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Computing Resources Scalability Performance Analysis in Cloud Computing Data Center

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Abstract

Today, cloud computing has become an essential technology in modern times, offering a wide range of benefits to organizations of all sizes. It provides access to computing resources on-demand over the internet, reducing costs and enabling organizations to respond quickly to changing business needs. Dynamic scalability is a crucial feature of cloud computing, allowing the system to dynamically allocate resources based on user demand at runtime while providing high quality of service (QoS) and performance to clients with minimal resource usage. This paper proposes a stochastic model based on queueing theory to study and analyze the performance of cloud data centers (CDC) and meet service level agreements (SLA) established with clients. The model is used to examine various performance metrics, including the mean response time, the mean waiting time, the probability of rejection, and the utilization of the system, as the arrival rate and the service rate vary. Simulation results are provided using the CloudSim simulator. The results of the analysis and simulation show that our model accurately estimates the number of virtual machines (VMs) required to meet QoS objectives, making it a valuable tool for improving the performance and scalability of cloud data centers. The results obtained from our analytical model are validated by an experimental example conducted on the Amazon Web Services (AWS) cloud platform.

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Acknowledgements

The authors thank the anonymous reviewers for their valuable comments, which have helped us to considerably improve the content, quality, and presentation of this article.

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Authors and Affiliations

  1. Hassan First University of Settat, Faculty of Sciences and Techniques, Computer, Networks, Mobility and Modeling Laboratory: IR2M, Settat, Morocco

    Oumaima Ghandour, Said El Kafhali & Mohamed Hanini

Authors
  1. Oumaima Ghandour
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  2. Said El Kafhali
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  3. Mohamed Hanini
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Contributions

Oumaima Ghandour developed the proposed model, performed the analytic calculations and performed the numerical and simulations results. Said El Kafhali contributed to the interpretation of the obtained results. Both Said El Kafhali and Mohamed Hanini authors contributed to the final version of the manuscript. Said El Kafhali supervised the work of this article.

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Correspondence to Said El Kafhali.

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Ghandour, O., El Kafhali, S. & Hanini, M. Computing Resources Scalability Performance Analysis in Cloud Computing Data Center. J Grid Computing 21, 61 (2023). https://doi.org/10.1007/s10723-023-09696-5

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