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

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 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.