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Super peer selection strategy in peer‐to‐peer networks based on learning automata
Author(s) -
Safara Fatemeh,
Souri Alireza,
Deiman Sara Fathipour
Publication year - 2020
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.4296
Subject(s) - computer science , peer to peer , selection (genetic algorithm) , learning automata , distributed computing , cellular automaton , automaton , artificial intelligence , machine learning
Summary A super peer is a peer that has the highest capacity in comparison with other peers in the network. It is trying to reduce the load of the rest of the peers and improve network performance. Selecting a super peer in a peer‐to‐peer–based network is a very crucial challenge. As the ability of peers are very different, by considering capacity of each peer and selecting a proper role, we can use network components much more efficiently. Because of the dynamicity of these networks, comparative methods of selecting super peers is of special importance. Comparative selection is continuously trying to select proper super peer. In recent studies, learning automata was introduced as a powerful learning model to solve this issue. In most of the studies, learning automata with an S model is employed. In this article, another selection method of learning automata with a P model environment is presented and its capability for super peer selection is shown. Moreover, simulation results show that removing some of the super peers would result in better performance in terms of inversion time in the high level of super‐peer capacity, required time for selecting proper super peer, and super peer tolerance.