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A micro-level compensation-based cost model for resource allocation in a fog environment


Battula, SK and Garg, S and Naha, RK and Thulasiraman, P and Thulasiram, R, A micro-level compensation-based cost model for resource allocation in a fog environment, Sensors, 19, (13) Article 2954. ISSN 1424-8220 (2019) [Refereed Article]


Copyright Statement

Copyright 2019 The Authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0)

DOI: doi:10.3390/s19132954


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.

Item Details

Item Type:Refereed Article
Keywords:cost model, fog computing, IoT, matching theory, resource allocation
Research Division:Information and Computing Sciences
Research Group:Distributed computing and systems software
Research Field:Distributed systems and algorithms
Objective Division:Information and Communication Services
Objective Group:Communication technologies, systems and services
Objective Field:Communication technologies, systems and services not elsewhere classified
UTAS Author:Battula, SK (Mr Sudheer Kumar Battula)
UTAS Author:Garg, S (Dr Saurabh Garg)
UTAS Author:Naha, RK (Mr Ranesh Kumar Naha)
ID Code:135315
Year Published:2019
Web of Science® Times Cited:15
Deposited By:Information and Communication Technology
Deposited On:2019-10-12
Last Modified:2020-06-12
Downloads:16 View Download Statistics

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