Research Article
iNet-EGT: An Evolutionarily Stable Adaptation Framework for Network Applications
@INPROCEEDINGS{10.1007/978-3-642-12808-0_4, author={Chonho Lee and Junichi Suzuki and Athanasios Vasilakos}, title={iNet-EGT: An Evolutionarily Stable Adaptation Framework for Network Applications}, proceedings={Bioinspired Models of Network, Information, and Computing Systems. 4th International Conference, BIONETICS 2009, Avignon, France, December 9-11, 2009, Revised Selected Papers}, proceedings_a={BIONETICS}, year={2012}, month={5}, keywords={Artificial immune systems Evolutionary game theory Biologically-inspired networking Autonomous and adaptive networks}, doi={10.1007/978-3-642-12808-0_4} }- Chonho Lee
Junichi Suzuki
Athanasios Vasilakos
Year: 2012
iNet-EGT: An Evolutionarily Stable Adaptation Framework for Network Applications
BIONETICS
Springer
DOI: 10.1007/978-3-642-12808-0_4
Abstract
This paper studies a bio-inspired framework, iNet-EGT, to build autonomous adaptive network applications. In iNet-EGT, each application is designed as a set of agents, each of which provides a functional service and possesses biological behaviors such as migration, replication and death. iNet-EGT implements an adaptive behavior selection mechanism for agents. It is designed after an immune process that produces specific antibodies to antigens (e.g., viruses) for eliminating them. iNet-EGT models a set of network conditions (e.g., workload and resource availability) as an antigen and an agent behavior as an antibody. iNet-EGT allows each agent to autonomously sense its surrounding network conditions (an antigen) and select a behavior (an antibody) according to the conditions. This behavior selection process is modeled as a series of evolutionary games among behaviors. It is theoretically proved to converge to an evolutionarily stable (ES) equilibrium; a specific (i.e., ES) behavior is always selected as the most rational behavior against a particular set of network conditions. This means that iNet-EGT allows every agent to always perform behaviors in a rational and adaptive manner. Simulation results verify this; agents invoke rational (i.e., ES) behaviors and adapt their performance to dynamic network conditions.
