Fog Orchestration for Internet of Things Services

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...

Full description

Saved in:
Bibliographic Details
Published in:IEEE internet computing Vol. 21; no. 2; pp. 16 - 24
Main Authors: Zhenyu Wen, Renyu Yang, Garraghan, Peter, Tao Lin, Jie Xu, Rovatsos, Michael
Format: Journal Article
Language:English
Published: IEEE 01.03.2017
Subjects:
Distributed computing
distributed systems
Edge computing
fog computing
fog orchestration
Internet of things
Internet/Web technologies
IoT
Large-scale systems
ISSN:1089-7801
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1089-7801
DOI:10.1109/MIC.2017.36