Distributed systems
Understanding distributed systems is essential to the understanding of blockchain technology, as blockchain is a distributed system at its core. It is a distributed ledger which can be centralized or decentralized. A blockchain is originally intended to be and is usually used as a decentralized platform. It can be thought of as a system that has properties of both decentralized and distributed paradigms. It is a decentralized-distributed system.
Distributed systems are a computing paradigm whereby two or more nodes work with each other in a coordinated fashion to achieve a common outcome. It is modeled in such a way that end users see it as a single logical platform. For example, Google's search engine is based on a large distributed system, but to a user, it looks like a single, coherent platform.
A node can be defined as an individual player in a distributed system. All nodes are capable of sending and receiving messages to and from each other. Nodes can be honest, faulty, or malicious, and they have memory and a processor. A node that exhibits irrational behavior is also known as a Byzantine node after the Byzantine Generals Problem.
In 1982, a thought experiment was proposed by Lamport and others in their research paper, The Byzantine Generals Problem which is available at: https://www.microsoft.com/en-us/research/publication/byzantine-generals-problem/ whereby a group of army generals who lead different parts of the Byzantine army are planning to attack or retreat from a city. The only way of communicating among them is via a messenger. They need to agree to strike at the same time in order to win. The issue is that one or more generals might be traitors who could send a misleading message. Therefore, there is a need for a viable mechanism that allows for agreement among the generals, even in the presence of the treacherous ones, so that the attack can still take place at the same time. As an analogy to distributed systems, the generals can be considered nodes, the traitors as Byzantine (malicious) nodes, and the messenger can be thought of as a channel of communication among the generals.
This problem was solved in 1999 by Castro and Liskov who presented the Practical Byzantine Fault Tolerance ( PBFT) algorithm, where consensus is reached after a certain number of messages are received containing the same signed content.
This type of inconsistent behavior of Byzantine nodes can be intentionally malicious, which is detrimental to the operation of the network. Any unexpected behavior by a node on the network, whether malicious or not, can be categorized as Byzantine.
A small-scale example of a distributed system is shown in the following diagram. This distributed system has six nodes out of which one (N4) is a Byzantine node leading to possible data inconsistency. L2 is a link that is broken or slow, and this can lead to partition in the network.
The primary challenge in distributed system design is coordination between nodes and fault tolerance. Even if some of the nodes become faulty or network links break, the distributed system should be able to tolerate this and continue to work to achieve the desired result. This problem has been an active area of distributed system design research for many years, and several algorithms and mechanisms have been proposed to overcome these issues.
Distributed systems are so challenging to design that a hypothesis known as the CAP theorem has been proven, which states that a distributed system cannot have all three of the much-desired properties simultaneously; that is, consistency, availability, and partition tolerance. We will dive into the CAP theorem in more detail later in this chapter.