StreamNet：一个支持流图计算的DAG系统
StreamNet: A DAG System with Streaming Graph Computing
摘要：
为了在基于POW的区块链系统中实现高吞吐量，研究人员提出了一系列方法，其中DAG是最活跃和最有前途的领域之一。我们设计并实现了StreamNet，旨在设计一个可扩展和耐用的DAG系统。在DAG中附加新块时，仅选择两个尖端。一种是父端，其定义与Conflux[1]中的定义相同;另一种是使用马尔可夫链蒙特卡罗（MCMC）技术，其定义与IOTA [2]相同。我们沿着图中每个时期的路径推断出一个枢轴链，并且可以在没有集中权威的情况下计算图的总阶数。为了扩大规模，我们利用了图流属性;即使DAG不断增长，也可以实现高事务验证速度。为了向外扩展，我们设计了直接信号流言协议来帮助在网络中传播块更新，从而可以更有效地在网络中传递消息。我们基于IOTA的参考代码（IRI）实现了我们的系统，并在多种网络拓扑的不同大小的集群上进行了全面的实验。
英文摘要：
To achieve high throughput in the POW based blockchain systems, researchers proposed a series of methods, and DAG is one of the most active and promising fields. We designed and implemented the StreamNet, aiming to engineer a scalable and endurable DAG system. When attaching a new block in the DAG, only two tips are selected. One is the parent tip whose definition is the same as in Conflux[1]; another is using Markov Chain Monte Carlo (MCMC) technique by which the definition is the same as IOTA [2]. We infer a pivotal chain along the path of each epoch in the graph, and a total order of the graph could be calculated without a centralized authority. To scale up, we leveraged the graph streaming property; high transaction validation speed will be achieved even if the DAG is growing. To scale out, we designed the direct signal gossip protocol to help disseminate block updates in the network, such that messages can be passed in the network more efficiently. We implemented our system based on IOTA's reference code (IRI) and ran comprehensive experiments over the different sizes of clusters of multiple network topologies.
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。
