The resolution of the problem is based on an algorithm that can guarantee it: in this article, we discussed some general information on the problem of consensus in distributed systems. What my son does not yet know or understand (apart from the (s) answer (s)!) is the profound impact of the problem and the practical application of the solution that is mentioned in the document entitled “Bitcoin: A Peer-to-Peer Electronic Cash System” published in 2008 by Satoshi Nakamoto. The problem is complicated by the presence of insidious generals, who vote not only in favour of a suboptimal strategy, but also selectively. For example, if nine generals vote, four of whom support an attack, while four others are in favour of withdrawal, the ninth general may send a withdrawal vote to these generals in favour of withdrawal and one vote to attack the rest. Those who have obtained a withdrawal vote from the ninth general will withdraw while the rest will attack (which may not be good for the attackers). The problem is further complicated by the fact that generals must be physically separated and send their votes through messengers who might not vote or falsify false votes. Reliability is an important research topic in distributed computing systems, which consist of a large number of processors. To achieve reliability, it is necessary to review the error tolerance scheme of the distributed calculation system. This type of problem is known as a Byzantine agreement (BA) problem.
It requires all flawless processors to agree on a common value, even if some components are damaged. As a result, important studies on this problem of the agreement have been conducted in distributed systems. However, traditional BA protocols focus on continuously executing ⌊ (n-1) /3⌋-1 message exchange rounds, so that each flawless processor can reach an agreement. In other words, because a large number of messages result in a large protocol overload, these protocols are ineffective and inappropriate, especially for some network environments with a large number of processors. In this study, we propose a new and effective protocol to reduce the number of messages. Our protocol can collect, compare and replace the values received to find reliable processors and replace the values sent by unreliable processors. Each processor can then agree on a common value through three message exchange rounds. In addition, the proposed protocol may use the minimum number of messages to tolerate the maximum number of defective components in a distributed system. Each lieutenant value in a particular exercise has the same paths for all its knots, and in this case, since only the general is defective, we know that all lieutenants will have the same input values on all his leaves.
As a result, all processes agree on the same value, 1 that fulfills the quality of the agreement. This situation does not necessarily improve simply by putting more defective processes on the problem. A naïve algorithm, as shown in Figure 1 and Figure 2, could tell any process what it received from P1. A process would then decide fair value by taking a simple majority of values in its incoming messages. This problem is based on an imaginary general who makes the decision to attack or withdraw and must notify his lieutenants of the decision. A number of these actors are traitors (including possibly the general). You cannot rely on traitors to communicate orders correctly; Worse, they can actively change the news to undermine the process. As Mike Maloney explains in his recent documentary, the problem of Byzantine generals can be summed up as a question: how do we ensure that several entities separated by distance completely coincide before action is taken? Some aircraft systems, such as Boeing 777 Aircraft`s information management system (via its ARINC 659 SAFEbus network),  use the Byzantine margin of error for Boeing 777 flight control systems and Boeing 787 flight control systems; Because these are real-time systems, Byzantine error-tolerance solutions must have very low latency.