Efficient adaptive resource provisioning for cloud applications using reinforcement learning

John, I and Sreekantan, A and Bhatnagar, S (2019) Efficient adaptive resource provisioning for cloud applications using reinforcement learning. In: 4th IEEE International Workshops on Foundations and Applications of Self* Systems, FAS*W 2019, 16 June 2019 - 20 June 2019, Umea, pp. 271-272.

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Abstract

An appealing feature of cloud computing is elasticity, that allows shrinking or expanding the resources allocated to an application in order to adjust to workload variations. The resource provisioning algorithm must also adhere to the performance requirements specified in the Service Level Agreement between the cloud provider and the client who runs the application. While the use of Reinforcement learning algorithms such as Q-learning has been proposed already to address this problem, those suffer from slow convergence and scalability issues. In this paper, we explore methods for overcoming such challenges and ensuring effective resource utilization. Preliminary experiments on CloudSim platform demonstrate the superiority of some of these methods over static, threshold-based and other reinforcement learning based allocation schemes.

Item Type:	Conference Paper
Publication:	Proceedings - 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems, FAS*W 2019
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords:	Cloud computing; Elasticity; Learning algorithms; Machine learning, Adaptive resource provisioning; Cloud applications; Performance requirements; Resource utilizations; Scalability issue; Service Level Agreements; Slow convergences; Workload variation, Reinforcement learning
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	23 Dec 2022 10:04
Last Modified:	23 Dec 2022 10:04
URI:	https://eprints.iisc.ac.in/id/eprint/78541

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