Press, 1999: 173-186.
17	厚香莹, 李志淮, 李宝莹. 容忍高比例拜占庭节点的区块链分片扩容算法[EB/OL]. [2023-01-11]. http://www.paper.edu.cn/releasepaper/content/202110-29.
	HOU X Y, LI Z H, LI B Y. Blockchain fragment expansion algorithm tolerant of high proportion of Byzantine nodes[EB/OL]. [2023-01-11]. http://www.paper.edu.cn/releasepaper/content/202110-29. (in Chinese)
18	ZHOU Z X, QIU Z J, YU Q, et al. A dynamic sharding protocol design for consortium blockchains[C]//Proceedings of IEEE International Conference on Big Data. Washington D. C., USA: IEEE Press, 2020: 2590-2595.
19	ZHANG J T, HONG Z C, QIU X Y, et al. SkyChain: a deep reinforcement learning-empowered dynamic blockchain sharding system[C]//Proceedings of the 49th International Conference on Parallel Processing. New York, USA: ACM Press, 2020: 1-11.
20	SET S K, PARK G S. Service-aware dynamic sharding approach for scalable blockchain. IEEE Transactions on Services Computing, 2023, 16 (4): 2954- 2969. doi: 10.1109/TSC.2022.3231619
21	KHACEF K, BENBERNOU S, OUZIRI M, et al. Trade-off between security and scalability in blockchain design: a dynamic sharding approach[C]//Proceedings of International Conference on Deep Learning, Big Data and Blockchain. Berlin, Germany: Springer, 2022: 77-90.
22	KIAYIAS A, RUSSELL A, DAVID B, et al. Ouroboros: a provably secure proof-of-stake blockchain protocol[C]//Proceedings of Annual International Cryptology Conference. Berlin, Germany: Springer, 2017: 357-388.
23	KOKORIS-KOGIAS E, JOVANOVIC P, GASSER L, et al. OmniLedger: a secure, scale-out, decentralized ledger via sharding[C]//Proceedings of IEEE Symposium on Security and Privacy. Washington D. C., USA: IEEE Press, 2018: 583-598.
24	LUU L, NARAYANAN V, ZHENG C D, et al. A secure sharding protocol for open blockchains[C]//Proceedings of 2016 ACM SIGSAC Conference on Computer and Communications Security. New York, USA: ACM Press, 2016: 17-30.
25	PASS R, SEEMAN L, SHELAT A. Analysis of the blockchain protocol in asynchronous networks[C]//Proceedings of Annual International Conference on the Theory and Applications of Cryptographic Techniques. Berlin, Germany: Springer, 2017: 643-673.
26	LEE B, JEONG Y, SONG H, et al. A scalable and highly available network management architecture on consistent hashing[C]//Proceedings of IEEE Global Telecommunications Conference. Washington D. C., USA: IEEE Press, 2010: 1-6.

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