QoS Routing under Adversarial Binary Uncertainty

Abstract

A cost-based admission control and routing scheme admits an arriving request on the minimum cost route if this cost does not exceed the cost of the request, and rejects the request otherwise. Cost-based strategies naturally arise as a result of optimization of the network performance or incorporating Quality of Service (QoS ) requirements into the admission and routing processes. In the former case, the implied cost of the resources represents expected future revenue losses due to insufficient resources to service future requests. In the latter case, the cost of a route represents the expected level of QoS (e.g., bandwidth, delay, packet loss, etc.) provided to the request carried on this route. In both cases, due to aggregation, the statistical nature of the resource costs, propagation and queueing delays in disseminating signaling information, or a nonsteady or adversarial operational environment the cost of the resources may not be known exactly. Usually, this uncertainty is modeled by assuming that resource costs are random variables with fixed probability distributions, which may or may not be known to the network. This paper explores a different approach intended to guard against adversarial uncertainty (i.e., worst case scenario) with respect to the resource costs lying within known "confidence" intervals. We assume that the network minimizes and the adversarial environment maximizes the loss or risk resulting from nonoptimal admission and routing decisions due to the uncertainty. In a symmetric case, we explicitly identify the optimal network strategy by solving the corresponding game of the network against environment.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2002
Accession Number
ADA512220

Entities

People

  • Vladimir Marbukh

Organizations

  • National Institute of Standards and Technology

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Bayesian Networks
  • Computational Science
  • Environment
  • Equations
  • Information Operations
  • Models
  • Network Topology
  • Networks
  • Optimization
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Random Variables
  • Rejection
  • Standards
  • Statistical Inference
  • Uncertainty

Fields of Study

  • Computer science

Readers

  • Distributed Systems and Data Platform Development
  • Mathematical Modeling and Probability Theory.
  • Radio communications and signal processing.