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Auto-scaling techniques for IoT-based cloud applications: a review

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Abstract

Cloud and IoT applications have inquiring effects that can strongly influence today’s ever-growing internet life along with necessity to resolve numerous challenges for each application such as scalability, security, privacy, and reliability. During the deployment of IoT-based Cloud applications, the demand for Cloud tenants is dynamic that makes challenging to maintain scalability of the system. Developing an effective scaling technique is not merely a big concern, but how to achieve autonomic scaling results using future load prediction and migration policies is also a crucial phase. Also, to evaluate such auto-scaling strategy, certain Quality of Service (QoS) metrics must be recognized, explored and leveraged to enhance the performance of the system. Therefore, in this paper, a survey of existing auto-scaling, load prediction and VM migration techniques for IoT-based Cloud applications has been carried out along with the evaluation of various QoS parameters. Further, the future trends have also been discussed for performing auto-scaling in a Cloud environment.

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    Shveta Verma & Anju Bala

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Verma, S., Bala, A. Auto-scaling techniques for IoT-based cloud applications: a review. Cluster Comput 24, 2425–2459 (2021). https://doi.org/10.1007/s10586-021-03265-9

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