A case for tree evolution in QoS multicasting

  • Authors:

  • Anirban Chakrabarti;G. Manimaran

  • Affiliations:

  • Dependable Computing and Networking Laboratory, Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA;Dependable Computing and Networking Laboratory, Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA

  • Venue:
  • Computer Communications
  • Year:
  • 2006

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Abstract

The continuous growth of group communications and QoS-aware applications over the Internet have accelerated the development of multicasting technologies. The Core-Based Tree (CBT) multicasting approach provides a scalable solution for large groups and for large networks, such as the Internet. However, unlike in shortest-path trees, the quality (tree cost) of the CBT may eventually degrade over time due to group dynamics (join/leave). In order to counteract this degradation, the core may be migrated and a new tree constructed. The method of migrating group members from the old core to the new core has a profound impact on the quality of the tree and also on the service disruption experienced by group members. Thus, there exists a trade-off between tree cost and service disruption as higher rate of migration decreases the overall tree cost but results in more service disruption. In this paper, we develop a new paradigm for tree migration, namely tree evolution. The proposed tree evolution model (Split-based Tree Evolution Protocol) provides an elegant solution that strikes a balance between service disruption and tree cost for highly dynamic groups. We propose two forms of evolution, viz. QoS-based and timer-based. We provide an analysis to estimate the evolution timer which determines the number of cores present in the group, and also compare and contrast the merits of tree evolution versus tree migration through extensive simulation studies. We also provide a soft state implementation of the evolution protocol which is an extension of the CBT soft state approach. Our simulation studies show that the proposed evolution model demonstrates excellent tree cost and service disruption for highly dynamic groups.