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Resource provisioning for containerized applications

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Abstract

Elasticity is an important feature of cloud computing, which allocates/de-allocates adequate computing resources automatically and provisions and de-provisions computing resources timely when the workload fluctuates. It can help in better resource utilization. Elasticity can address the challenges of limited physical resources such as cost, computing power, storage capacity, etc. In cloud computing, the cloud providers achieve elasticity through virtualization techniques such as virtual machines (VMs) or containers. Elasticity can help to scale-up/down the various computing resources of a running system when the workload increases or decreases over the machine. In this paper, we propose a hybrid model that uses a container-based virtualization techniques allocated for various computing resources of a running system (container). It uses a multi-criteria decision making procedure for the allocation of resources. This model uses a reactive approach for scaling up and scaling down the containers’ resources as per the requirements of the application. The proposed approach provides better resource utilization and minimizes subscription costs from both the provider and consumer perspectives. The experimental results show that the hybrid model dynamically allocates resources to applications instantly and maintains higher resource utilization than horizontal elasticity, vertical elasticity, and Docker.

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Acknowledgements

This research was supported/partially supported by [Visvesvaraya PhD Scheme for Electronics and IT, which is governed by MeitY (Ministry of Electronics and Information Technology), Government of India]. We thank our colleagues from MNNIT Allahabad, Prayagraj, India who provided insight and expertise that greatly assisted the research, although they may not agree with all of the interpretations/conclusions of this paper.

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Correspondence to Mahendra Pratap Yadav.

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Yadav, M.P., Pal, N. & Yadav, D.K. Resource provisioning for containerized applications. Cluster Comput 24, 2819–2840 (2021). https://doi.org/10.1007/s10586-021-03293-5

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