A Cost-Aware Mechanism for Optimized Resource Provisioning in Cloud Computing

• URL: http://arxiv.org/abs/2309.11299v1
• Date: Wed, 20 Sep 2023 13:27:30 GMT
• Title: A Cost-Aware Mechanism for Optimized Resource Provisioning in Cloud Computing
• Authors: Safiye Ghasemi, Mohammad Reza Meybodi, Mehdi Dehghan Takht Fooladi, and Amir Masoud Rahmani
• Abstract summary: We have proposed a novel learning based resource provisioning approach that achieves cost-reduction guarantees of demands. Our method adapts most of the requirements efficiently, and furthermore the resulting performance meets our design goals.
• Score: 6.369406986434764
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Due to the recent wide use of computational resources in cloud computing, new resource provisioning challenges have been emerged. Resource provisioning techniques must keep total costs to a minimum while meeting the requirements of the requests. According to widely usage of cloud services, it seems more challenging to develop effective schemes for provisioning services cost-effectively; we have proposed a novel learning based resource provisioning approach that achieves cost-reduction guarantees of demands. The contributions of our optimized resource provisioning (ORP) approach are as follows. Firstly, it is designed to provide a cost-effective method to efficiently handle the provisioning of requested applications; while most of the existing models allow only workflows in general which cares about the dependencies of the tasks, ORP performs based on services of which applications comprised and cares about their efficient provisioning totally. Secondly, it is a learning automata-based approach which selects the most proper resources for hosting each service of the demanded application; our approach considers both cost and service requirements together for deploying applications. Thirdly, a comprehensive evaluation is performed for three typical workloads: data-intensive, process-intensive and normal applications. The experimental results show that our method adapts most of the requirements efficiently, and furthermore the resulting performance meets our design goals.

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