Performance and Cost Assessment of Cloud Services

Brebner, Paul; Liu, Anna

doi:10.1007/978-3-642-19394-1_5

Cited by 23 publications

(17 citation statements)

References 5 publications

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“…In particular, Brebner and Liu [32] have reported on running applications costs with multiple workloads in different Amazon, Microsoft and Google offering when choosing suitable cloud service providers for applications migration.…”

Section: Comparative Evaluationmentioning

confidence: 99%

A Comparative Evaluation of State-of-the-Art Cloud Migration Optimization Approaches

Abdelmaboud

Jawawi

Ghani

et al. 2014

Advances in Intelligent Systems and Computing

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Abstract. Cloud computing has become more attractive for consumers to migrate their applications to the cloud environment. However, because of huge cloud environments, application customers and providers face the problem of how to assess and make decisions to choose appropriate service providers for migrating their applications to the cloud. Many approaches have investigated how to address this problem. In this paper we classify these approaches into non-evolutionary cloud migration optimization approaches and evolutionary cloud migration optimization approaches. Criteria including cost, QoS, elasticity and degree of migration optimization have been used to compare the approaches. Analysis of the results of comparative evaluations shows that a Multi-Objectives optimization approach provides a better solution to support decision making to migrate an application to the cloud environment based on the significant proposed criteria. The classification of the investigated approaches will help practitioners and researchers to deliver and build solid approaches.

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Section: Comparative Evaluationmentioning

confidence: 99%

A Comparative Evaluation of State-of-the-Art Cloud Migration Optimization Approaches

Abdelmaboud

Jawawi

Ghani

et al. 2014

Advances in Intelligent Systems and Computing

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“…For example, Brebner and Liu conducted empirical evaluations of different cloud infrastructures using a suite of cloud testing applications [2]. They also use those experimental evaluations to predict the resource requirements in terms of application performance, cost and limitations of a realistic application for different deployment scenarios.…”

Section: Related Workmentioning

confidence: 99%

Windows Azure: Resource Organization Performance Analysis

Gušev

Ristov

Koteska

et al. 2014

Advanced Information Systems Engineering

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International audienceCloud customers can scale the resources according to their needs in order to avoid application bottleneck. The scaling can be done in two ways, either by increasing the existing virtual machine instance with additional resources, or by adding an additional virtual machine instance with the same resources. Although it is expected that the costs rise proportionally to scaling, we are interested in finding out which organization offers scaling with better performance. The goal of this paper is to determine the resource organization that produces better performance for the same cost, and help the customers decide if it is better to host a web application on a more ”small” instances or less ”large” instances. The first hypothesis states that better performance is obtained by using more and smaller instances. The second hypothesis is that the obtained speedup while scaling the resources is smaller than the scaling factor. The results from the provided experiments have not proven any of the hypotheses, meaning that using less, but larger instances results with better performance and that the user gets more performances than expected by scaling the resources

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“…In [13]. Brebner and Liu have used a Service Oriented Performance Modeling Technology for modeling the performance and scalability of Service Oriented Applications architected for a variety of platforms.…”

Section: Earlier Workmentioning

confidence: 99%

Performance Analysis of Web Applications on IaaS Cloud Computing Platform

Addamani¹,

Basu²

2013

IJCA

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This paper proposes a queuing model for performance analysis of web applications on a Cloud Environment. In this model, the IaaS cloud computing platform is modeled as multiple queues and the virtual machines VMs are modeled as service centers. The instances act as virtual machines and VMs run on servers, its number decided a priori before running an application. The model is based on the Reserved Instances behavior of applications on Amazon EC2 [1]. The web applications running on the Cloud platform were analyzed to determine the statistical distributions of the relevant parameters and experiments were conducted for validation of the model by running the web applications on EC2. Results show that the model though simple can predict the performance accurately.

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Performance and Cost Assessment of Cloud Services

Cited by 23 publications

References 5 publications

A Comparative Evaluation of State-of-the-Art Cloud Migration Optimization Approaches

A Comparative Evaluation of State-of-the-Art Cloud Migration Optimization Approaches

Windows Azure: Resource Organization Performance Analysis

Performance Analysis of Web Applications on IaaS Cloud Computing Platform

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