Abstract
Self-adaptive systems have the ability to adapt their behavior to dynamic operation conditions. In reaction to changes in the environment, these systems determine the appropriate corrective actions based in part on information about which action will have the best impact on the system. Existing models used to describe the impact of adaptations are either unable to capture the underlying uncertainty and variability of such dynamic environments, or are not compositional and described at a level of abstraction too low to scale in terms of specification effort required for non-trivial systems. In this paper, we address these shortcomings by describing an approach to the specification of impact models based on architectural system descriptions, which at the same time allows us to represent both variability and uncertainty in the outcome of adaptations, hence improving the selection of the best corrective action. The core of our approach is an impact model language equipped with a formal semantics defined in terms of Discrete Time Markov Chains. To validate our approach, we show how employing our language can improve the accuracy of predictions used for decision-making in the Rainbow framework for architecture-based self-adaptation.
This work is supported in part by award N000141310401 from the Office of Naval Research, by the Foundation for Science and Technology via project CMU-PT/ELE/0030/2009, and is based upon work funded and supported by the Department of Defense under Contract No. FA8721-05-C-0003 with Carnegie Mellon University for the operation of the Software Engineering Institute, a federally funded research and development center. This material has been approved for public release and unlimited distribution. (DM-0001079).
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Notes
- 1.
Acme is in turn derived from OCL [16], with the addition of functions that relate to architectural structure.
- 2.
For utility calculation, we assume a representation of system state in terms of qualities. In Znn, we take the average of response time in all clients and the sum of the costs of active servers.
- 3.
The impact of individual adaptation actions is specified in terms of constant impact vectors (called cost/benefit attribute vectors) which describe how the execution of adaptation actions affects system quality attributes [5].
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Moreno, J.C., Lopes, A., Garlan, D., Schmerl, B. (2015). Impact Models for Architecture-Based Self-adaptive Systems. In: Lanese, I., Madelaine, E. (eds) Formal Aspects of Component Software. FACS 2014. Lecture Notes in Computer Science(), vol 8997. Springer, Cham. https://doi.org/10.1007/978-3-319-15317-9_6
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