Scalable Reliable Multicast Transport for the Internet

这是与南加州大学的Deborah Estrin在NCR-9628729奖项下的共同努力。 许多应用需要可靠的多点数据传输。 一些众所周知的例子是网络新闻，电子邮件列表和网络路由协议，如BGP。 然而，到目前为止，由于缺乏对IP组播和可靠组播传输的广泛支持，这些应用一直使用多个TCP连接来实现到多个目的地的可靠数据传输，这导致了网络资源的可扩展性和效率低下。 本研究的目标是开发一个框架和一组机制，以提供高效的，强大的，可扩展的，可靠的组播数据传输在非常大的网络和非常大的组播组。指导设计的基本原则包括应用层成帧（ALF），IP组播组的概念和接收者发起的组管理，以及TCP/IP的核心设计原则-简单的数据报服务，端到端的可靠性方法和自动适应不断变化的操作环境。 本研究将（1）系统地研究所提出的自适应错误恢复方案的可扩展性问题;（2）开发本地化错误恢复流量的机制，并大大减少当前设计中的恢复开销;（3）开发多对多组播数据传输的流量和拥塞算法;以及（4）研究新的会话消息分发模型，以进一步提高可扩展性。

This is a joint effort with Deborah Estrin, University of Southern California under award NCR-9628729. Many applications require reliable multipoint data delivery. Some well known examples are netnews, electronic mailing list, and network routing protocols such as BGP. Up until now, however, due to the lack of widely available support for both IP multicast and reliable multicast transport, these applications have been using multiple TCP connections to achieve reliable data delivery to multiple destinations, which results in both poor scalability and inefficient utilization of network resources. The goal of this research is to develop a framework and a set of mechanisms to provide efficient, robust, scalable, and reliable multicast data delivery both over very large networks and with very large multicast groups. The basic principles that have guided the design include the application level framing (ALF), the IP multicast group concept and receiver-initiated group management, and the core design principles of TCP/IP --- simple datagram service, the end-to-end approach to reliability, and automatic adaptation to changing operational environment. The research will (1) systematically investigate the scaling issues with the proposed adaptive error recovery scheme; (2) develop mechanisms to localize error recovery traffic and substantially reduce the recovery overhead in the current design; (3) develop flow and congestion algorithms for many-to-many multicast data delivery; and (4) investigate new models for session message distribution to further improve the scalability.