An Approach to Estimating Cost of Running Cloud Applications Based on AWS

He, Huihong; Ma, Zhiyi; Li, Xiang; Chen, Hongjie; Shao, Wang

doi:10.1109/apsec.2012.84

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…We recommend organic integration of the above two technologies, where the application characteristic model is created by static analysis before deployment and updated by runtime model generation incrementally during operation. The readers may refer to our prior work [15] to obtain additional details.…”

Section: Figure 2 Package Organization Of Meta-modelmentioning

confidence: 99%

“…PurchaseStrategy exploits the environment provider's billing model and preferential strategies to make a capacity plan, in order to meet the predicted application system workload within a certain budget constraint. [15] PriorityStrategy includes two strategy types according to the adaptation level. ServiceOrientedOptimization strategy is used to improving the target service performance by scarifying other services, for example, grabbing resources from other services.…”

Section: Figure 5 Semantic Model For Application Adaptation Strategymentioning

confidence: 99%

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How the crowd impacts commercial applications: a user-oriented approach

Chen

et al. 2014

Proceedings of the 1st International Workshop on Crowd-Based Software Development Methods and Technologies

Self Cite

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As crowdsourcing has been applied to a variety of disciplines, e.g. marketing and operationalization, more and more scientists turn their sights to how the crowd innovate software engineering to produce high quality software. However, they mainly focus on the impacts brought by domain experts or experienced developers on developing and managing open source softwares, whereas how softwares are influenced by the ordinary people e.g. end users is seldom discussed and easily omitted. To fill up the research gaps, we investigate into commercial application improvement paradigm with assistance of user crowd. The approach focuses on end users by proposing a workflow loop to form a healthy cycle between them and applications. Especially, the approach propose a suggestion model to encourage users to participate into application runtime adaptation. So far, a prototype is developed to enable the crowd to raise and modify their advices, and our prior work has proven the effectiveness in which applications consider the users' advices to adapt themselves.

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Section: Figure 2 Package Organization Of Meta-modelmentioning

confidence: 99%

Section: Figure 5 Semantic Model For Application Adaptation Strategymentioning

confidence: 99%

How the crowd impacts commercial applications: a user-oriented approach

Chen

et al. 2014

Proceedings of the 1st International Workshop on Crowd-Based Software Development Methods and Technologies

Self Cite

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show abstract

“…Recently, IaaS cost estimation with the dynamic cost model have been proposed [17][18][19][20][21]. Agarwal et al present a method for predicting spot prices using techniques of artificial neural network and show the experimental evaluation results on various instances of Amazon Elastic Compute Cloud (EC2) [17].…”

Section: Related Workmentioning

confidence: 99%

A Cost Estimation Model for Cloud Services and Applying to PC Laboratory Platforms

Cho

Bahn²

2020

Processes

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IaaS (Infrastructure as a Service) is a well-known computing service, which provides infrastructures over the cloud without owning real hardware resources. This is attractive as resources can be scaled up and down instantly according to the user's computing demands. Customers of such services would like to adjust the utilization policy promptly by considering the charge of the service, but an instantaneous response is not possible as it takes several hours or even a couple of days for cloud service providers to inform the billing information. In this article, we present an instant cost estimation model for estimating the cost of public cloud resources. Specifically, our model estimates the cost of IaaS by monitoring the usage of resources on behalf of virtual machine instances. As this is performed by generating a user-side metering daemon, it is very precise and thus similar to the resource usage evaluated by the cloud service provider. To validate our model, we run PC laboratory services for 50 students in two classes by making use of a public cloud during a semester. Experimental results show that the accuracy of our model is over 99.3% in comparison with the actual charge of the public cloud.

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Adaptive Cost Efficient Framework for Cloud-Based Machine Learning

Pakdel

Herbert

2017

2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC)

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An Approach to Estimating Cost of Running Cloud Applications Based on AWS

Cited by 5 publications

References 4 publications

How the crowd impacts commercial applications: a user-oriented approach

How the crowd impacts commercial applications: a user-oriented approach

A Cost Estimation Model for Cloud Services and Applying to PC Laboratory Platforms

Adaptive Cost Efficient Framework for Cloud-Based Machine Learning

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