Team for Research in
Ubiquitous Secure Technology

Citation
Joe Hoffert, Daniel Mack, Douglas Schmidt. "Using Machine Learning to Maintain Pub/Sub System QoS in Dynamic Environments". 8th Workshop on Adaptive and Reflective Middleware, December, 2009.

Abstract
Quality-of-service (QoS)-enabled publish/subscribe (pub/sub) middleware provides powerful support for scalable data dissemination. It is hard, however, to maintain specified QoS properties (such as reliability and latency) in dynamic environments (such as disaster relief operations or power grids). For example, managing QoS manually is often not feasible in dynamic systems due to (1) slow human response times, (2) the complexity of managing multiple interrelated QoS settings, and (3) the scale of the systems being managed. For certain applications the systems must be able to reflect on the conditions of their environment and adapt accordingly. Machine learning techniques provide a promising adaptation approach to maintaining QoS properties of QoS-enabled pub/sub middleware in dynamic environments. These techniques include decision trees, neural networks, and linear logistic regression classifiers that can be trained on existing data to interpolate and extrapolate for new data. By training the machine learning techniques with system performance metrics in a wide variety of configurations, changes to middleware mechanisms (e.g., associations of publishers and subscribers to transport protocols) can be driven by machine learning to maintain specified QoS. This paper describes how we are applying machine learning techniques to simplify the configuration of QoS-enabled middleware and adaptive transport protocols to maintain specified QoS as systems change dynamically. The results of our work thus far show that decision trees and neural networks can effectively classify the best protocols to use. In particular, decision trees answer questions about which measurements and variables are most important when considering the reliability and latency of pub/sub systems.

Electronic downloads

• ADAMANT-ARM09.pdf · application/pdf · 124 kbytes

• HTML

  Joe Hoffert, Daniel Mack, Douglas Schmidt. <a
  href="http://www.truststc.org/pubs/679.html"
  >Using Machine Learning to Maintain Pub/Sub System QoS in
  Dynamic Environments</a>, 8th Workshop on Adaptive and
  Reflective Middleware, December, 2009.

• Plain text

  Joe Hoffert, Daniel Mack, Douglas Schmidt. "Using
  Machine Learning to Maintain Pub/Sub System QoS in Dynamic
  Environments". 8th Workshop on Adaptive and Reflective
  Middleware, December, 2009.

• BibTeX

  @inproceedings{HoffertMackSchmidt09_UsingMachineLearningToMaintainPubSubSystemQoSInDynamic,
      author = {Joe Hoffert and Daniel Mack and Douglas Schmidt},
      title = {Using Machine Learning to Maintain Pub/Sub System
                QoS in Dynamic Environments},
      booktitle = {8th Workshop on Adaptive and Reflective Middleware},
      month = {December},
      year = {2009},
      abstract = {Quality-of-service (QoS)-enabled publish/subscribe
                (pub/sub) middleware provides powerful support for
                scalable data dissemination. It is hard, however,
                to maintain specified QoS properties (such as
                reliability and latency) in dynamic environments
                (such as disaster relief operations or power
                grids). For example, managing QoS manually is
                often not feasible in dynamic systems due to (1)
                slow human response times, (2) the complexity of
                managing multiple interrelated QoS settings, and
                (3) the scale of the systems being managed. For
                certain applications the systems must be able to
                reflect on the conditions of their environment and
                adapt accordingly. Machine learning techniques
                provide a promising adaptation approach to
                maintaining QoS properties of QoS-enabled pub/sub
                middleware in dynamic environments. These
                techniques include decision trees, neural
                networks, and linear logistic regression
                classifiers that can be trained on existing data
                to interpolate and extrapolate for new data. By
                training the machine learning techniques with
                system performance metrics in a wide variety of
                configurations, changes to middleware mechanisms
                (e.g., associations of publishers and subscribers
                to transport protocols) can be driven by machine
                learning to maintain specified QoS. This paper
                describes how we are applying machine learning
                techniques to simplify the configuration of
                QoS-enabled middleware and adaptive transport
                protocols to maintain specified QoS as systems
                change dynamically. The results of our work thus
                far show that decision trees and neural networks
                can effectively classify the best protocols to
                use. In particular, decision trees answer
                questions about which measurements and variables
                are most important when considering the
                reliability and latency of pub/sub systems.},
      URL = {http://www.truststc.org/pubs/679.html}
  }
  

Posted by Joe Hoffert on 29 Mar 2010.
Groups: trust
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