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SoFA: A Spark-oriented Fog Architecture
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
University of Padua, Italy .
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2019 (English)In: IEEE 45th Annual Conference of the Industrial Electronics Society IECON'19, 2019Conference paper, Published paper (Refereed)
Abstract [en]

Fog computing offers a wide range of service levels including low bandwidth usage, low response time, support of heterogeneous applications, and high energy efficiency. Therefore, real-time embedded applications could potentially benefit from Fog infrastructure. However, providing high system utilization is an important challenge of Fog computing especially for processing embedded applications. In addition, although Fog computing extends cloud computing by providing more energy efficiency, it still suffers from remarkable energy consumption, which is a limitation for embedded systems. To overcome the above limitations, in this paper, we propose SoFA, a Spark-oriented Fog architecture that leverages Spark functionalities to provide higher system utilization, energy efficiency, and scalability. Compared to the common Fog computing platforms where edge devices are only responsible for processing data received from their IoT nodes, SoFA leverages the remaining processing capacity of all other edge devices. To attain this purpose, SoFA provides a distributed processing paradigm by the help of Spark to utilize the whole processing capacity of all the available edge devices leading to increase energy efficiency and system utilization. In other words, SoFA proposes a near- sensor processing solution in which the edge devices act as the Fog nodes. In addition, SoFA provides scalability by taking advantage of Spark functionalities. According to the experimental results, SoFA is a power-efficient and scalable solution desirable for embedded platforms by providing up to 3.1x energy efficiency for the Word-Count benchmark compared to the common Fog processing platform.

Place, publisher, year, edition, pages
2019.
Keywords [en]
Fog ComputingDistributed ProcessingSparkProgrammingIoTEnergy Efficiency
National Category
Engineering and Technology Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-45053OAI: oai:DiVA.org:mdh-45053DiVA, id: diva2:1345077
Conference
IEEE 45th Annual Conference of the Industrial Electronics Society IECON'19, 14 Oct 2019, Lisbon, Portugal
Projects
Future factories in the CloudDeepMaker: Deep Learning Accelerator on Commercial Programmable DevicesMobiFog: mobility management in Fog-assisted IoT networksHealth5G: Future eHealth powered by 5GFlexiHealth: flexible softwarized networks for digital healthcareAvailable from: 2019-08-22 Created: 2019-08-22 Last updated: 2019-08-22Bibliographically approved

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Loni, MohammadDaneshtalab, MasoudFotouhi, Hossein

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CiteExportLink to record
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Citation style
  • apa
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