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Cluster Computing

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15-06-2021

A cost-efficient auto-scaling mechanism for IoT applications in fog computing environment: a deep learning-based approach

Authors: Masoumeh Etemadi, Mostafa Ghobaei-Arani, Ali Shahidinejad

Published in: Cluster Computing | Issue 4/2021

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Abstract

The fog computing model has emerged as a viable infrastructure for serving IoT applications in recent years. In the fog ecosystem, it is essential to manage resources for different workloads due to the high volume and rapid growth of requests. Therefore, a challenge faced in this area is dynamic and efficient resource auto-scaling because fog resources must be allocated to requests efficiently. More fog resources than needed leads to “Over-Provisioning”, and fewer fog resources leads to the “Under-provisioning” issue. To this end, an effective deep learning-based resource auto-scaling mechanism has been proposed to manage the number of resources needed to handle dynamic workloads in a fog environment. The simulation results indicated that the proposed solution reduces cost, network usage, and delay violation and increases CPU utilization compared with existing resource auto-scaling mechanisms.

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Title: A cost-efficient auto-scaling mechanism for IoT applications in fog computing environment: a deep learning-based approach
Authors: Masoumeh Etemadi
Mostafa Ghobaei-Arani
Ali Shahidinejad
Publication date: 15-06-2021
Publisher: Springer US
Published in: Cluster Computing / Issue 4/2021
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-021-03307-2

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