Ultra-large scale (ULS) systems are becoming pervasive. They are inherently complex, which makes their design and control a challenge for traditional methods. Amoretti and Gershenson propose the design and analysis of ULS systems using measures of complexity, emergence, self-organization, and homeostasis based on information theory. They evaluate the proposal with a ULS computing system provided with genetic adaptation mechanisms. Researchers 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.
Michele Amoretti, Carlos Gershenson
Measuring the Complexity of Ultra-Large-Scale Evolutionary Systems
Neural and Evolutionary Computing. Submitted on 27 Jul 2012