Modeling Control Channel Dynamics of the SAAM Architecture Using the NS Network Simulation Tool.

Abstract

The explosive growth of the Internet and the advent of real-time network applications have stretched the capacity of current network technology. It has become evident that to realize the full potential of the Information Super Highway a new network architecture would have to be developed. It was for these reasons the Next Generation Internet Project was started. As a part of this effort the Server and Agent based Active network Management (SAAM) Project was started. SAAM is a server based hierarchical routing architecture designed to provide Quality of Service (QoS) routing services for network resource intensive applications. Because the study of this topic entailed emulating large Wide Area Networks, a simulation of the entire architecture would have to be developed. This thesis provides the first step towards achieving that goal. The model developed as the basis for this thesis concentrates on the control traffic overhead required to configure and implement the routing mechanism of SAAM. Specifically it simulates the control channel dynamics required to pass control messages between servers, routers and real-time applications.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1999
Accession Number
ADA371825

Entities

People

  • Brian E. Tiefert

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Counter WMD
  • Cyber

DTIC Thesaurus Topics

  • California
  • Computer Networks
  • Computer Programming
  • Computer Programs
  • Computer Science
  • Computers
  • Computing System Architectures
  • Electrical Engineering
  • Information Science
  • Network Architecture
  • Network Protocols
  • Network Simulation
  • Operating Systems
  • Packet Loss
  • Routing Protocols
  • Simulations
  • Transport Protocols

Fields of Study

  • Computer science

Readers

  • Database Systems and Applications
  • Distributed Systems and Data Platform Development
  • Neural Network Machine Learning.