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Abstract

With the increasing number of Internet of Things (IoT) devices, the volume and variety of data being generated by these devices are increasing rapidly. Cloud computing cannot process this data due to its high latency and scalability. In order to process this data in less time, fog computing has evolved as an extension to Cloud computing. In a fog computing environment, a resource monitoring service plays a vital role in providing advanced services such as scheduling, scaling and migration. Most of the research in fog computing has assumed that a resource monitoring service is already available. Conventional methods proposed for other distributed systems may not be suitable due to the unique features of a fog environment. To improve the overall performance of fog computing and to optimise resource usage, effective resource monitoring techniques are required. Hence, we propose a Support and Confidence based (SCB) technique which optimises the resource usage in the resource monitoring service. The performance of our proposed system is evaluated by examining a real-time traffic use case in a fog emulator with synthetic data. The experimental results obtained from the fog emulator show that the proposed technique consumes 19% lesser resources compared with the existing technique.

Item Type:	Article
Authors/Creators:	Battula, S and Garg, S and Montgomery, J and Kang, B
Keywords:	fog computing, IoT, big data, resource monitoring, Internet of Things
Journal or Publication Title:	IEEE Transactions on Services Computing
Publisher:	Institute of Electrical and Electronics Engineers
ISSN:	1939-1374
DOI / ID Number:	10.1109/TSC.2019.2962682
Copyright Information:	Copyright 2019 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.
Related URLs:	Google Scholar: Garg, S UTAS Profile: Garg, S Google Scholar: Montgomery, J UTAS Profile: Montgomery, J Google Scholar: Kang, B UTAS Profile: Kang, B
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