论文标题
基于DAG的区块链系统网络增长过程的马尔可夫流程理论
A Markov Process Theory for Network Growth Processes of DAG-based Blockchain Systems
论文作者
论文摘要
请注意,区块链的串行结构具有许多必不可少的陷阱,因此引入了数据网络结构及其基于DAG的区块链来解决区块链陷阱。从这样的网络角度来看,对基于DAG的区块链系统的分析变得有趣且具有挑战性。因此,模拟模型被广泛采用。在本文中,我们首先通过两层提示和内部尖端的不耐烦的连接行为来描述具有IOTA缠结的基于DAG的区块链的简单模型。然后,我们设置了一个连续的马尔可夫流程,以分析基于DAG的区块链系统,并表明该Markov过程是一个依赖水平的准胎 - 死亡(QBD)过程。基于此,我们证明QBD过程必须是不可还原且积极的。此外,一旦给出了QBD过程的固定概率向量,我们将提供基于DAG的区块链系统的性能分析。接下来,我们提出了一种新的有效方法,用于通过第一个段落时间和pH分布来计算该系统中任何内部提示的平均确认时间。最后,我们使用数值示例来检查我们的理论结果的有效性,并指示某些关键系统参数如何影响该系统的性能度量。因此,我们希望本文中开发的方法和结果可以适用于处理更一般的基于DAG的区块链系统,从而有可能开发一系列有希望的研究。
Note that the serial structure of blockchain has many essential pitfalls, thus a data network structure and its DAG-based blockchain are introduced to resolve the blockchain pitfalls. From such a network perspective, analysis of the DAG-based blockchain systems becomes interesting and challenging. So, the simulation models are adopted widely. In this paper, we first describe a simple Markov model for the DAG-based blockchain with IOTA Tangle by means of two layers of tips and internal tips' impatient connection behavior. Then we set up a continuous-time Markov process to analyze the DAG-based blockchain system and show that this Markov process is a level-dependent quasi-birth-and-death (QBD) process. Based on this, we prove that the QBD process must be irreducible and positive recurrent. Furthermore, once the stationary probability vector of the QBD process is given, we provide performance analysis of the DAG-based blockchain system. Next, we propose a new effective method for computing the average confirmation time of any arriving internal tip at this system by means of the first passage times and the PH distributions. Finally, we use numerical examples to check the validity of our theoretical results and indicate how some key system parameters influence the performance measures of this system. Therefore, we hope that the methodology and results developed in this paper can be applicable to deal with more general DAG-based blockchain systems such that a series of promising research can be developed potentially.