Publikationer från Malmö universitet
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Analyzing Distributed Deep Neural Network Deployment on Edge and Cloud Nodes in IoT Systems
Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).
Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).ORCID-id: 0000-0002-8209-0921
Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).ORCID-id: 0000-0003-0998-6585
Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP).ORCID-id: 0000-0003-0326-0556
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2020 (engelsk)Inngår i: IEEE International Conference on Edge Computing (EDGE), Virtual conference, October 18–24, 2020., 2020, s. 59-66Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

For the efficient execution of Deep Neural Networks (DNN) in the Internet of Things, computation tasks can be distributed and deployed on edge nodes. In contrast to deploying all computation to the cloud, the use of Distributed DNN (DDNN) often results in a reduced amount of data that is sent through the network and thus might increase the overall performance of the system. However, finding an appropriate deployment scenario is often a complex task and requires considering several criteria. In this paper, we introduce a multi-criteria decision-making method based on the Analytical Hierarchy Process for the comparison and selection of deployment alternatives. We use the RECAP simulation framework to model and simulate DDNN deployments on different scales to provide a comprehensive assessment of deployments to system designers. In a case study, we apply the method to a smart city scenario where different distributions and deployments of a DNN are analyzed and compared.

sted, utgiver, år, opplag, sider
2020. s. 59-66
Emneord [en]
Edge Computing, Internet of Things, Distributed Deep Neural Networks, Simulation, Smart Cities
HSV kategori
Identifikatorer
URN: urn:nbn:se:mau:diva-37023DOI: 10.1109/EDGE50951.2020.00017ISI: 000659316400010Scopus ID: 2-s2.0-85100251401ISBN: 978-1-7281-8254-4 (digital)ISBN: 978-1-7281-8255-1 (tryckt)OAI: oai:DiVA.org:mau-37023DiVA, id: diva2:1504498
Konferanse
IEEE International Conference on Edge Computing (EDGE) 2020. 19-23 Oct. 2020. Beijing, China
Tilgjengelig fra: 2020-11-27 Laget: 2020-11-27 Sist oppdatert: 2024-06-17bibliografisk kontrollert
Inngår i avhandling
1. Towards Supporting IoT System Designers in Edge Computing Deployment Decisions
Åpne denne publikasjonen i ny fane eller vindu >>Towards Supporting IoT System Designers in Edge Computing Deployment Decisions
2021 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Malmö: Malmö universitet, 2021. s. 141
Serie
Studies in Computer Science ; 13
Emneord
Internet of Things, Edge computing, Decision Support, Quality Attrib-utes, Metrics, Simulation
HSV kategori
Identifikatorer
urn:nbn:se:mau:diva-37068 (URN)10.24834/isbn.9789178771592 (DOI)978-91-7877-158-5 (ISBN)978-91-7877-159-2 (ISBN)
Veileder
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Note: The papers are not included in the fulltext online

Tilgjengelig fra: 2020-12-03 Laget: 2020-12-02 Sist oppdatert: 2024-03-07bibliografisk kontrollert

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Totalt: 178 treff
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