Publication no #570
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|Uncertainty Reduction in Self-Adaptive Systems|
Gabriel A. Moreno,
David Garlan and Mark Klein.
In Proc. of the 13th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), Gothenburg, Sweden, 28-29 May 2018.
Online links: Plain Text
|Self-adaptive systems depend on models of themselves and their
environment to decide whether and how to adapt, but these models
are often affected by uncertainty. While current adaptation decision
approaches are able to model and reason about this uncertainty,
they do not consider ways to reduce it. This presents an opportunity
for improving decision-making in self-adaptive systems, because
reducing uncertainty results in a better characterization of the current
and future states of the system and the environment (at some
cost), which in turn supports making better adaptation decisions.
We propose uncertainty reduction as the natural next step in uncertainty
management in the field of self-adaptive systems. This
requires both an approach to decide when to reduce uncertainty,
and a catalog of tactics to reduce different kinds of uncertainty. We
present an example of such a decision, examples of uncertainty
reduction tactics, and describe how uncertainty reduction requires
changes to the different activities in the typical self-adaptation loop.|
Keywords: Self-adaptation, uncertainty.
| Created: 2018-03-21 12:47:32 Modified: 2018-06-06 15:37:28|