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Towards Care Systems Using Model-Driven Adaptation and Monitoring of Autonomous Multi-clouds

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Advances in Conceptual Modeling (ER 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10651))

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Abstract

In cloud computing, the ability to run and manage multi-cloud systems allows exploiting the peculiarities of each cloud solution and hence optimising the performance, availability, and cost of the applications. In this paper, we investigate the use case of a robotic care system as an application of autonomous multi-clouds. We present requirements and properties of an Abstract State Machines-based conceptual model that coordinates the multi-cloud interaction through the specification of a middleware exploiting adaptive interfaces to multiple clouds and supporting various service formats. While the multi-cloud system is running in normal mode, data about the execution will be gathered and evaluated by the monitoring component, and in case any critical situation is discovered the adaptation component is alerted. We show that for the care system this can be fruitfully exploited for failure alerts, failure anticipation and prevention, and safety hazards detection.

The research reported in this paper has been supported by the Christian-Doppler Society in the frame of the Christian-Doppler Laboratory for Client-Centric Cloud Computing.

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Notes

  1. 1.

    Our model is based on the commercially available GrowMu (see http://www.growmeup.eu/index.php/home/growmu-robot) robot, but extends it in various ways.

  2. 2.

    This is merely a selection of services that can be extended.

  3. 3.

    A more detailed specification and results of the verification can be found at http://cdcc.faw.jku.at/staff/abuga/esocc.zip.

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Correspondence to Andreea Buga .

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Buga, A., Nemeş, S.T., Schewe, KD. (2017). Towards Care Systems Using Model-Driven Adaptation and Monitoring of Autonomous Multi-clouds. In: de Cesare, S., Frank, U. (eds) Advances in Conceptual Modeling. ER 2017. Lecture Notes in Computer Science(), vol 10651. Springer, Cham. https://doi.org/10.1007/978-3-319-70625-2_3

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  • DOI: https://doi.org/10.1007/978-3-319-70625-2_3

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