Gossip plays a very significant role in human society. Information spreads throughout the human grapevine at an amazing speed, often reaching almost everyone in a community, without any central coordinator. Moreover, rumor tends to be extremely stubborn: once spread, it is nearly impossible to erase it. In many distributed computer systems—most notably in cloud computing and peer-to-peer computing—this speed and robustness, combined with algorithmic simplicity and the lack of central management, are very attractive features. Accordingly, over the past few decades several gossip-based algorithms have been developed to solve various problems. In this chapter, we focus on two main manifestations of gossip: information spreading (also known as multicast) where a piece of news is being spread, and information aggregation (or distributed data mining), where distributed information is being summarised. For both topics, we discuss theoretical issues, mostly relying on results from epidemiology, and we also consider design issues and optimisations in distributed applications.
While writing this chapter, M. Jelasity was supported by the Bolyai Scholarship of the Hungarian Academy of Sciences.
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