%0 Journal Article %1 amoretti2012measuring %A Amoretti, Michele %A Gershenson, Carlos %D 2012 %K Evolutionary complexity %T Measuring the Complexity of Ultra-Large-Scale Evolutionary Systems %U http://arxiv.org/abs/1207.6656 %X Ultra-large scale (ULS) systems are becoming pervasive. They are inherently complex, which makes their design and control a challenge for traditional methods. Here we propose the design and analysis of ULS systems using measures of complexity, emergence, self-organization, and homeostasis based on information theory. We evaluate the proposal with a ULS computing system provided with genetic adaptation mechanisms. We show the evolution of the system with stable and also changing workload, using different fitness functions. When the adaptive plan forces the system to converge to a predefined performance level, the nodes may result in highly unstable configurations, that correspond to a high variance in time of the measured complexity. Conversely, if the adaptive plan is less äggressive", the system may be more stable, but the optimal performance may not be achieved.

@article{amoretti2012measuring, abstract = {Ultra-large scale (ULS) systems are becoming pervasive. They are inherently complex, which makes their design and control a challenge for traditional methods. Here we propose the design and analysis of ULS systems using measures of complexity, emergence, self-organization, and homeostasis based on information theory. We evaluate the proposal with a ULS computing system provided with genetic adaptation mechanisms. We show the evolution of the system with stable and also changing workload, using different fitness functions. When the adaptive plan forces the system to converge to a predefined performance level, the nodes may result in highly unstable configurations, that correspond to a high variance in time of the measured complexity. Conversely, if the adaptive plan is less "aggressive", the system may be more stable, but the optimal performance may not be achieved.}, added-at = {2012-08-27T16:44:08.000+0200}, author = {Amoretti, Michele and Gershenson, Carlos}, biburl = {https://www.bibsonomy.org/bibtex/202f8da2f3e0a51ed8a043c14d34fff14/kmukhar}, description = {Measuring the Complexity of Ultra-Large-Scale Evolutionary Systems}, interhash = {b7f928f2eb9fb8b523a1425dd9b2ea80}, intrahash = {02f8da2f3e0a51ed8a043c14d34fff14}, keywords = {Evolutionary complexity}, note = {cite arxiv:1207.6656Comment: 9 pages, 10 figures, submitted to INFOCOM2013}, timestamp = {2012-08-27T16:44:08.000+0200}, title = {Measuring the Complexity of Ultra-Large-Scale Evolutionary Systems}, url = {http://arxiv.org/abs/1207.6656}, year = 2012 }