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Digital Twins for Autonomic Cloud Application Management

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Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 451)

Abstract

Cloud applications are distributed in nature, and it is challenging to orchestrate an application across different Cloud providers and for the different capabilities along the Cloud continuum, from the centralized data centers to the edge of the network. Furthermore, optimal dynamic reconfiguration of an application often takes more time than available at runtime. The approach presented in this paper uses a concurrent simulation model of the application that is continuously updated with real-time monitoring data, optimizing, and validating deployment reconfiguration decisions prior to enacting them for the running applications. This enables proactive decisions to be taken for a future time point, thereby allowing ample time for the reconfiguration actions, as well as realistic Bayesian estimation of the application’s time variate operational parameters for the optimization process.

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  • DOI: 10.1007/978-3-030-99619-2_14
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Notes

  1. 1.

    https://abs-models.org/manual/.

  2. 2.

    https://html.spec.whatwg.org/.

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Acknowledgements

This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 871643 MORPHEMIC (http://morphemic.cloud) Modelling and Orchestrating heterogeneous Resources and Polymorphic applications for Holistic Execution and adaptation of Models In the Cloud.

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Correspondence to Geir Horn .

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Horn, G., Schlatte, R., Johnsen, E.B. (2022). Digital Twins for Autonomic Cloud Application Management. In: Barolli, L., Hussain, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2022. Lecture Notes in Networks and Systems, vol 451. Springer, Cham. https://doi.org/10.1007/978-3-030-99619-2_14

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