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An Application Layer Multicast Approach Based On Topology-aware Clustering

机译:基于拓扑感知聚类的应用层组播方法

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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.
机译:应用层多播加速了多播通信的普遍部署,并带来了不可避免的性能损失,因为组成员是动态的并且缺乏对基础拓扑的直接了解。一种改善多播性能的有前途的方法是将附近的节点分组。但是,它面临一些实际挑战。一个挑战是,很难分配一个接近范围来确定是否应该对某些节点进行集群。另一个实际问题是如何组织相应的多播结构。在本文中,我们提出了一种新的拓扑感知层次聚类模型,该模型可实现不同粒度的聚类。基于该模型,我们提出了一种名为HCcast的应用程序层多播解决方案,特别是针对大型团体应用程序。 HCcast使用拓扑感知方法在不同级别选择候选父级,并使用分布式深度优先搜索(DFS)方法将主机定位在同一级别。 HCcast的群集基于拓扑,因此不需要群集拆分和合并操作,从而减少了维护开销。我们的仿真实验结果表明,HCcast可以构建具有理想传递性能的多播树,并且在不同的加入序列中性能保持稳定。

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