Inference of Multicast Routing Trees and Bottleneck Bandwidths using End-to-end Measurements

Abstract

The efficacy of end-to-end multicast transport protocols depends critically upon their ability to scale efficiently to a large number of receivers. Several research multicast protocols attempt to achieve this high scalability by identifying sets of co-located receivers in order to enhance loss recovery, congestion control and so forth. A number of these schemes could be enhanced and simplified by some level of explicit knowledge of the topology of the multicast distribution tree, the value of the bottleneck bandwidth along the path between the source and each individual receiver and the approximate location of the bottlenecks in the tree. In this paper, we explore the problem of inferring the internal structure of a multicast distribution tree using only observations made at the end hosts. By noting correlations of loss patterns across the receiver set and by measuring how the network perturbs the fine-grained timing structure of the packets sent from the source, we can determine both the underlying multicast tree structure as well as the bottleneck bandwidths. Our simulations show that the algorithm is robust and appears to converge to the correct tree with high probability.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1998
Accession Number
ADA637131

Entities

People

  • Steven Mccanne
  • Sylvia Ratnasamy

Organizations

  • University of California, Berkeley

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Bandwidth
  • Computer Science
  • Failure Mode And Effect Analysis
  • Frequency
  • Mathematical Analysis
  • Measurement
  • Models
  • Network Protocols
  • Packet Loss
  • Probabilistic Models
  • Probability
  • Recovery
  • Simulations
  • Topology
  • Transport Protocols
  • Trees (Data Structures)

Fields of Study

  • Computer science

Readers

  • Applied Combinatorial Optimization and Logic Circuit Design.
  • Neural Network Machine Learning.
  • Radio communications and signal processing.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms