Reinforcement Learning for Call Admission Control and Routing under Quality of Service Constraints in Multimedia Networks

Authors:
Hui Tong;Timothy X. Brown
Affiliations:
Electrical and Computer Engineering, University of Colorado, Boulder, CO 80309-0425, USA. tongh@colorado.edu;Electrical and Computer Engineering, University of Colorado, Boulder, CO 80309-0425, USA. timxb@colorado.edu
Venue:
Machine Learning
Year:
2002

Citing 16
Cited 4

Data networks (2nd ed.)

Data networks (2nd ed.)
Technical Note: \cal Q-Learning

Machine Learning
On the self-similar nature of Ethernet traffic

SIGCOMM '93 Conference proceedings on Communications architectures, protocols and applications
The asymptotic convergence-rate of Q-learning

NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Optimizing admission control while ensuring quality of service in multimedia networks via reinforcement learning

Proceedings of the 1998 conference on Advances in neural information processing systems II
ATM Network Resource Management

ATM Network Resource Management
Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
Neuro-Dynamic Programming

Neuro-Dynamic Programming
Multi-criteria Reinforcement Learning

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A Neuro-Dynamic Programming Approach to Admission Control in ATM Networks: The Single Link Case

ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 1 - Volume 1
QoS based routing algorithm in integrated services packet networks

ICNP '97 Proceedings of the 1997 International Conference on Network Protocols (ICNP '97)
Adaptive admission control and routing under quality of service constraints in broadband communications

Adaptive admission control and routing under quality of service constraints in broadband communications
Robust dynamic admission control for unified cell and call QoS in statistical multiplexers

IEEE Journal on Selected Areas in Communications
Call admission control and routing in integrated services networks using neuro-dynamic programming

IEEE Journal on Selected Areas in Communications
Adaptive call admission control under quality of service constraints: a reinforcement learning solution

IEEE Journal on Selected Areas in Communications
Routing subject to quality of service constraints in integrated communication networks

IEEE Network: The Magazine of Global Internetworking

Cooperative information sharing to improve distributed learning in multi-agent systems

Journal of Artificial Intelligence Research
Coordinated learning in multiagent MDPs with infinite state-space

Autonomous Agents and Multi-Agent Systems
Application of Bayesian Networks for Autonomic Network Management

Journal of Network and Systems Management
A survey of multi-objective sequential decision-making

Journal of Artificial Intelligence Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we solve the call admission control and routing problem in multimedia networks via reinforcement learning (RL). The problem requires that network revenue be maximized while simultaneously meeting quality of service constraints that forbid entry into certain states and use of certain actions. The problem can be formulated as a constrained semi-Markov decision process. We show that RL provides a solution to this problem and is able to earn significantly higher revenues than alternative heuristics.