Selecting optimum cloud availability zones by learning user satisfaction levels

Unuvar, Merve; Tosi, Stefania; Doǧanata, Yurdaer N.; Steinder, Małgorzata; Tantawi, Asser N.

doi:10.1109/tsc.2014.2381225

Cited by 18 publications

(6 citation statements)

References 10 publications

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“…'s concept of geographically dispersed data centres for cloud service providers is proposed, and an algorithm is suggested for service providers' net profit maximization through the use of energy-efficient, profit-and cost-aware request dispatching and resource allocation. Customer satisfaction, however, has not been taken into account [20,21,22,23,24,25,26,27,28]. Utilizing the utility theory from economics, Chen et al [20] proposed the utility model for gauging customer satisfaction in cloud systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Multi-Server Configuration for Cloud Computing to Maximize Profits with Customer Satisfaction

2023

IRJMETS

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Cloud computing is a widely adopted technology that offers very cost-and energy-efficient computing resource management. The primary focus for cloud service providers to pay attention to is the configuration of cloud service platforms for earning the maximum profit. This issue can be solved by considering customer satisfaction. We proposed to investigate the interrelationship between customer satisfaction and profit maximization and develop the profit optimization model with customer satisfaction. Further we design the optimal cloud configuration using discrete hill climbing algorithm to have maximum profit. Finally, we will implement the proposed work using Java and HTML and execution results will confirm the effectiveness of the system.

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Section: Literature Reviewmentioning

confidence: 99%

Optimal Multi-Server Configuration for Cloud Computing to Maximize Profits with Customer Satisfaction

2023

IRJMETS

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“…Using the history logs, optimal zones are selected. This prediction approach has been extended in reference, 17 in order to refine the obtained results' accuracy. That's by learning cloud zones' new behavior on the basis of past scenarios.…”

Section: Selection Of Cazsmentioning

confidence: 99%

“…The need, mentioned above, for multicloud environments should be followed by a well-studied selection of the hosting cloud zones for each BPaaS, according to its requirements. Although that has been treated in reference 17 in a predictive way, guaranteeing the resource provisioning agility, we notice that there are additional challenges that would be treated. In fact, only the user satisfaction level has been taken into account in the learning system of cloud zones' behavior.…”

Section: Selection Of Cazsmentioning

confidence: 99%

Predictive BPaaS management with quantum and neural computing

Hedhli

Mezni

Saïd

2022

J Software Evolu Process

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With the increasing adoption of cloud computing, the deployment and management of business processes over cloud environments have become an essential operation for most enterprises, leading to the emergence of BPaaS (Business Process as a Service) as a new cloud service model. This SaaS-like service, like its ancestors, should be strategically distributed and managed over multiple cloud zones, while taking into account several constraints and conditions (e.g., sensitivity of BPaaS fragments, insecure and untrusted cloud zones, lack of resources, and workload changes). However, current BPaaS approaches are static, which means that they are no longer suitable to manage such enterprise-oriented cloud service model and to deal with the uncertain and dynamic nature of cloud availability zones. To fill this gap, we adopt a predictive BPaaS management strategy by proposing a model that forecasts the next-short time overload of cloud zones. These latter, as hosting environments for the managed BPaaS, are categorized as overloaded or underloaded, which triggers the migration of BPaaS fragments to high-performance cloud zones. The proposed neural network prediction model (called QGA-NN) is enhanced with a quantum genetic algorithm to optimize the prediction of cloud zones' overload. QGA-NN is evaluated using a BPaaS placement algorithm, which we defined as a triggered management operation. Experimental results have proved the accuracy and effectiveness of our predictive approach, compared with state-ofthe-art solutions.

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“…They made sure that these experiments and simulations were done under different traffic conditions and successfully completed the experiments and later foumd out they could even increase the user experience and decreasing the energy consumption. The Authors Merve Unuvar, Stefania Tosi, Yurdaer N. Doganata, Malgorzata Steinder and Asser N. Tantawi proposed a way to select the best available zone that contains the business applications from the zones that contain the same worldwide [4]. Since each zone offers different quality of service it is essential to select the zone which has good QoS that would satisfy the user requirements.…”

Section: IImentioning

confidence: 99%

Profit Maximization for Clients by Optimized Resource usage in Cloud Computing

Abisheg.¹

2018

IJRASET

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The vast increase in the usage of cloud storage has led to problem solving techniques for the efficient use the cloud system. The methods so far utilized have not brought a solution to pay for what a user consumes, rather is purchased on a nonstatistical and non-analyzed manner. The proposed paper contributes an idea by providing options of short term and long term renting schemes for the cloud storage, through which the consumer pays for the data that is actually uploaded, and need not acquire on an earlier estimated data. This process is much efficient on both the consumer and the server side, as it conserves the rental payment, as well as the data space in the cloud.

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Selecting optimum cloud availability zones by learning user satisfaction levels

Cited by 18 publications

References 10 publications

Optimal Multi-Server Configuration for Cloud Computing to Maximize Profits with Customer Satisfaction

Optimal Multi-Server Configuration for Cloud Computing to Maximize Profits with Customer Satisfaction

Predictive BPaaS management with quantum and neural computing

Profit Maximization for Clients by Optimized Resource usage in Cloud Computing

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