Stochastic Estimation and Control of Queues Within a Computer Network

Abstract

Captain Nathan C. Stuckey implemented the idea of the stochastic estimation and control for network in OPNET simulator. He used extended Kalman filter to estimate packet size and packet arrival rate of network queue to regulate queue size. To validate stochastic theory, network estimator and controller is designed by OPNET model. These models validated the transient queue behavior in OPNET and work of Kalman filter by predicting the queue size and arrival rate. However, it was not enough to verify a theory by experiment. So, it needed to validate the stochastic control theory with other tools to get high validity. Our goal was to make a new model to validate Stuckey's simulation. For this validation, NS-2 was studied and modified the Kalman filter to cooperate with MATLAB. Moreover, NS-2 model was designed to predict network characteristics of queue size with different scenarios and traffic types. Through these NS-2 models, the performance of the network state estimator and network queue controller was investigated and shown to provide high validity for Stuckey's simulations.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2009
Accession Number
ADA497702

Entities

People

  • Mingook Kim

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Biomedical
  • Energy and Power Technologies
  • Human Systems
  • Materials and Manufacturing Processes
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Computer Networks
  • Computers
  • Control Systems
  • Control Theory
  • Estimators
  • Filters
  • Kalman Filters
  • Mathematical Filters
  • Mobile Phones
  • Monte Carlo Method
  • Network Protocols
  • Simulations
  • Simulators
  • Statistical Algorithms
  • Stochastic Control

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Distributed Systems and Data Platform Development
  • Mathematical Modeling and Probability Theory.