Scheduling Real-Time IoT Workflows in a Fog Computing Environment Utilizing Cloud Resources with Data-Aware Elasticity

In a fog computing environment the supplementary public cloud resources should be managed as effectively as possible, utilizing a dynamic scaling mechanism, in order to provide monetary cost savings and resilience against workload fluctuations. Furthermore, the dynamic scaling strategy should take i...

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Bibliographic Details
Published in:2021 Sixth International Conference on Fog and Mobile Edge Computing (FMEC) pp. 1 - 8
Main Authors: Stavrinides, Georgios L., Karatza, Helen D.
Format: Conference Proceeding
Language:English
Published: IEEE 06.12.2021
Subjects:
autoscaling
Cloud computing
data-aware elasticity
dynamic scaling
Dynamic scheduling
Elasticity
Fluctuations
fog computing
Multi-access edge computing
Processor scheduling
Simulation
workflow scheduling
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Summary:In a fog computing environment the supplementary public cloud resources should be managed as effectively as possible, utilizing a dynamic scaling mechanism, in order to provide monetary cost savings and resilience against workload fluctuations. Furthermore, the dynamic scaling strategy should take into account the data dependencies of the workload, in order to prevent data loss. Towards this direction, in this paper we propose a reactive data-aware dynamic scaling mechanism for the provision of cloud resources, along with a heuristic for the scheduling of real-time Internet of Things (IoT) workflows, in a three-tier IoT-fog-cloud architecture. The performance of the proposed scheme is evaluated and compared via simulation to a static provisioning case, under different patterns of incoming workload. The simulation results provide useful insights into how each workload pattern affects the performance of the framework under study, in each of the provisioning cases of the cloud resources.
DOI:10.1109/FMEC54266.2021.9732561