用于数据分发管理的组播分组

Katherine L Morse , Michael Zyda
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引用次数: 28

摘要

高级架构(HLA)的数据分布管理(DDM)服务是一系列系统中最新的,旨在减少大规模分布式模拟中单个模拟接收的数据量。这些兴趣管理系统中常见的优化是使用多播组将数据发送到所有潜在接收者的选定子集。在这个应用中,多播的使用已经取得了相当大的成功。然而,迄今为止,它的使用依赖于模拟之间通信模式的先验知识和多播组对这些模式的静态分配。随着更大、更复杂、更不可预测的模拟的建立,更有效地使用多播组的需求已经出现,因为它们是一种有限的资源[3Com公司,用CoreBuilder 3500 Layer 3交换机扩展性能和管理增长(可在http://www.3com.com/products/dsheets/400347a.html获得)列出了6k的限制,这是合成战区(STOW) (D. Van Hook, RITN IM和IM历史,个人通信,1996年1月)的硬件限制大约为1000。一个典型的工作站网络接口卡只有几个(H. Abrams,可扩展的兴趣管理用于可扩展的持久分布式虚拟环境,博士论文,海军研究生院,1999年12月)]。本文提出了两种分组算法,并通过将这些算法应用于选定的数据集,改进了消息传递时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multicast grouping for data distribution management

The High Level Architecture's (HLA) Data Distribution Management (DDM) services are the most recent in a succession of systems designed to reduce the amount of data received by individual simulations in large-scale distributed simulations. A common optimization in these interest management systems is the use of multicast groups for sending data to a selected subset of all potential receivers. The use of multicast has met with considerable success in this application. However, its use to date has relied on a priori knowledge of communication patterns between simulations and static assignment of multicast groups to these patterns. As larger, more complex, and less predictable simulations are built, the need has arisen for more efficient use of multicast groups as they are a restricted resource [3Com Corporation, Scaling Performance and Managing Growth with the CoreBuilder 3500 Layer 3 Switch (available at http://www.3com.com/products/dsheets/400347a.html) lists a limit of 6 K, the highest number identified while Synthetic Theater of War (STOW) (D. Van Hook, RITN IM and IM history, personal communication, January 1996) had a hardware limit of approximately 1000. A typical workstation network interface card has only a few (H. Abrams, Extensible interest management for scalable persistent distributed virtual environments, Ph.D. Dissertation, Naval Postgraduate School, December 1999)]. This paper presents two algorithms for performing grouping, and the message delivery time improvements resulting from applying the algorithms to selected data sets.

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