A Cost-Aware Mechanism for Optimized Resource Provisioning in Cloud Computing
Safiye Ghasemi, Mohammad Reza Meybodi, Mehdi Dehghan Takht Fooladi,, and Amir Masoud Rahmani

TL;DR
This paper introduces a novel learning-based resource provisioning mechanism for cloud computing that minimizes costs while efficiently meeting application requirements, outperforming traditional models by considering service-specific needs.
Contribution
It presents a cost-aware, learning automata-based approach that optimizes resource allocation for application services, focusing on total cost reduction and comprehensive workload evaluation.
Findings
Effective cost reduction in resource provisioning.
High adaptability to different workload types.
Performance meets design efficiency goals.
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…
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Taxonomy
TopicsCloud Computing and Resource Management · IoT and Edge/Fog Computing · Caching and Content Delivery
Methodstravel james
