An Online Influence Detection Algorithm for Organic Computing Systems
S. Rudolph, S. Tomforde, B. Sick, H. Heck, A. Wacker, and J. Haehner. Architecture of Computing Systems. Proceedings, ARCS 2015-The 28th International Conference on Architecture of Computing Systems – ARCS Workshops, page 1-8. VDE, VDE Verlag, (March 2015)
Abstract
In recent years, technical systems have become substantially more complex due to an increasing number of subsystems, the interactions between them, and the mutual influence on each other. Often, even today's systems are no longer manageable by humans and this trend is assumed to intensify rapidly. Therefore, the Organic Computing initiative develops techniques in order to put these systems on a self-organized and self-adaptive level. In this work, we provide a novel method for the detection of mutual influences in Organic Computing systems through the adaption of stochastic dependency measures. We depict how the quantification and detection of such influences take place and verify the appropriate function through simulations of a typical Organic Computing system.
%0 Conference Paper
%1 rutosickhe2015
%A Rudolph, Stefan
%A Tomforde, Sven
%A Sick, Bernhard
%A Heck, Henner
%A Wacker, Arno
%A Haehner, Joerg
%B Architecture of Computing Systems. Proceedings, ARCS 2015-The 28th International Conference on Architecture of Computing Systems – ARCS Workshops
%D 2015
%I VDE Verlag
%K detection heck itegpub myown organic
%P 1-8
%T An Online Influence Detection Algorithm for Organic Computing Systems
%X In recent years, technical systems have become substantially more complex due to an increasing number of subsystems, the interactions between them, and the mutual influence on each other. Often, even today's systems are no longer manageable by humans and this trend is assumed to intensify rapidly. Therefore, the Organic Computing initiative develops techniques in order to put these systems on a self-organized and self-adaptive level. In this work, we provide a novel method for the detection of mutual influences in Organic Computing systems through the adaption of stochastic dependency measures. We depict how the quantification and detection of such influences take place and verify the appropriate function through simulations of a typical Organic Computing system.
@inproceedings{rutosickhe2015,
abstract = {In recent years, technical systems have become substantially more complex due to an increasing number of subsystems, the interactions between them, and the mutual influence on each other. Often, even today's systems are no longer manageable by humans and this trend is assumed to intensify rapidly. Therefore, the Organic Computing initiative develops techniques in order to put these systems on a self-organized and self-adaptive level. In this work, we provide a novel method for the detection of mutual influences in Organic Computing systems through the adaption of stochastic dependency measures. We depict how the quantification and detection of such influences take place and verify the appropriate function through simulations of a typical Organic Computing system.},
added-at = {2015-10-06T22:56:13.000+0200},
author = {Rudolph, Stefan and Tomforde, Sven and Sick, Bernhard and Heck, Henner and Wacker, Arno and Haehner, Joerg},
biburl = {https://www.bibsonomy.org/bibtex/2ce7ed45ce0d7da670708c5f8a6c033df/wacker},
booktitle = {Architecture of Computing Systems. Proceedings, ARCS 2015-The 28th International Conference on Architecture of Computing Systems – ARCS Workshops},
interhash = {e66e23861c83d30917a66d9104da119b},
intrahash = {ce7ed45ce0d7da670708c5f8a6c033df},
keywords = {detection heck itegpub myown organic},
month = {March},
organization = {VDE},
pages = {1-8},
publisher = {VDE Verlag},
timestamp = {2016-04-14T16:29:14.000+0200},
title = {An Online Influence Detection Algorithm for Organic Computing Systems},
year = 2015
}