Towards a Taxonomy of Performance Evaluation of Commercial Cloud Services

Li, Zheng; O’Brien, Liam; Cai, Rainbow; Zhang, He

doi:10.1109/cloud.2012.74

Cited by 39 publications

(50 citation statements)

References 20 publications

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“…Distinctly the selection of an appropriate framework depend on provision of its features that could be evaluated through fundamental metrics [30]. In this section, we have presented the fundamental aspects and their perspective QoS metrics through that the services (which provide QoS for multimedia applications over cloud computing) could be evaluated.…”

Section: Qos Metrics For Cloudy Multimedia Evaluationmentioning

confidence: 99%

Provisioning Quality of Service for Multimedia Applications in Cloud Computing

Ashraf¹,

Arif²,

Basit³

et al. 2018

IJITCS

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Abstract-Since the last decade, many new trends have been introduced to access network technologies and services through internet. Cloud computing is one of those significant technologies that reduce the cost and increase the productivity by providing a variety of services. Recently, cloud computing based system is primarily used for multimedia applications. Over the cloud computing, multimedia applications has some significant quality of service (QoS) requirements such as bandwidth, jitter, latency etc. But due to some limitations in services providing, it is constantly complex to make selection for an appropriate service. Keeping in view the provision of multimedia services through cloud computing, many different concepts and approaches that provide better cloud services under the constraints of QoS attributes have been described in the literature. The goal of this paper is to assess the applicability and provision of multimedia applications over cloud computing through enhanced quality of service. We have identified the primary quality of service msetrics evaluation of multimedia services over cloud computing. Furthermore, under these metrics we evaluated the existing approaches that provide multimedia related services with their strengths and limitations. This evaluation approach could provide the service that can provide better QoS in multimedia applications over cloud computing.

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Section: Qos Metrics For Cloudy Multimedia Evaluationmentioning

confidence: 99%

Provisioning Quality of Service for Multimedia Applications in Cloud Computing

Ashraf¹,

Arif²,

Basit³

et al. 2018

IJITCS

View full text Add to dashboard Cite

show abstract

“…4), we name the Scalability from the second perspective of changing workload as Original Scalability. Moreover, it has been identified that Cloud service variability and scalability have to be reflected by the change of value of other performance features [30]. Corresponding to the aforementioned MediaWise architecture, we may evaluate the variability and scalability of Cloud services through observing the performance change around three physical properties, namely Communication (e.g., Ethernet), Computation (e.g., VM Instance), and Storage (e.g., BLOB Original Scalability:…”

Section: Requirement Recognition and Service Feature Identificationmentioning

confidence: 99%

“…In this paper, we use variability to indicate the extent of fluctuation in values of an individual service performance index. Considering Cloud service variability could be related to time and location [30], we define two requirement questions around variability: How variable are the related Cloud services when running MediaWise Cloud for a period of time?…”

Section: Requirement Recognition and Service Feature Identificationmentioning

confidence: 99%

Towards understanding the runtime configuration management of do-it-yourself content delivery network applications over public clouds

Mitra

Zhang

et al. 2014

Future Generation Computer Systems

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Towards understanding the runtime configuration management of do-it-yourself content delivery network applications over public clouds, Future Generation Computer Systems (2014), http://dx.doi.org/10.1016/j.future. 2013.12.019 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Towards Understanding the Runtime Configuration Management of Do-It-Yourself Content Delivery NetworkApplications over Public Clouds AbstractCloud computing is a new paradigm shift which enables applications and related content (audio, video, text, images, etc.) to be provisioned in an on-demand manner and being accessible to anyone anywhere in the world without the need for owning expensive computing and storage infrastructures. Interactive multimedia content-driven applications in the domains of healthcare, aged-care, and education have emerged as one of the new classes of big data applications. This new generation of applications need to support complex content operations including production, deployment, consumption, personalisation, and distribution. However, to efficiently provision these applications on the Cloud data centres, there is a need to understand their run-time resource configurations. For example: (i) where to store and distribute the content to and from driven by end-user Service Level Agreements (SLAs)? (ii) how many content distribution servers to provision? and (iii) what Cloud VM configuration (number of instances, types, speed, etc.) to provision? In this paper, we present concepts and factors related to engineering such content-driven applications over public Clouds. Based on these concepts and factors, we propose a performance evaluation methodology for quantifying and understanding the runtime configuration these classes of applications. Finally, we conduct several benchmark driven experiments for validating the feasibility of the proposed methodology.

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“…When it comes to the performance evaluation of Cloud services, our previous taxonomy work [15] can be used to facilitate exploring available performance properties. Following the original study, we directly identified the combination of the related performance properties as Computation Latency and also Variability of Computation Latency, as shown in Figure 2.…”

Section: ) Service Feature Identificationmentioning

confidence: 99%

“…By exploring the existing practices of Cloud services evaluation [19], we show that three service features have been mainly of concern, namely Performance, Economics, and Security. In particular, the elements of the Performance feature can be divided into Physical Properties and Capacities [15], while the Economics feature covers Cost and Elasticity of using Cloud services. Although the Security feature has not been well evaluated yet [19], it may comprise numerous security concerns ranging from access control to prosecution [4], [8].…”

Section: B Service Feature Identificationmentioning

confidence: 99%

CEEM: A Practical Methodology for Cloud Services Evaluation

O’Brien²,

Zhang

2013

2013 IEEE Ninth World Congress on Services

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Abstract-Given an increasing number of Cloud services available in the market, evaluating candidate Cloud services is crucial and beneficial for both service customers (e.g. costbenefit analysis) and providers (e.g. direction of improvement). When it comes to performing any evaluation, a suitable methodology is inevitably required to direct experimental implementations. Nevertheless, there is still a lack of a sound methodology to guide the evaluation of Cloud services. By borrowing the lessons from evaluation of traditional computing systems, referring to the guidelines for Design of Experiments (DOE), and summarizing the existing experiences of real experimental studies, we proposed a generic Cloud Evaluation Experiment Methodology (CEEM) for Cloud services evaluation. Furthermore, we have established a pre-experimental knowledge base and specified corresponding suggestions to make this methodology more practical in the Cloud Computing domain. Through evaluating the Google AppEngine Python runtime as a preliminary validation, we show that Cloud evaluators may achieve more rational and convincing experimental results and conclusions following such an evaluation methodology.

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Towards a Taxonomy of Performance Evaluation of Commercial Cloud Services

Cited by 39 publications

References 20 publications

Provisioning Quality of Service for Multimedia Applications in Cloud Computing

Provisioning Quality of Service for Multimedia Applications in Cloud Computing

Towards understanding the runtime configuration management of do-it-yourself content delivery network applications over public clouds

CEEM: A Practical Methodology for Cloud Services Evaluation

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