Causal Scheduling of Multiclass Traffic With Deadlines and Priorities.

Abstract

This thesis analyzes causal scheduling and scheduling-dropping policies in a discrete time model. Packets from different priority classes arrive with arbitrary deadlines. The packets must be scheduled before their deadlines. We present three sets of results. The first set is divided into two parts. We first characterize all causal scheduling policies which maximize the throughput whatever the sequence of packets arriving to be scheduled. We then extend the analysis to causal scheduling-dropping policies, that is, scheduling policies which can drop packets before there expiration without scheduling them. We characterize all such scheduling policies that maximize the throughput and those that do so while minimizing the buffer occupancy. The second set of results considers a two class model. We characterize all causal scheduling and scheduling-dropping policies that maximize the high-priority class throughput subject to maximizing the overall throughput For the scheduling dropping policies, we characterize those policies that minimize the buffer occupancy. We finally analyze the more general multi-class case, characterize all causal scheduling policies that optimize two novel optimality criteria, and present two simple scheduling-dropping policies that optimize those criteria.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1998
Accession Number
ADA364544

Entities

People

  • Pierre Seri

Organizations

  • University of Illinois Urbana–Champaign

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Bandwidth
  • Channel Models
  • Code Division Multiple Access
  • Computer Science
  • Data Science
  • Equations
  • Gaussian Noise
  • Gaussian Processes
  • Information Science
  • Instructions
  • Intervals
  • Reliability
  • Sequences
  • Statistics
  • Throughput
  • Waveforms

Fields of Study

  • Computer science

Readers

  • Computer Networking
  • Mathematical Modeling and Probability Theory.