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Open AccessArticle

A Micro-Level Compensation-Based Cost Model for Resource Allocation in a Fog Environment

Discipline of ICT, School of Technology, Environment and Design (TED), University of Tasmania, Hobart, TAS 7005, Australia
Department of Computer Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
Authors to whom correspondence should be addressed.
Sensors 2019, 19(13), 2954;
Received: 28 May 2019 / Revised: 26 June 2019 / Accepted: 2 July 2019 / Published: 4 July 2019
(This article belongs to the Special Issue Recent Advances in Fog/Edge Computing in Internet of Things)
Fog computing aims to support applications requiring low latency and high scalability by using resources at the edge level. In general, fog computing comprises several autonomous mobile or static devices that share their idle resources to run different services. The providers of these devices also need to be compensated based on their device usage. In any fog-based resource-allocation problem, both cost and performance need to be considered for generating an efficient resource-allocation plan. Estimating the cost of using fog devices prior to the resource allocation helps to minimize the cost and maximize the performance of the system. In the fog computing domain, recent research works have proposed various resource-allocation algorithms without considering the compensation to resource providers and the cost estimation of the fog resources. Moreover, the existing cost models in similar paradigms such as in the cloud are not suitable for fog environments as the scaling of different autonomous resources with heterogeneity and variety of offerings is much more complicated. To fill this gap, this study first proposes a micro-level compensation cost model and then proposes a new resource-allocation method based on the cost model, which benefits both providers and users. Experimental results show that the proposed algorithm ensures better resource-allocation performance and lowers application processing costs when compared to the existing best-fit algorithm. View Full-Text
Keywords: cost model; fog computing; IoT; matching theory; resource allocation cost model; fog computing; IoT; matching theory; resource allocation
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Battula, S.K.; Garg, S.; Naha, R.K.; Thulasiraman, P.; Thulasiram, R. A Micro-Level Compensation-Based Cost Model for Resource Allocation in a Fog Environment. Sensors 2019, 19, 2954.

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