Gossip protocol
A gossip protocol or epidemic protocol is a procedure or process of computer peer-to-peer communication that is based on the way epidemics spread. Some distributed systems use peer-to-peer gossip to ensure that data is disseminated to all members of a group. Some ad-hoc networks have no central registry and the only way to spread common data is to rely on each member to pass it along to their neighbors. Gossip communication. The concept of "gossip communication" can be illustrated by the analogy of office workers spreading rumors. Let's say each hour the  office workers congregate around the water cooler. Each employee pairs off with another, chosen at random, and shares the latest gossip. At the start of the day, Dave starts a new rumor: he comments to Bob that he believes that Charlie dyes his mustache. At the next meeting, Bob tells Alice, while Dave repeats the idea to Eve. After each water cooler rendezvous, the number of individuals who have heard the rumor roughly doubles (though this doesn't account for gossiping twice to the same person; perhaps Dave tries to tell the story to Frank, only to find that Frank  already heard it from Alice). Computer systems typically implement this type of protocol with a form of random "peer selection": with a given frequency, each machine picks another machine at random and shares any rumors. Many variants and styles. There are probably hundreds of variants of specific gossip-like protocols because each use-scenario is likely to be customized to the organization's specific needs. For example, a gossip protocol might employ some of these ideas: Gossip protocol types. It is useful to distinguish two prevailing styles of gossip protocol: Many protocols that predate the earliest use of the term  gossip fall within this rather inclusive definition. For example, Internet routing protocols often use gossip-like information exchanges. A gossip substrate can be used to implement a standard routed network: nodes "gossip" about traditional point-to-point messages, effectively pushing traffic through the gossip layer. Bandwidth permitting, this implies that a gossip system can potentially support any classic protocol or implement any classical distributed service. However, such a broadly inclusive interpretation is rarely intended. More typically gossip protocols are those that specifically run in a regular, periodic, relatively lazy, symmetric and decentralized manner; the high degree of symmetry among nodes  is particularly characteristic. Thus, while one could run a 2-phase commit protocol over a gossip substrate, doing so would be at odds with the spirit, if not the wording, of the definition. The term "convergently consistent" is sometimes used to describe protocols that achieve exponentially rapid spread of information. For this purpose, a protocol must propagate any new information to all nodes that will be affected by the information within time logarithmic in the size of the system (the "mixing time" must be logarithmic in system size). Examples. Suppose that we want to find the object that most closely  matches some search pattern, within a network of unknown size, but where the computers are linked to one another and where each machine is running a small "agent" program that implements a gossip protocol. If the messages might get large (for example, if many searches are active all at the same time), a size limit should be introduced. Also, searches should "age out" of the network. It follows that within logarithmic time in the size of the network (the number of agents), any new search string will have reached all agents. Within an additional delay of the same approximate length , every agent will learn where the best match can be found. In particular, the agent that started the search will have found the best match. For example, in a network with 25,000 machines, we can find the best match after about 30 rounds of gossip: 15 to spread the search string and 15 more to discover the best match. A gossip exchange could occur as often as once every tenth of a second without imposing undue load, hence this form of network search could search a big data center in about 3 seconds. In this scenario, searches might automatically age  out of the network after, say, 10 seconds. By then, the initiator knows the answer and there is no point in further gossip about that search. Gossip protocols have also been used for achieving and maintaining distributed database consistency or with other types of data in consistent states, counting the number of nodes in a network of unknown size, spreading news robustly, organizing nodes according to some structuring policy, building so-called overlay networks, computing aggregates, sorting the nodes in a network, electing leaders, etc. Epidemic algorithms. Gossip protocols can be used to propagate information in a manner rather similar  to the way that a viral infection spreads in a biological population. Indeed, the mathematics of epidemics are often used to model the mathematics of gossip communication. The term "epidemic algorithm" is sometimes employed when describing a software system in which this kind of gossip-based information propagation is employed.