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Architecturing Elastic Edge Storage Services for Data-Driven Decision Making

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Software Architecture (ECSA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11681))

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Abstract

In the IoT era, a massive number of smart sensors produce a variety of data at unprecedented scale. Edge storage has limited capacities posing a crucial challenge for maintaining only the most relevant IoT data for edge analytics. Currently, this problem is addressed mostly considering traditional cloud-based database perspectives, including storage optimization and resource elasticity, while separately investigating data analytics approaches and system operations. For better support of future edge analytics, in this work, we propose a novel, holistic approach for architecturing elastic edge storage services, featuring three aspects, namely, (i) data/system characterization (e.g., metrics, key properties), (ii) system operations (e.g., filtering, sampling), and (iii) data processing utilities (e.g., recovery, prediction). In this regard, we present seven engineering principles for the architecture design of edge data services.

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Notes

  1. 1.

    https://prometheus.io/.

  2. 2.

    https://www.fluentd.org/.

  3. 3.

    https://fluentbit.io/.

  4. 4.

    https://fogger.io/.

  5. 5.

    https://www.mainflux.com/.

  6. 6.

    https://docs.docker.com.

  7. 7.

    https://www.ansible.com/.

  8. 8.

    https://www.terraform.io/.

  9. 9.

    https://www.vapor.io/kinetic-edge/.

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Acknowledgments

The work in this paper has been partially funded through Rucon project (Runtime Control in Multi Clouds), FWF Y 904 START-Programm 2015 and Ivan Lujic’s netidee scholarship by the Internet Foundation Austria.

Author information

Authors and Affiliations

  1. Institute of Information Systems Engineering, TU Wien, Vienna, Austria

    Ivan Lujic

  2. Department of Computer Science, Aalto University, Espoo, Finland

    Hong-Linh Truong

Authors
  1. Ivan Lujic
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  2. Hong-Linh Truong
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Corresponding author

Correspondence to Ivan Lujic .

Editor information

Editors and Affiliations

  1. Charles University, Prague, Czech Republic

    Tomas Bures

  2. University of Lille, Villeneuve d’Ascq, France

    Laurence Duchien

  3. University of L’Aquila, L’Aquila, Italy

    Paola Inverardi

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Lujic, I., Truong, HL. (2019). Architecturing Elastic Edge Storage Services for Data-Driven Decision Making. In: Bures, T., Duchien, L., Inverardi, P. (eds) Software Architecture. ECSA 2019. Lecture Notes in Computer Science(), vol 11681. Springer, Cham. https://doi.org/10.1007/978-3-030-29983-5_7

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  • DOI: https://doi.org/10.1007/978-3-030-29983-5_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29982-8

  • Online ISBN: 978-3-030-29983-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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