A Selection Model for MSNs of Overlay Network Based on Hybrid Algorithm

After analyzing the characteristics of overlay network, a hybrid K-medoids genetic model (HKGM) is proposed based on the hybrid clustering algorithms, which is used to choose the multicast service nodes (MSNs) from network nodes. Compared with the traditional K-medoids model, HKGM not only avoids converging to local minimum value, but also is robust to initialization. Also, during the evolution, according to actual features of MSNs in overlay network, the evolutional control strategies including diverse gene and evolutionary elite reservation are used to enhance the local search ability of model, and to increase the convergent speed.

[1]  R. Chadha,et al.  Adaptive dynamic server placement in MANETs , 2005, MILCOM 2005 - 2005 IEEE Military Communications Conference.

[2]  Joshua Zhexue Huang,et al.  Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values , 1998, Data Mining and Knowledge Discovery.

[3]  Weiguo Sheng,et al.  A hybrid algorithm for k-medoid clustering of large data sets , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[4]  R. Venkatesha Prasad,et al.  Server allocation algorithms for VOIP conferencing , 2005, First International Conference on Distributed Frameworks for Multimedia Applications.

[5]  Spiridon Bakiras.  Approximate server selection algorithms in content distribution networks , 2005, IEEE International Conference on Communications, 2005. ICC 2005. 2005.

[6]  Zbigniew Michalewicz,et al.  Genetic algorithms + data structures = evolution programs (3rd ed.) , 1996 .

[7]  Bu-Sung Lee,et al.  A survey of application level multicast techniques , 2004, Comput. Commun..

[8]  Paul Francis,et al.  Yoid: Extending the Internet Multicast Architec-ture , 2000 .

[9]  Henri Casanova,et al.  Clustering hosts in P2P and global computing platforms , 2003, CCGrid 2003. 3rd IEEE/ACM International Symposium on Cluster Computing and the Grid, 2003. Proceedings..

[10]  Filip De Turck,et al.  Server placement algorithms for the construction of a QoS enabled gaming infrastructure , 2005, 10th IEEE Symposium on Computers and Communications (ISCC'05).

[11]  Steven H. Low,et al.  A server allocation and placement algorithm for content distribution , 2002, Proceedings of the IEEE Information Theory Workshop.

[12]  Bobby Bhattacharjee,et al.  Scalable application layer multicast , 2002, SIGCOMM '02.

[13]  Christophe Diot,et al.  Deployment issues for the IP multicast service and architecture , 2000, IEEE Netw..

[14]  Xing Xiao.  A Novel K-means Clustering Based on the Immune Programming Algorithm , 2003 .

[15]  Qin Liu,et al.  A novel selection approach for replicated multicast servers using genetic algorithm , 2005, Proceedings. 2005 International Conference on Wireless Communications, Networking and Mobile Computing, 2005..

[16]  Liu Yun.  Performance Evaluation Models for Stress and Stretch of Overlay Network Multicast , 2005 .

[17]  Lixia Zhang,et al.  Host multicast: a framework for delivering multicast to end users , 2002, Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies.

[18]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .