Abstract
This paper investigates a bio-inspired framework, iNet- EGT/C, to build adaptive, cooperative and stable network applications. In this framework, each application is designed as a decentralized set of agents, each of which provides a functional service and possesses biological behaviors such as migration, replication and death. iNet-EGT/C implements an adaptive behavior selection mechanism for agents. Its selection process is modeled as a series of evolutionary games among behaviors. iNet-EGT/C allows agents to seek to operate at evolutionarily stable equilibria and adapt to dynamic networks by invoking evolutionarily stable behaviors. It is theoretically proved that each behavior selection process retains stability (i.e., reachability to at least one evolutionarily stable equilibrium). iNet-EGT/C leverages the notion of coalitions for agents to select behaviors as coalitional decisions in a cooperative manner rather than individual decisions in a selfish manner. This cooperative behavior selection accelerates agents’ adaptation speed by up to 78%.
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References
Weiss, A.: Computing in the clouds. ACM netWorker Magazine 11(4) (2007)
Lee, C., Suzuki, J.: An immunologically-inspired autonomic framework for self- organizing and evolvable network applications. ACM Trans. Autonomous and Adaptive Systems 4(4) (2009)
Lee, C., Vasilakos, A.V., Suzuki, J.: iNet-EGT: An evolutionarily stable adaptation framework for network applications. In: Proc. of Int’l Conference on Bio-inspired Models of Network, Information and Computing Systems (December 2009)
Subrata, R., Zomaya, A.Y., Landfeldt, B.: Game theoretic approach for load balancing in computational grids. IEEE Transactions on Parallel and Distributed Systems 19(1) (2008)
Kodialam, M., Lakshman, T.V.: Detecting network intrusions via sampling: a game theoretic approach. In: Proc. of IEEE Int’l. Conf. on Computer Comm. (2003)
Agah, A., Basu, K., Das, S.K.: Preventing dos attack in sensor networks: a game theoretic approach. In: Proc. of IEEE Int’l. Conf. on Comm. (May 2005)
Otrok, H., Mehrandish, M., Assi, C.: Game theoretic models for detecting network intrusions. Computer Communications 31(10) (June 2008)
Kannan, R., Iyengar, S.: Game theoretic models for reliable path-length and energy constrained routing with data aggregation in wireless sensor networks. IEEE Journal on Selected Areas in Communications 22(6) (2004)
Yan, L., Hailes, S.: Cooperative packet relaying model for wireless ad hoc networks. In: Proc. of the First ACM International Workshop on Foundations of Wireless Ad Hoc and Sensor Networking and Computing (May 2008)
Vasilakos, A.V., Anastasopoulos, M.: Application of evolutionary game theory to wireless mesh networks. In: Jain, L.C., Palade, V., Srinivasan, D. (eds.) Advances in Evolutionary Computing for System Design. Springer (2007)
Anastasopoulos, M.P., Petraki, D.K., Kannan, R., Vasilakos, A.V.: Tcp throughput adaptation in wimax networks using replicator dynamics. IEEE Transactions on Systems, Man, and Cybernetics (January 2010)
Li, Z., Parashar, M.: Rudder: A rule-based multi-agent infrastructure for supporting autonomic grid applications. In: Proc. of IEEE International Conference on Autonomic Computing (2004)
Hagimont, D., Bouchenak, S., Palma, N., Taton, C.: Self-sizing of clustered databases. In: Proc. of International Symposium on World of Wireless, Mobile and Multimedia Networks (June 2006)
Wang, Y., Li, S., Chen, Q., Hu, W.-l.: Biology Inspired Robot Behavior Selection Mechanism: Using Genetic Algorithm. In: Li, K., Fei, M., Irwin, G.W., Ma, S. (eds.) LSMS 2007. LNCS, vol. 4688, pp. 777–786. Springer, Heidelberg (2007)
Damas, B.D., Custódio, L.: Emotion-based decision and learning using associative memory and statistical estimation. Informatica 27(2) (2003)
Kim, K.-J., Cho, S.-B.: Bn+bn: Behavior network with bayesian network for intelligent agent. In: Proc. of AFOSR Australian Conf. on Artificial Intelligence (December 2003)
Weibull, J.W.: Evolutionary Game Theory. MIT Press (1996)
Taylor, P., Jonker, L.: Evolutionary stable strategies and game dynamics. Elsevier Mathematical Biosciences 40(1) (1978)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2) (2002)
Chase, J., Anderson, D., Thakar, P., Vahdat, A., Doyle, R.: Managing energy and server resources in hosting centers. In: 18th Symposium on Operating Systems Principles (October 2001)
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Lee, C., Suzuki, J., Vasilakos, A.V. (2012). An Evolutionary Game Theoretic Framework for Adaptive, Cooperative and Stable Network Applications. In: Suzuki, J., Nakano, T. (eds) Bio-Inspired Models of Network, Information, and Computing Systems. BIONETICS 2010. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 87. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32615-8_21
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DOI: https://doi.org/10.1007/978-3-642-32615-8_21
Publisher Name: Springer, Berlin, Heidelberg
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