误差分类如下。首先，错误或故障可能是遗漏故障，其中的故障要么是流程对接收请求没有响应。或者，当进程根本没有接收到请求时，可能会发生故障。第二个错误称为“执行失败或撒谎”。这是一个错误的数据或不一致的数据发送失败。这可能会破坏正在等待发送某种形式的数据的本地状态。当格式不匹配时，会导致破坏和不一致，在此上下文中观察到的一些更常见的错误如下。首先，有四舍五入误差，这是函数向数据库传递一个变量的结果，或者当已经损坏的数据库未能检测到错误中的变量时(Ghosh, 2014)。此外，还可能存在编译器错误的问题，在这种情况下，flips可能生成带有错误的消息。研究人员提出的拜占庭问题抓住了这个问题的核心。拜占庭的一般问题通常被解释为军队开始围攻一个城市，但随后军队得到了支付，因为这将导致计划的最高执行效率(Pan & Zhang, 2012)。然而，这里所发生的是，只有通过适当的沟通才能保证成功的做法出现了错误。
有些叛徒发布了错误的信息。这些都是流氓程序。它们也可能是分布式编程和进程中的数据错误(Varela & Agha, 2013)。现在的挑战是找到一个算法，当用来实现战术行动计划。这将能够很好地处理错误，并确保流程也是安全的。作者Cachin等人(2011)的工作是这样的，它提出了一个抽象的问题。研究中提出的拜占庭式容错(Byzantine fault tolerance)是程序执行时能够抵御故障的级别或能力(Hwang et al.， 2013;Hussain等，2013)。在讨论拜占庭式失败的书中，也详细讨论了这种失败发生的一些武断的方式。这里讨论了研究人员关于停止崩溃、进程请求失败交付和地方政府腐败的争论。这里使用的真实世界模型对于分布式程序员来说是很有用的，他们试图理解过程中断上下文中的程序和系统故障。
The error is classified as follows. Firstly, the error or fault could be an omission failure where the failure is either non response to reception of request by a process. Alternatively, the failure could happen when a process does not receive the request at all. The second error is called the ‘Execution Failure or Lying’. This is a failure where incorrect data or inconsistent data is sent. This could corrupt the local state which is waiting for some form of data to be sent. When the format does not match, corruption and inconsistency results, some of the more common errors that are observed in this context are as follows. Firstly, there are rounding off errors which are a result of a function passing one variable to the database or when an already corrupted database fails to detect the variable that is in error (Ghosh, 2014). In addition, there can be issues of compiler error where flips can be producing a message with an error. The Byzantine general problem as presented by researchers captures the core of this issue. The Byzantine’s general problem is explained conventionally as one in which armies set out to besiege a city but then the army gets disbursed as this would result in maximum efficiency of implementation of the plan (Pan & Zhang, 2012). However, what happens here is, success that can be guaranteed only by means of having proper communication goes awry.
There are traitors that send out wrong information. These are the rogue processes. They can also be data errors in the case of distributed programming and processes (Varela & Agha, 2013). Now the challenge is to find out an algorithm which when used to implement a tactical plan of action. This would enable the errors to be handled well as well as ensures that processes are also fail safe. The work of authors Cachin et al. (2011) is such that it presents an abstraction of the problem. Associated algorithms could help defend against such errors The Byzantine fault tolerance as presented in research is the level or capability with which the program implemented can defend against failures (Hwang et al., 2013; Hussain et al., 2013). The book in discussing the Byzantine failures also discusses in detail a number of arbitrary ways in which such a failure occurs. Researchers’ argument for crashing by stopping, process request failed delivery and local state corruption is discussed here. The real world modelling used here is useful to understand for distributed programmers who seek to understand program and system failure in the context of process disruption.