A Hierarchical Loss Network Model for Performance Evaluation

Abstract

In this paper we present a hierarchical loss network model for estimating the end-to-end blocking probabilities for large networks. As networks grow in size, nodes tend to form clusters geographically and hierarchical routing schemes are more commonly used. Loss network and reduced load models are often used to approximate end-to-end call blocking probabilities and hence throughput. However so far all work being done in this area is for flat networks with at routing schemes. We aim at developing a more efficient approximation method for networks that have a natural hierarchy and/or when some form of hierarchical routing policy is used. We present two hierarchical models in detail for mixed hierarchical routing and dynamic hierarchical routing policies, respectively, via the notion of network abstraction, route segmentation, traffic segregation and aggregation. Computation is done separately within each cluster (local) and among clusters (global), and the mixed point is obtained by iteration between local and global computations. We also present numerical results for the first case.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2000
Accession Number
ADA441030

Entities

People

  • John Baras
  • Mingyan Liu

Organizations

  • University of Maryland

Tags

Communities of Interest

  • Human Systems
  • Space

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Artificial Satellites
  • Bandwidth
  • Communication Networks
  • Department Of Defense
  • Group Processes (Social Psychology)
  • Hierarchies
  • Iterations
  • Load Distribution
  • Network Protocols
  • Networks
  • Peer Groups
  • Probability
  • Steady State
  • Test And Evaluation
  • Universities
  • Websites

Fields of Study

  • Computer science

Readers

  • Calculus or Mathematical Analysis
  • Computer Networking