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Performance Evaluation
Volume 57, Issue 2, June 2004, Pages 105-119
 
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doi:10.1016/j.peva.2003.10.002    
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Copyright © 2003 Published by Elsevier Science B.V.

Using adaptive routing to achieve Quality of Service*1

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Pu SuCorresponding Author Contact Information, E-mail The Corresponding Author and Michael GellmanE-mail The Corresponding Author

School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, USA


Received 15 April 2003; 
Revised 27 September 2003. 
Available online 19 December 2003.

Abstract

Self-monitoring allows a network to observe its own behavior via probing and measurement mechanisms. This can then be exploited by the system to take autonomous decisions for the purpose of system management, performance management and user Quality of Service (QoS). In this paper, we experimentally explore how QoS goals that are externally set by network users, can then be explicitly exploited by a self-aware network to control its own behavior to attain these goals. The experiments we report are conducted in two distinct Cognitive Packet Network (CPN) test-beds which use probing to select the routes which best satisfy the QoS goal. Our experiments validate this concept with QoS goals which include end-to-end delay, packet loss, and a mixture of these two metrics. We observe that using only delay in the QoS goal is a good way to reduce delay and loss if losses result only from congestion. However, using loss in the QoS goal is seen to be useful if the paths that are adaptively selected avoid nodes where packet losses occur for reasons other than congestion. In general we observe that CPN networks effectively adapt routing behavior to the QoS goal that is specified.

Author Keywords: Quality of Service; Routing; Self-monitoring; Loss; Delay; Cognitive packet networks

Article Outline

1. Introduction
2. CPN routing
2.1. Reinforcement learning and routing
3. Constructing composite QoS goals
3.1. Estimating link loss and path loss
3.2. Simplifying the cumulative loss
4. Measurement results
5. Future work
Acknowledgements
References
Vitae








Corresponding Author Contact InformationCorresponding author.

*1 This work was supported by US Army Stricom via NAWC under Contract N61339-02-C0117 and by the National Science Foundation under Grant EIA0203446.


Performance Evaluation
Volume 57, Issue 2, June 2004, Pages 105-119
 
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