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2023 | OriginalPaper | Chapter

Analysis of MAPE-K Loop in Self-adaptive Systems for Cloud, IoT and CPS

Authors : Jiyoung Oh, Claudia Raibulet, Joran Leest

Published in: Service-Oriented Computing – ICSOC 2022 Workshops

Publisher: Springer Nature Switzerland

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Abstract

Self-adaptive approaches aim to address the complexity of modern computing generated by the runtime variabilities and uncertainties. In this context, MAPE-K loop is considered today a major approach for the design and implementation of self-adaptive solutions because it captures in a systematic way the main steps of the adaptation process: (1) Monitor the execution context, (2) Analyze the monitored context, (3) Plan the appropriate adaptation strategy, (4) Execute the adaptation strategy, all these steps using a common Knowledge about the context. Implementations of MAPE-K loops may be particularly complex, domain specific, as well as case study dependent. In this paper, we provide a preliminary analysis of MAPE-K loops in various artifacts in different application domains (i.e., cloud - Hogna and TMA, cyber-physical systems - TRAPP and AMELIA, Internet of Things - DeltaIoT). Our main objective is to outline the similarities and differences among the available implementations of MAPE-K control feedback loops in self-adaptive systems. Additionally, the application domains of the considered examples are highly related, so that solutions in one domain may trigger developments in others. We also provide an insight into MAPE-K loops to enable researchers and practitioners to use, re-use, improve the available solutions.

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previous chapter EXOGEM: Extending OpenAPI Generator for Monitoring of RESTful APIs

next chapter SASH: Safe Autonomous Self-Healing

SEAMS artifacts - https://www.hpi.uni-potsdam.de/giese/public/selfadapt/exemplars/.

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Title: Analysis of MAPE-K Loop in Self-adaptive Systems for Cloud, IoT and CPS
Authors: Jiyoung Oh
Claudia Raibulet
Joran Leest
Publisher: Springer Nature Switzerland
Book: Service-Oriented Computing – ICSOC 2022 Workshops
Print ISBN: 978-3-031-26506-8

Electronic ISBN: 978-3-031-26507-5

Copyright Year: 2023
DOI: https://doi.org/10.1007/978-3-031-26507-5_11

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