Wen, Z, Yang, R, Garraghan, P et al. (3 more authors) (2017) Fog Orchestration for Internet of Things Services. IEEE Internet Computing, 21 (2). pp. 16-24. ISSN 1089-7801
Abstract
Large-scale Internet of Things (IoT) services such as healthcare, smart cities, and marine monitoring are pervasive in cyber-physical environments strongly supported by Internet technologies and fog computing. Complex IoT services are increasingly composed of sensors, devices, and compute resources within fog computing infrastructures. The orchestration of such applications can be leveraged to alleviate the difficulties of maintenance and enhance data security and system reliability. However, efficiently dealing with dynamic variations and transient operational behavior is a crucial challenge within the context of choreographing complex services. Furthermore, with the rapid increase of the scale of IoT deployments, the heterogeneity, dynamicity, and uncertainty within fog environments and increased computational complexity further aggravate this challenge. This article gives an overview of the core issues, challenges, and future research directions in fog-enabled orchestration for IoT services. Additionally, it presents early experiences of an orchestration scenario, demonstrating the feasibility and initial results of using a distributed genetic algorithm in this context.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Internet/Web technologies, Internet of Things, IoT, fog computing, fog orchestration, distributed systems |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 22 Oct 2020 13:42 |
Last Modified: | 12 Nov 2020 04:51 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/MIC.2017.36 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:166992 |