OCEDS: Optimal Cost-Effective Data Storage in Cloud Data Centers

Introduction

As companies gradually approach services via the cloud, the computing world is moving towards an essential transition from a product hub to a service direction. It is defined as a computer model that differs from the services provided and used in the Internet model. The result of completing an all-encompassing model from data center to cloud access devices. From other processes of computer services, two attributes distinguish cloud computing: 1) flexibility and 2) scalability.

Flexibility refers to the ability to arbitrarily obtain small or large. Thus consumers can only pay for the computer resources they need at a given time. Measuring the ability to measure the performance of a service to improve the number of allocated devices, for example, expanded memory and network bandwidth and disk storage.

Cloud computing is an organized service that improves the performance of a data center through workload integration with optimal utilization and energy management techniques. Focusing on improving space to save and implement time at low cost is a significant concern in the cloud computing industry. This work proposed the Optimal Cost Effective Data Storage (OCEDS) method to reduce costs in cloud computing.

Two entities are involved in cost improvement in cloud computing:

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  Cost optimization performed by Cloud Service Providers (CSPs); and

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  Cost optimization performed by consumers.

The rest of the paper is organized like this - section II reviews the related work of previous researchers about low cost data storage techniques in cloud computing. Section III presents the process of the OCEDS algorithm. Section IV discusses the experimental results of the OCEDS algorithm. After that section V concludes the paper.

Related Work

The ECOGreen, proposed by (Pahlevan A et al., 2020) is a comprehensive strategy for the datacenter RS issue and a virtual machine (VM) allocation enhancement that meets the hourly power market hurdles in the presence of power saving and renewable power. ECOGreen saves 71 percent on electricity prices compared to other sophisticated datacenter power cutting technology participating in the power market.

(Yeganeh H et al., 2019) proposed an efficiency plan with dynamic pricing mechanism that takes into account different factors in reducing energy consumption in the green data centers of the fourth / fifth generation mobile system networks that provide mobile cloud computing services. The proposed method determines the optimal number of servers and addresses the transaction between operating cost and service delay.

(Bhandayker Y. R, 2019) establish that price savings were a strong incentive for companies to adopt cloud computing. CC's services are generally classified into three categories: IaaS (Infrastructure as a Service), PaaS (Platform as a Service) and SaaS (Software as a Service), each service belonging to a specific category and provided at a specific cost. There are two important pricing approaches. Charging per application is the most commonly used approach (Al-Roomi M et al., 2013) in which a customer is charged per unit time. With a consistent pricing approach, a customer can use an unlimited amount of unit resources. But, in a few contracts, use is prohibited to the maximum extent that customers do not want to achieve. In some cases, their use is reduced after reaching the maximum limit.

(Al-Roomi M et al., 2013) One of the price approaches: fixed price, no size limits, fixed price and one unit rate and fixed purchase quantity and one unit price rate. Research focused on understanding consumer motivation demonstrates that they are willing to send money and are willing to pay for the services they have. Occasionally they are not charged at fixed prices with services they do not consume.

(Wu S. Y & Banker R. D, 2010) A few providers find that they set prices for the application and offer some customer surplus to the consumer in the best possible way.

(Lambrecht A & Skiera B, 2006) have recognized two types of dependencies: one is the case based on a fixed price, in which customers prefer a fixed price approach even if they pay a lower price per application. Second, there are fee-based options where customers want to pay even if they pay less than the standard price for an application.

(Khurana R & Bawa R. K, 2017) proposed a quality-based cloud service broker for optimal cloud service provider selection. CSPs claim that the services provided to them are unique and hassle-free. To test their claim, CSB checks the service quality of CSPs and the level of user requirements.