Measuring the Complexity of Ultra-Large-Scale Adaptive Systems
This work addresses the complexity problem for designers and controllers of ultra-large-scale systems, offering a novel evaluation framework, though it appears incremental in applying existing information theory concepts to a specific domain.
The paper tackles the challenge of designing and controlling ultra-large-scale (ULS) adaptive systems by proposing information-theoretic measures of complexity, emergence, self-organization, and homeostasis to evaluate and guide their design. It demonstrates that aggressive adaptation plans can lead to unstable configurations with high complexity variance, while less aggressive plans may sacrifice optimal performance for stability.
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. These measures allow the evaluation of ULS systems and thus can be used to guide their design. We evaluate the proposal with a ULS computing system provided with 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.