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MESDD: A Distributed Geofence-Based Discovery Method for the Computing Continuum

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Euro-Par 2023: Parallel Processing (Euro-Par 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14100))

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Abstract

Service discovery is a vital process that enables low latency provisioning of Internet of Things (IoT) applications across the computing continuum. Unfortunately, it becomes increasingly difficult to identify a proper service within strict time constraints due to the high heterogeneity of the computing continuum. Moreover, the plethora of network technologies and protocols commonly used by IoT applications further hinders service discovery. To address these issues, we introduce a novel Mobile Edge Service Discovery using the DNS (MESDD) algorithm, which uses a so-called Intermediate Discovery Code to identify suitable service instances. MESDD uses geofences for fine-grained service segmentation based on a naming scheme that identifies users’ locations across the computing continuum. We deployed a real-life distributed computing continuum testbed and compared MESDD with three related methods, outperformed by 60 % after eight update iterations.

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Acknowledgement

This work received funding from the European Commission’s Horizon 2020 program (grant 101016835, DataCloud) and Austrian Research Promotion Agency (FFG) (grant 888098, Kärntner Fog).

Author information

Authors and Affiliations

  1. Institute of Information Technology, University Klagenfurt, Klagenfurt, Austria

    Kurt Horvath, Dragi Kimovski, Christoph Uran & Radu Prodan

  2. Faculty of Engineering and IT, Carinthian University of Applied Science, Klagenfurt, Austria

    Christoph Uran & Helmut Wöllik

Authors
  1. Kurt Horvath
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  2. Dragi Kimovski
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  3. Christoph Uran
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  4. Helmut Wöllik
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  5. Radu Prodan
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Corresponding author

Correspondence to Dragi Kimovski .

Editor information

Editors and Affiliations

  1. University of Glasgow, Glasgow, UK

    José Cano

  2. University of Cyprus, Nicosia, Cyprus

    Marios D. Dikaiakos

  3. University of Cyprus, Nicosia, Cyprus

    George A. Papadopoulos

  4. Chalmers University of Technology, Gothenburg, Sweden

    Miquel Pericàs

  5. University of Manchester, Manchester, UK

    Rizos Sakellariou

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Horvath, K., Kimovski, D., Uran, C., Wöllik, H., Prodan, R. (2023). MESDD: A Distributed Geofence-Based Discovery Method for the Computing Continuum. In: Cano, J., Dikaiakos, M.D., Papadopoulos, G.A., Pericàs, M., Sakellariou, R. (eds) Euro-Par 2023: Parallel Processing. Euro-Par 2023. Lecture Notes in Computer Science, vol 14100. Springer, Cham. https://doi.org/10.1007/978-3-031-39698-4_9

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  • DOI: https://doi.org/10.1007/978-3-031-39698-4_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-39697-7

  • Online ISBN: 978-3-031-39698-4

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