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A taxonomy of architectural patterns for self-adaptive systems

Published: 10 July 2013 Publication History

Abstract

Autonomic systems are able to adapt themselves to unpredicted and unexpected situations. Such adaptation capabilities can reside in individual components as well as in ensembles of components. In particular, a variety of different architectural patterns can be conceived to support self-adaptation at the level both of components and of ensembles. In this paper, we propose a classification of such self-adaptation patterns -- for both the component level and the system level -- by means of a taxonomy organized around the locus in which the feedback loops promoting adaptation reside. We show that the proposed classification covers most self-adaptation patterns, and enables deriving further ones by applying a simple set of composition mechanisms. Three examples of existing patterns of the taxonomy are detailed in the paper to show the applicability of the approach. As discussed in the paper, the advantage of the proposed classification is twofold: it enables identifying the (possibly common) properties of the existing self-adaptation patterns; and, consequently, it can help developers in choosing the most appropriate self-adaptation patterns for the development of autonomic systems.

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    cover image ACM Other conferences
    C3S2E '13: Proceedings of the International C* Conference on Computer Science and Software Engineering
    July 2013
    155 pages
    ISBN:9781450319768
    DOI:10.1145/2494444
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • Concordia University: Concordia University
    • ISEP-IPP: Polytechnic Institute of Porto / ISEP

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 10 July 2013

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    Author Tags

    1. adaptativity
    2. pattern
    3. taxonomy

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    • Concordia University
    • ISEP-IPP

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    C3S2E '13 Paper Acceptance Rate 12 of 42 submissions, 29%;
    Overall Acceptance Rate 12 of 42 submissions, 29%

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