Game Paradigm for Wired Networks

Game Paradigm for Wired Networks

Copyright: © 2014 |Pages: 8
DOI: 10.4018/978-1-4666-6050-2.ch016

Abstract

There are a great number of situations in which a many agent system self-organizes by coordinating individual actions. Such coordination is usually achieved by agents with partial information about the system, and in some cases optimizing utility functions that conflict with each other. A similar situation is found in many network situations. An example of a frustrated multi-agent system is given by the evolutionary minority game in which many players have to make a binary choice and the winning option is the one made by the minority. In evolutionary minority game, players make decisions by evaluating the performance of their strategies from past experience, and hence, they can adapt. The players have access to global information, which is in turn generated by the actions of the agents themselves. As the game progresses, non-trivial fluctuations arise in the agents' collective decisions – these can be understood in terms of the dynamical formation of crowds consisting of agents using correlated strategies. This chapter explores the game paradigm for wired networks.
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Evolutionary Minority Game Based Congestion Control (Emgcc) Scheme For Wired Networks

Telecommunication technology advances in the past decade have brought networking to another level in terms of reliability and link speeds. However, existing transmission control protocols do not provide satisfactory performance due to their inefficient congestion control mechanisms. Recently, S. Kim proposed a new Evolutionary Minority Game based Congestion Control (EMGCC) scheme to provide QoS provisioning while ensuring bandwidth efficiency. Based on the evolutionary minority game model, the EMGCC scheme adaptively controls the packet transmission to converge a desirable network equilibrium. For the efficient network management, the evolutionary minority game approach is dynamic and flexible that can adaptively respond to current network conditions.

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