ROAR: A QoS-oriented modeling framework for automated cloud resource allocation and optimization

Sun, Yu; White, Jules; Eade, Sean; Schmidt, Douglas C.

doi:10.1016/j.jss.2015.08.006

Cited by 47 publications

(33 citation statements)

References 10 publications

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“…The experiments measure the performance of two SaaS applications using three public clouds, and three private clouds, evaluating both the scaling up and out in Amazon EC2, and scaling out in Emulab and Open Cirrus. The study [51] presents a modeling framework (ROAR) for automated cloud resource allocation, optimization, and benchmarking. In two experiments using Amazon and Google clouds, they use the ROAR to deploy multi-tier applications to cloud providers and an auto-scaling engine.…”

Section: ) Benchmarkingmentioning

confidence: 99%

A Systematic Mapping Study of Empirical Studies on Software Cloud Testing Methods

Ahmad

Brereton

András

2017

2017 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

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Abstract-Context: Software has become more complicated, dynamic, and asynchronous than ever, making testing more challenging. With the increasing interest in the development of cloud computing, and increasing demand for cloud-based services, it has become essential to systematically review the research in the area of software testing in the context of cloud environments. Objective: The purpose of this systematic mapping study is to provide an overview of the empirical research in the area of software cloud-based testing, in order to build a classification scheme. We investigate functional and non-functional testing methods, the application of these methods, and the purpose of testing using these methods. Method: We searched for electronically available papers in order to find relevant literature and to extract and analyze data about the methods used. Result: We identified 69 primary studies reported in 75 research papers published in academic journals, conferences, and edited books. Conclusion: We found that only a minority of the studies combine rigorous statistical analysis with quantitative results. The majority of the considered studies present early results, using a single experiment to evaluate their proposed solution.

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Section: ) Benchmarkingmentioning

confidence: 99%

A Systematic Mapping Study of Empirical Studies on Software Cloud Testing Methods

Ahmad

Brereton

András

2017

2017 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

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“…This technique stressed on the need for a model-driven optimization architecture for multi-cloud systems. A 2ehaviour framework used for automatic resource allocation in cloud (ROAR) was proposed by Sun et al In [11]. This technique not only operates with its basis on resource allocation, it also provides techniques for optimization of the resource allocation decisions.…”

Section: Related Workmentioning

confidence: 99%

Location Based Optimal Package Selection in Multi-Cloud

Guruprakash¹,

SivaSathya²

2017

IJET

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Abstract-Cloud computing is a ubiquitous platform that can be used to access services and resources. The requirements for accessing a cloud resource is minimal, however, the users of a cloud platform are initially required to configure the type of services they need to access in the cloud. Even though cloud platform is elastic, frequent upscaling is costly. This paper presents an effective technique that can be used to automatically identify user's requirements and to allocate appropriate packages depending on the requirements. Usage logs of the client is grouped geographically and group based requirements are identified. This helps to determine the resource requirement for each group which address the problem of underutilization or overutilization of resources. These requirements are passed to PSO, along with the packages offered by multiple cloud operators in the same region to identify the best package for the current requirement. Experiments conducted on this architecture proves the effective working nature of the system in minimal time and with low QoS difference.

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“…Sun et al [27] present a tool to test, optimize, and automate cloud resource allocation decisions to meet QoS goals for web applications. 18 https://goo.gl/SxKG1e…”

Section: Related Workmentioning

confidence: 99%

Automatic non-functional testing of code generators families

et al. 2016

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The intensive use of generative programming techniques provides an elegant engineering solution to deal with the heterogeneity of platforms and technological stacks. The use of domain-specific languages for example, leads to the creation of numerous code generators that automatically translate highlevel system specifications into multi-target executable code. Producing correct and efficient code generator is complex and error-prone. Although software designers provide generally high-level test suites to verify the functional outcome of generated code, it remains challenging and tedious to verify the behavior of produced code in terms of non-functional properties. This paper describes a practical approach based on a runtime monitoring infrastructure to automatically check the potential inefficient code generators. This infrastructure, based on system containers as execution platforms, allows code-generator developers to evaluate the generated code performance. We evaluate our approach by analyzing the performance of Haxe, a popular high-level programming language that involves a set of cross-platform code generators. Experimental results show that our approach is able to detect some performance inconsistencies that reveal real issues in Haxe code generators.

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ROAR: A QoS-oriented modeling framework for automated cloud resource allocation and optimization

Cited by 47 publications

References 10 publications

A Systematic Mapping Study of Empirical Studies on Software Cloud Testing Methods

A Systematic Mapping Study of Empirical Studies on Software Cloud Testing Methods

Location Based Optimal Package Selection in Multi-Cloud

Automatic non-functional testing of code generators families

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