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Abstract: Elasticity is a fundamental feature of cloud computing and can be considered as a great advantage and a key benefit of cloud computing. One key challenge in cloud elastic... View more
Notes: IEEE Xplore Notice to Reader "Quantitative Modeling and Analytical Calculation of Elasticity in Cloud Computing" by Keqin Li published in IEEE Transactions on Cloud Computing Early Access Digital Object Identifier: 10.1109/TCC.2017.2665549 This article includes an author who was prohibited from publishing with IEEE prior to publication of the article. Due to this prohibition, reasonable effort should be made to remove all past references to this article, and refrain from future references to this article. We regret any inconvenience this may have caused.
Elasticity is a fundamental feature of cloud computing and can be considered as a great advantage and a key benefit of cloud computing. One key challenge in cloud elasticity is lack of consensus on a quantifiable, measurable, observable, and calculable definition of elasticity and systematic approaches to modeling, quantifying, analyzing, and predicting elasticity. Another key challenge in cloud computing is lack of effective ways for prediction and optimization of performance and cost in an elastic cloud platform. The present paper makes the following significant contributions. First, we present a new, quantitative, and formal definition of elasticity in cloud computing, i.e., the probability that the computing resources provided by a cloud platform match the current workload. Our definition is applicable to any cloud platform and can be easily measured and monitored. Furthermore, we develop an analytical model to study elasticity by treating a cloud platform as a queueing system, and use a continuous-time Markov chain (CTMC) model to precisely calculate the elasticity value of a cloud platform by using an analytical and numerical method based on just a few parameters, namely, the task arrival rate, the service rate, the virtual machine start-up and shut-down rates. In addition, we formally define auto-scaling schemes and point out that our model and method can be easily extended to handle arbitrarily sophisticated scaling schemes. Second, we apply our model and method to predict many other important properties of an elastic cloud computing system, such as average task response time, throughput, quality of service, average number of VMs, average number of busy VMs, utilization, cost, cost-performance ratio, productivity, and scalability. In fact, from a cloud consumer's point of view, these performance and cost metrics are even more important than the elasticity metric. Our study in this paper has two significance. On one hand, a cloud service provider can predict ...
Notes: IEEE Xplore Notice to Reader "Quantitative Modeling and Analytical Calculation of Elasticity in Cloud Computing" by Keqin Li published in IEEE Transactions on Cloud Computing Early Access Digital Object Identifier: 10.1109/TCC.2017.2665549 This article includes an author who was prohibited from publishing with IEEE prior to publication of the article. Due to this prohibition, reasonable effort should be made to remove all past references to this article, and refrain from future references to this article. We regret any inconvenience this may have caused.
Published in: IEEE Transactions on Cloud Computing ( Volume: 8 , Issue: 4 , 01 Oct.-Dec. 2020 )
Date of Publication: 07 February 2017
References is not available for this document.
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Cloud computing is a paradigm for enabling ubiquitous, convenient, and on-demand network accesses to a shared pool of configurable computing resources (e.g., servers, storage, networks, data, software, applications, and services), that can be rapidly provisioned and released with minimal management effort or service provider interaction [32]. The unique and essential characteristics of cloud computing include on-demand self-service, broad and variety of network access, resource pooling and sharing, rapid elasticity, measured and metered service. Among these features, elasticity is a fundamental and key feature of cloud computing, which can be considered as a great advantage and a key benefit of cloud computing, and perhaps what distinguishes this new computing paradigm from other ones, such as cluster and grid computing [14].
2016 IEEE International Conference on Smart Cloud (SmartCloud)
2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2023 IEEE - All rights reserved.
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2023 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.
All Books Conferences Courses Journals & Magazines Standards Authors Citations
Abstract: Elasticity is a fundamental feature of cloud computing and can be considered as a great advantage and a key benefit of cloud computing. One key challenge in cloud elastic... View more
Notes: IEEE Xplore Notice to Reader "Quantitative Modeling and Analytical Calculation of Elasticity in Cloud Computing" by Keqin Li published in IEEE Transactions on Cloud Computing Early Access Digital Object Identifier: 10.1109/TCC.2017.2665549 This article includes an author who was prohibited from publishing with IEEE prior to publication of the article. Due to this prohibition, reasonable effort should be made to remove all past references to this article, and refrain from future references to this article. We regret any inconvenience this may have caused.
Elasticity is a fundamental feature of cloud computing and can be considered as a great advantage and a key benefit of cloud computing. One key challenge in cloud elasticity is lack of consensus on a quantifiable, measurable, observable, and calculable definition of elasticity and systematic approaches to modeling, quantifying, analyzing, and predicting elasticity. Another key challenge in cloud computing is lack of effective ways for prediction and optimization of performance and cost in an elastic cloud platform. The present paper makes the following significant contributions. First, we present a new, quantitative, and formal definition of elasticity in cloud computing, i.e., the probability that the computing resources provided by a cloud platform match the current workload. Our definition is applicable to any cloud platform and can be easily measured and monitored. Furthermore, we develop an analytical model to study elasticity by treating a cloud platform as a queueing system, and use a continuous-time Markov chain (CTMC) model to precisely calculate the elasticity value of a cloud platform by using an analytical and numerical method based on just a few parameters, namely, the task arrival rate, the service rate, the virtual machine start-up and shut-down rates. In addition, we formally define auto-scaling schemes and point out that our model and method can be easily extended to handle arbitrarily sophisticated scaling schemes. Second, we apply our model and method to predict many other important properties of an elastic cloud computing system, such as average task response time, throughput, quality of service, average number of VMs, average number of busy VMs, utilization, cost, cost-performance ratio, productivity, and scalability. In fact, from a cloud consumer's point of view, these performance and cost metrics are even more important than the elasticity metric. Our study in this paper has two significance. On one hand, a cloud service provider can predict ...
Notes: IEEE Xplore Notice to Reader "Quantitative Modeling and Analytical Calculation of Elasticity in Cloud Computing" by Keqin Li published in IEEE Transactions on Cloud Computing Early Access Digital Object Identifier: 10.1109/TCC.2017.2665549 This article includes an author who was prohibited from publishing with IEEE prior to publication of the article. Due to this prohibition, reasonable effort should be made to remove all past references to this article, and refrain from future references to this article. We regret any inconvenience this may have caused.
Published in: IEEE Transactions on Cloud Computing ( Volume: 8 , Issue: 4 , 01 Oct.-Dec. 2020 )
Date of Publication: 07 February 2017
References is not available for this document.
IEEE Account
Change Username/Password
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Purchase Details
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View Purchased Documents
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Worldwide: +1 732 981 0060
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Cloud computing is a paradigm for enabling ubiquitous, convenient, and on-demand network accesses to a shared pool of configurable computing resources (e.g., servers, storage, networks, data, software, applications, and services), that can be rapidly provisioned and released with minimal management effort or service provider interaction [32]. The unique and essential characteristics of cloud computing include on-demand self-service, broad and variety of network access, resource pooling and sharing, rapid elasticity, measured and metered service. Among these features, elasticity is a fundamental and key feature of cloud computing, which can be considered as a great advantage and a key benefit of cloud computing, and perhaps what distinguishes this new computing paradigm from other ones, such as cluster and grid computing [14].
2016 IEEE International Conference on Smart Cloud (SmartCloud)
2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2023 IEEE - All rights reserved.
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2023 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.
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