Title :
A Dimensionless Graceful Degradation Metric for Quantifying Resilience
Author_Institution :
Raytheon BBN Technol., Cambridge, MA, USA
Abstract :
Many self-* properties are variations on the same theme: resilience of a system to changes in itself or the conditions under which it operates. Quantifying resilience is difficult, however: there are no metrics of resilience that are readily comparable across systems, and the space of possible changes is typically prohibitively large. To address this problem, I propose a quantitative measure of graceful degradation that is independent of the units, scales, and number of system parameters. Although this metric is typically intractable to compute precisely, it can be approximated by perturbation surveys, and the quality of approximation is likely to be improved by a random perturbation approach based on recent advances in manifold learning.
Keywords :
learning (artificial intelligence); random processes; self-adjusting systems; dimensionless graceful degradation metric; engineered self-organization;; manifold learning; perturbation survey; quantitative measure; random perturbation approach; resilience quantification; self-adaptability; system parameter; system resilience; engineered self-organization; graceful degradation; perturbation analysis; sel; self-adaptability;
Conference_Titel :
Self-Adaptive and Self-Organizing Systems Workshops (SASOW), 2012 IEEE Sixth International Conference on
Conference_Location :
Lyon
Print_ISBN :
978-1-4673-5153-9
DOI :
10.1109/SASOW.2012.24
