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Cloud, Edge, or Both? Towards Decision Support for Designing IoT Applications
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0003-0998-6585
Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0003-0326-0556
2018 (English)In: 2018 Fifth International Conference on Internet of Things: Systems, Management and Security, 2018Conference paper, Published paper (Other academic)
Abstract [en]

The rapidly evolving Internet of Things (IoT) includes applications which might generate a huge amount of data, this requires appropriate platforms and support methods. Cloud computing offers attractive computational and storage solutions to cope with these issues. However, sending to centralized servers all the data generated at the edge of the network causes latency, energy consumption, and high bandwidth demand. Performing some computations at the edge of the network, known as Edge computing, and using a hybrid Edge-Cloud architecture can help addressing these challenges. While such architecture may provide new opportunities to distribute IoT applications, making optimal decisions regarding where to deploy the different application components is not an easy and straightforward task for designers. Supporting designers’ decisions by considering key quality attributes impacting them in an Edge-Cloud architecture has not been investigated yet. In this paper, we: explore the importance of decision support for the designers, discuss how different attributes impact the decisions, and describe the required steps toward a decision support framework for IoT application designers.

Place, publisher, year, edition, pages
2018.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mau:diva-16827DOI: 10.1109/IoTSMS.2018.8554827ISI: 000455671800023Scopus ID: 2-s2.0-85059973173Local ID: 26740ISBN: 978-1-5386-9585-2 (electronic)OAI: oai:DiVA.org:mau-16827DiVA, id: diva2:1420341
Conference
The Fifth International Conference on Internet of Things: Systems, Management and Security (IoTSMS 2018), Valencia, Spain (15-18 Oct 2018)
Available from: 2020-03-30 Created: 2020-03-30 Last updated: 2023-12-28Bibliographically approved
In thesis
1. Towards Supporting IoT System Designers in Edge Computing Deployment Decisions
Open this publication in new window or tab >>Towards Supporting IoT System Designers in Edge Computing Deployment Decisions
2021 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The rapidly evolving Internet of Things (IoT) systems demands addressing new requirements. This particularly needs efficient deployment of IoT systems to meet the quality requirements such as latency, energy consumption, privacy, and bandwidth utilization. The increasing availability of computational resources close to the edge has prompted the idea of using these for distributed computing and storage, known as edge computing. Edge computing may help and complement cloud computing to facilitate deployment of IoT systems and improve their quality. However, deciding where to deploy the various application components is not a straightforward task, and IoT system designer should be supported for the decision.

To support the designers, in this thesis we focused on the system qualities, and aimed for three main contributions. First, by reviewing the literature, we identified the relevant and most used qualities and metrics. Moreover, to analyse how computer simulation can be used as a supporting tool, we investigated the edge computing simulators, and in particular the metrics they provide for modeling and analyzing IoT systems in edge computing. Finally, we introduced a method to represent how multiple qualities can be considered in the decision. In particular, we considered distributing Deep Neural Network layers as a use case and raked the deployment options by measuring the relevant metrics via simulation.

Place, publisher, year, edition, pages
Malmö: Malmö universitet, 2021. p. 141
Series
Studies in Computer Science ; 13
Keywords
Internet of Things, Edge computing, Decision Support, Quality Attrib-utes, Metrics, Simulation
National Category
Communication Systems Other Electrical Engineering, Electronic Engineering, Information Engineering Computer Systems
Identifiers
urn:nbn:se:mau:diva-37068 (URN)10.24834/isbn.9789178771592 (DOI)978-91-7877-158-5 (ISBN)978-91-7877-159-2 (ISBN)
Supervisors
Note

Note: The papers are not included in the fulltext online

Available from: 2020-12-03 Created: 2020-12-02 Last updated: 2024-03-07Bibliographically approved

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fulltext(329 kB)358 downloads
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Ashouri, MajidDavidsson, PaulSpalazzese, Romina

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