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arXiv 2404.04438cs.DC
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对抗性交易生成下的稳定区块链分片

Stable Blockchain Sharding under Adversarial Transaction Generation

Ramesh Adhikari, Costas Busch, Dariusz R. Kowalski

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11 pages, 3 figures
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AI总结：

针对对抗性交易生成下的区块链分片稳定性问题，提出均匀与非均匀系统的分布式调度算法，给出注入速率上界并首次进行对抗性稳定性分析。

摘要：

分片用于提高区块链系统的可扩展性和性能。我们研究了区块链分片的稳定性，其中交易由对抗性模型持续生成。系统由 $n$ 个处理节点组成，这些节点被划分为 $s$ 个分片。遵循经典对抗性排队理论的范式，交易以注入速率 $\rho\leq 1$ 和突发性 $b > 0$ 持续到达。我们给出了任何调度器能够保证交易队列有界和延迟有界的最大注入速率的绝对上界 $\max\{ \frac{2}{k+1}, \frac{2}{ \left\lfloor\sqrt{2s}\right\rfloor}\}$，其中 $k$ 是每笔交易访问的分片数量。接下来，我们为分片彼此等距的均匀系统给出一种基本的分布式调度算法。为保证稳定性，注入速率限制为 $\rho\leq \max\{ \frac{1}{18k}, \frac{1}{\lceil 18 \sqrt{s} \rceil} \}$。然后，我们为分片彼此任意远的非均匀系统提供一种完全分布式调度算法。通过使用分片的层次聚类，在注入速率 $\rho\leq \frac{1}{c_1d \log^2 s} \cdot \max\{ \frac{1}{k}, \frac{1}{\sqrt{s}} \}$ 下保证稳定性，其中 $d$ 是任何交易到其将访问的分片的最差距离，$c_1$ 是某个正常数。我们还进行了仿真以评估算法，并测量整个系统的平均队列大小和延迟。据我们所知，这是对分片区块链系统的首次对抗性稳定性分析。

英文摘要：

Sharding is used to improve the scalability and performance of blockchain systems. We investigate the stability of blockchain sharding, where transactions are continuously generated by an adversarial model. The system consists of $n$ processing nodes that are divided into $s$ shards. Following the paradigm of classical adversarial queuing theory, transactions are continuously received at injection rate $ρ\leq 1$ and burstiness $b > 0$. We give an absolute upper bound $\max\{ \frac{2}{k+1}, \frac{2}{ \left\lfloor\sqrt{2s}\right\rfloor}\}$ on the maximum injection rate for which any scheduler could guarantee bounded queues and latency of transactions, where $k$ is the number of shards that each transaction accesses. We next give a basic distributed scheduling algorithm for uniform systems where shards are equally close to each other. To guarantee stability, the injection rate is limited to $ρ\leq \max\{ \frac{1}{18k}, \frac{1}{\lceil 18 \sqrt{s} \rceil} \}$. We then provide a fully distributed scheduling algorithm for non-uniform systems where shards are arbitrarily far from each other. By using a hierarchical clustering of the shards, stability is guaranteed with injection rate $ρ\leq \frac{1}{c_1d \log^2 s} \cdot \max\{ \frac{1}{k}, \frac{1}{\sqrt{s}} \}$, where $d$ is the worst distance of any transaction to the shards it will access, and $c_1$ is some positive constant. We also conduct simulations to evaluate the algorithms and measure the average queue sizes and latency throughout the system. To our knowledge, this is the first adversarial stability analysis of sharded blockchain systems.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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