A fuzzy reinforcement learning approach for pre-congestion notification based admission control

Authors:
Stylianos Georgoulas;Klaus Moessner;Alexis Mansour;Menelaos Pissarides;Panagiotis Spapis
Affiliations:
Centre for Communication Systems Research, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, Surrey, United Kingdom;Centre for Communication Systems Research, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, Surrey, United Kingdom;Centre for Communication Systems Research, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, Surrey, United Kingdom;Centre for Communication Systems Research, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, Surrey, United Kingdom;Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece
Venue:
AIMS'12 Proceedings of the 6th IFIP WG 6.6 international autonomous infrastructure, management, and security conference on Dependable Networks and Services
Year:
2012

Citing 8
Cited 0

Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
PCN Based Admission Control for Autonomic Video Quality Differentiation: Design and Evaluation

Journal of Network and Systems Management
Self-organizing networks in next generation radio access networks: Application to fractional power control

Computer Networks: The International Journal of Computer and Telecommunications Networking
A fuzzy reinforcement learning approach for self‐optimization of coverage in LTE networks

Bell Labs Technical Journal
Edge-based differentiated services

IWQoS'05 Proceedings of the 13th international conference on Quality of Service
ADMISSION CONTROL IN MULTI-SERVICE IP NETWORKS: A TUTORIAL

IEEE Communications Surveys & Tutorials
A Survey of PCN-Based Admission Control and Flow Termination

IEEE Communications Surveys & Tutorials
Admission Control in Multiservice IP Networks: Architectural Issues and Trends

IEEE Communications Magazine

Quantified Score

Hi-index	0.00

Visualization

Abstract

Admission control aims to compensate for the inability of slow-changing network configurations to react rapidly enough to load fluctuations. Even though many admission control approaches exist, most of them suffer from the fact that they are based on some very rigid assumptions about the per-flow and aggregate underlying traffic models, requiring manual reconfiguration of their parameters in a "trial and error" fashion when these original assumptions stop being valid. In this paper we present a fuzzy reinforcement learning admission control approach based on the increasingly popular Pre-Congestion Notification framework that requires no a priori knowledge about traffic flow characteristics, traffic models and flow dynamics. By means of simulations we show that the scheme can perform well under a variety of traffic and load conditions and adapt its behavior accordingly without requiring any overly complicated operations and with no need for manual and frequent reconfigurations.