Multicast Communication with Guaranteed Quality of Service

Abstract

In this thesis, we address the problem of constructing multicast data distribution trees with guaranteed quality of service (QoS) for supporting multiparty interactions. We present an approach that integrates reservation with tree construction to facilitate a guaranteed quality of service. The proposed approach is based on the use of information about participants registered before the interaction starts. We first identify the design goals for multicast tree construction with minimum QoS requirements. We then describe a protocol to locate a set of distribution centers for an interaction that depends upon the current load distribution, locations of the participants, and their QoS requirements. The protocol sets up a suitable number of center-specific trees for the interaction transparently. We compare the quality of the resulting trees on large, hypothetical networks with that of sender-specific and Steiner trees. Our results show that center-specific trees, built around the centers located by our approach, reserve fewer resources than sender-specific trees even for a significant number of simultaneous senders while sacrificing minimally in the average delay faced by each receiver.

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Document Details

Document Type
Technical Report
Publication Date
Dec 16, 1993
Accession Number
ADA277650

Entities

People

  • Eric B. Boyer

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • C4I
  • Ground and Sea Platforms
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Abstracts
  • California
  • Command And Control
  • Communication Systems
  • Computer Networks
  • Computer Science
  • Engineering
  • Instructors
  • Load Distribution
  • Local Area Networks
  • Network Protocols
  • Network Science
  • Network Topology
  • Networks
  • Routing Protocols
  • Simulators
  • United States Naval Academy

Fields of Study

  • Computer science

Readers

  • Computer Networking
  • Graph Algorithms and Convex Optimization.