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To Adapt or Not to Adapt: A Quantification Technique for Measuring an Expected Degree of Self-Adaptation

Intelligent Systems, Christian-Albrechts-Universität zu Kiel, 24118 Kiel, Germany
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This paper is an extended version of our paper published in the Workshop on Self-Aware Computing (SeAC), held in conjunction with Foundations and Applications of Self* Systems (FAS* 2019).
Computers 2020, 9(1), 21; https://doi.org/10.3390/computers9010021
Received: 14 February 2020 / Revised: 13 March 2020 / Accepted: 15 March 2020 / Published: 18 March 2020
(This article belongs to the Special Issue Applications in Self-Aware Computing Systems and their Evaluation)
Self-adaptation and self-organization (SASO) have been introduced to the management of technical systems as an attempt to improve robustness and administrability. In particular, both mechanisms adapt the system’s structure and behavior in response to dynamics of the environment and internal or external disturbances. By now, adaptivity has been considered to be fully desirable. This position paper argues that too much adaptation conflicts with goals such as stability and user acceptance. Consequently, a kind of situation-dependent degree of adaptation is desired, which defines the amount and severity of tolerated adaptations in certain situations. As a first step into this direction, this position paper presents a quantification approach for measuring the current adaptation behavior based on generative, probabilistic models. The behavior of this method is analyzed in terms of three application scenarios: urban traffic control, the swidden farming model, and data communication protocols. Furthermore, we define a research roadmap in terms of six challenges for an overall measurement framework for SASO systems. View Full-Text
Keywords: self-adaptation; quantification; system analysis; organic computing; metric; degree of adaptation self-adaptation; quantification; system analysis; organic computing; metric; degree of adaptation
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Tomforde, S.; Goller, M. To Adapt or Not to Adapt: A Quantification Technique for Measuring an Expected Degree of Self-Adaptation. Computers 2020, 9, 21.

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