By Topic

A calculus for information-driven networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Kui Wu ; Computer Science Department, University of Victoria, BC, Canada V8W 3P6 ; Yuming Jiang ; Guoqiang Hu

Information-driven networks include a large category of networking systems, where network nodes are aware of information delivered and thus can not only forward data packets but may also perform information processing. In many situations, the quality of service (QoS) in information-driven networks is provisioned with the redundancy in information. Traditional performance models generally adopt evaluation measures suitable for packet-oriented service guarantee, such as packet delay, throughput, and packet loss rate. These performance measures, however, do not align well with the actual need of information-driven networks. New performance measures and models for information-driven networks, despite their importance, have been mainly blank, largely because information processing is clearly application dependent and cannot be easily captured within a generic framework. To fill the vacancy, we develop a new performance evaluation framework particularly tailored for information-driven networks, based on the recent development of stochastic network calculus. Particularly, our model captures the information processing and the QoS guarantee with respect to stochastic information delivery rates, which have never been formally modeled before. This analytical model is very useful in deriving theoretical performance bounds for a large body of systems where QoS is stochastically guaranteed with a certain level of information delivery.

Published in:

Quality of Service, 2009. IWQoS. 17th International Workshop on

Date of Conference:

13-15 July 2009