Abstract
Fog computing is introduced to help leverage the processing burden in cloud as it can no longer sustain the ever-growing volume, velocity and variety of the IoT-generated data. Existing studies have shown that fog computing is able to reduce the latency and provide other benefits in the IoT-fog-cloud environment. While fog is heterogeneous since can be any device with computing, networking and storage ability, its scalability must be ensured. Currently, not many studies have been conducted to see the performance of fog in different scalability approaches i.e. scaling up and scaling down. This paper provides a brief explanation on iFogSim, which is a Java-based program that allows modelling and simulation of fog computing environments. The iFogSim is used in this study to simulate the fog environment in scaling up and scaling out approaches running in five configuration settings. In the scaling out approach, it presents a cluster of fogs with similar specifications and the scaling up approach presents a high-end fog with greater capabilities than the fogs in the first approach. Our initial findings delineate that the scaling out approach gives a better result in reducing the cost of execution in cloud. In this paper, we provide an insightful discussion on the strength and weakness in these two approaches. This would open up new avenues of further research.
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Rahman, F.H., Au, T.W., Shah Newaz, S.H., Haji Suhaili, W.S. (2019). A Performance Study of High-End Fog and Fog Cluster in iFogSim. In: Omar, S., Haji Suhaili, W., Phon-Amnuaisuk, S. (eds) Computational Intelligence in Information Systems. CIIS 2018. Advances in Intelligent Systems and Computing, vol 888. Springer, Cham. https://doi.org/10.1007/978-3-030-03302-6_8
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