基于组播的微移动性状态分析与聚合研究
State Analysis and Aggregation Study for Multicast-based Micro Mobility
摘要：
IP移动解决了在移动过程中透明地改变网络连接点的问题。移动IP是IETF提出的标准。然而，一些研究表明，移动IP有几个缺点，如三角路由和切换性能差。基于多播的移动性被认为是解决上述问题的一个很有前途的方案，它具有更小的端到端时延和快速平滑的切换。尽管如此，这种架构遭受多播状态的可扩展性问题与移动节点的数量的增长。这种架构还需要无处不在的组播部署和更复杂的安全措施。为了缓解这些问题，我们提出了一个域内组播为基础的移动性解决方案。移动代理为移动到其域的每个移动设备分配多播地址。移动设备在域中使用此多播地址进行微移动。此外，聚合被认为是减少多播状态。我们进行多播状态分析，研究几个聚合技术的效率。我们使用广泛的模拟来评估我们的协议的性能在各种真实的和生成的拓扑结构。我们将聚合增益作为我们评估的指标。我们的模拟结果表明，在一般情况下，泄漏聚集获得更好的增益比完美的聚集。此外，我们注意到，聚合增益的增加与访问移动节点的数量的增加，并在一个域内的移动代理的数量减少。
英文摘要：
IP mobility addresses the problem of changing the network point-of-attachment transparently during movement. Mobile IP is the proposed standard by IETF. Several studies, however, have shown that Mobile IP has several drawbacks, such as triangle routing and poor handoff performance. Multicast-based mobility has been proposed as a promising solution to the above problems, incurring less end-to-end delays and fast smooth handoff. Nonetheless, such architecture suffers from multicast state scalability problems with the growth in number of mobile nodes. This architecture also requires ubiquitous multicast deployment and more complex security measures. To alleviate these problems, we propose an intra-domain multicast-based mobility solution. A mobility proxy allocates a multicast address for each mobile that moves to its domain. The mobile uses this multicast address within a domain for micro mobility. Also, aggregation is considered to reduce the multicast state. We conduct multicast state analysis to study the efficiency of several aggregation techniques. We use extensive simulation to evaluate our protocol's performance over a variety of real and generated topologies. We take aggregation gain as metric for our evaluation. Our simulation results show that in general leaky aggregation obtains better gains than perfect aggregation. Also, we notice that aggregation gain increases with the increase in number of visiting mobile nodes and with the decrease in number of mobility proxies within a domain.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
