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Computing Resources Scalability Performance Analysis in Cloud Computing Data Center

Computing Resources Scalability Performance Analysis in Cloud Computing Data Center 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Grid Computing Springer Journals

Computing Resources Scalability Performance Analysis in Cloud Computing Data Center

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References (34)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
1570-7873
eISSN
1572-9184
DOI
10.1007/s10723-023-09696-5
Publisher site
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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.

Journal

Journal of Grid Computing – Springer Journals

Published: Dec 1, 2023

Keywords: Cloud computing; Scalability; CloudSim; Service level agreements; Resources management; Quality of service; Queueing theory

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