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Towards Supporting IoT System Designers in Edge Computing Deployment Decisions
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
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 [en]
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: urn:nbn:se:mau:diva-37068DOI: 10.24834/isbn.9789178771592ISBN: 978-91-7877-158-5 (print)ISBN: 978-91-7877-159-2 (electronic)OAI: oai:DiVA.org:mau-37068DiVA, id: diva2:1506179
Supervisors
Available from: 2020-12-03 Created: 2020-12-02 Last updated: 2024-02-23Bibliographically approved
List of papers
1. Analyzing Distributed Deep Neural Network Deployment on Edge and Cloud Nodes in IoT Systems
Open this publication in new window or tab >>Analyzing Distributed Deep Neural Network Deployment on Edge and Cloud Nodes in IoT Systems
Show others...
2020 (English)In: IEEE International Conference on Edge Computing (EDGE), Virtual conference, October 18–24, 2020., 2020, p. 59-66Conference paper, Published paper (Refereed)
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.

Keywords
Edge Computing, Internet of Things, Distributed Deep Neural Networks, Simulation, Smart Cities
National Category
Computer Systems Communication Systems
Identifiers
urn:nbn:se:mau:diva-37023 (URN)10.1109/EDGE50951.2020.00017 (DOI)000659316400010 ()2-s2.0-85100251401 (Scopus ID)978-1-7281-8254-4 (ISBN)978-1-7281-8255-1 (ISBN)
Conference
IEEE International Conference on Edge Computing (EDGE) 2020. 19-23 Oct. 2020. Beijing, China
Available from: 2020-11-27 Created: 2020-11-27 Last updated: 2024-02-05Bibliographically approved
2. Edge Computing Simulators for IoT System Design: An Analysis of Qualities and Metrics
Open this publication in new window or tab >>Edge Computing Simulators for IoT System Design: An Analysis of Qualities and Metrics
2019 (English)In: Future Internet, E-ISSN 1999-5903, Vol. 11, no 11, p. 235-246Article in journal (Refereed) Published
Abstract [en]

The deployment of Internet of Things (IoT) applications is complex since many quality characteristics should be taken into account, for example, performance, reliability, and security. In this study, we investigate to what extent the current edge computing simulators support the analysis of qualities that are relevant to IoT architects who are designing an IoT system. We first identify the quality characteristics and metrics that can be evaluated through simulation. Then, we study the available simulators in order to assess which of the identified qualities they support. The results show that while several simulation tools for edge computing have been proposed, they focus on a few qualities, such as time behavior and resource utilization. Most of the identified qualities are not considered and we suggest future directions for further investigation to provide appropriate support for IoT architects.

Place, publisher, year, edition, pages
MDPI, 2019
Keywords
Internet of Things, edge computing, simulation tools, quality characteristics, metrics, ISO/IEC 25023
National Category
Computer Systems Communication Systems Embedded Systems
Identifiers
urn:nbn:se:mau:diva-37014 (URN)10.3390/fi11110235 (DOI)000502277600015 ()2-s2.0-85075344380 (Scopus ID)
Available from: 2020-11-27 Created: 2020-11-27 Last updated: 2024-02-05Bibliographically approved
3. Cloud, Edge, or Both? Towards Decision Support for Designing IoT Applications
Open this publication in new window or tab >>Cloud, Edge, or Both? Towards Decision Support for Designing IoT Applications
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.

National Category
Engineering and Technology
Identifiers
urn:nbn:se:mau:diva-16827 (URN)10.1109/IoTSMS.2018.8554827 (DOI)000455671800023 ()2-s2.0-85059973173 (Scopus ID)26740 (Local ID)978-1-5386-9585-2 (ISBN)26740 (Archive number)26740 (OAI)
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
4. Quality attributes in edge computing for the Internet of Things: A systematic mapping study
Open this publication in new window or tab >>Quality attributes in edge computing for the Internet of Things: A systematic mapping study
2021 (English)In: Internet of Things: Engineering Cyber Physical Human Systems, E-ISSN 2542-6605, Vol. 13, article id 100346Article in journal (Refereed) Published
Abstract [en]

Many Internet of Things (IoT) systems generate a massive amount of data needing to be processed and stored efficiently. Cloud computing solutions are often used to handle these tasks. However, the increasing availability of computational resources close to the edge has prompted the idea of using these for distributed computing and storage. Edge computing may help to improve IoT systems regarding important quality attributes like latency, energy consumption, privacy, and bandwidth utilization. However, deciding where to deploy the various application components is not a straightforward task. This is largely due to the trade-offs between the quality attributes relevant for the application. We have performed a systematic mapping study of 98 articles to investigate which quality attributes have been used in the literature for assessing IoT systems using edge computing. The analysis shows that time behavior and resource utilization are the most frequently used quality attributes; further, response time, turnaround time, and energy consumption are the most used metrics for quantifying these quality attributes. Moreover, simulation is the main tool used for the assessments, and the studied trade-offs are mainly between only two qualities. Finally, we identified a number of research gaps that need further study.

Place, publisher, year, edition, pages
Elsevier, 2021
Keywords
Internet of Things, Edge computing, Quality attributes, Metrics, Systematic mapping study
National Category
Computer Systems Communication Systems Embedded Systems
Identifiers
urn:nbn:se:mau:diva-39120 (URN)10.1016/j.iot.2020.100346 (DOI)000695695700015 ()2-s2.0-85106740791 (Scopus ID)
Available from: 2021-01-13 Created: 2021-01-13 Last updated: 2024-02-05Bibliographically approved

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Ashouri, Majid

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