Treffer: Multicast grouping for data distribution management

Title:
Multicast grouping for data distribution management
Contributors:
Modeling, Virtual Environments, and Simulation Institute (MOVES)
Source:
Simulation Practice and Theory. 9:121-141
Publisher Information:
Elsevier BV, 2002.
Publication Year:
2002
Document Type:
Fachzeitschrift Article<br />Conference object
File Description:
application/xml; application/pdf
Language:
English
ISSN:
0928-4869
DOI:
10.1016/s0928-4869(01)00054-4
Rights:
Elsevier TDM
Accession Number:
edsair.doi.dedup.....e232d2fd734e336167d4118a9ba8488c
Database:
OpenAIRE

Weitere Informationen

Summary: 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 [\(3\)Com 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.