Name
Abstract | ||
|---|---|---|
Application layer multicast accelerates ubiquitous deployment of the multicast communication, as well as brings unavoidable performance penalties because the group members are dynamic and lack direct knowledge about the underlying topology. A promising approach to improve the multicasting performance is to cluster the nearby nodes in groups. However, it confronts some practical challenges. A challenge is that it is difficult to assign a proximity bound to determine whether some nodes should be clustered or not. Another practical problem is how to organize the corresponding multicast structure. In this paper, we propose a new topology-aware hierarchical clustering model, which implements clustering in different grain sizes. Based on the model, we propose an application layer multicast solution named HCcast, especially for large-scale group applications. HCcast employs a topology-aware approach to choose candidate parents at different levels, and uses distributed depth first searching (DFS) approach to position a host at the same level. The clusters of HCcast are topology-based, therefore cluster split and merge operations are unnecessary, which reduces the maintenance overhead. The results of our simulation experiments show that HCcast can build multicast trees with desirable delivery performance, and the performance keep stable in different join sequences. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.comcom.2008.12.041 | Computer Communications |
Keywords | Field | DocType |
Application layer multicast,Cluster,Topology-awareness | Protocol Independent Multicast,Source-specific multicast,Computer science,Xcast,Computer network,Real-time computing,Distance Vector Multicast Routing Protocol,Distributed computing,Inter-domain,Topology,Pragmatic General Multicast,Reliable multicast,Multicast | Journal |
Volume | Issue | ISSN |
32 | 6 | Computer Communications |
Citations | PageRank | References |
5 | 0.40 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinchang Zhang | 1 | 10 | 3.00 |
| Xiaodong Li | 2 | 55 | 3.89 |
| Wanming Luo | 3 | 40 | 6.46 |
| BaoPing Yan | 4 | 118 | 22.81 |