SMICloud: A Framework for Comparing and Ranking Cloud Services

Garg, Saurabh; Versteeg, Steve; Buyya, Rajkumar

doi:10.1109/ucc.2011.36

Cited by 270 publications

(172 citation statements)

References 7 publications

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“…[7] Many frameworks and taxonomies have been developed to assist enterprises as they consider the use of the cloud. [6][2] [17] These frameworks generally focus on technical capabilities and measure business-relevant attributes. Others provide mechanisms for comparing business services.…”

Section: Cloud Standardsmentioning

confidence: 99%

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Framework for Cloud Usability

Stanton

Theofanos

Joshi

2015

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Section: Cloud Standardsmentioning

confidence: 99%

“…Others provide mechanisms for comparing business services. For example, Garg et al [6] developed a framework that measures the quality of cloud services and ranks. Usability features have only been mentioned in passing, if at all.…”

Section: Cloud Standardsmentioning

confidence: 99%

Framework for Cloud Usability

Stanton

Theofanos

Joshi

2015

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“…Garg et al [3] also recognize the difficulty for customers to select service providers. In their paper, they propose a mechanism and a framework to measure the quality and prioritize Cloud services.…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach for Performance Characterization of IaaS Clouds

Ács

Németh

Gergely

2014

Lecture Notes in Computer Science

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Abstract. Infrastructure-as-a-Service (IaaS) clouds are widely used today, however there are no standardized or commonly used performance evaluation methods and metrics that can be used to compare the services of the different providers. Performance evaluation tools and benchmarks are able to grasp some aspects or details of performance but for various reasons are not capable to characterize cloud performance. Our aim is to collect these elementary or primitive facets of performance and derive high-level aggregated and qualitative performance characterization semantically far above the output of tools and benchmarks. We designed and implemented a framework that collects low-level raw performance data (in terms of CPU, disk, memory and network) of cloud providers based on standard benchmark tools and these data are aggregated and evaluated using a hierarchical fuzzy system. In this process performance characteristics are associated with symbolic values and fuzzy inference is applied to produce the normalized qualitative comparable and readable performance metrics. In this paper, we discuss the issues of cloud performance analysis, present the concept and implementation of our method, illustrate the proposed solution by comparing -in terms of performancethe general purpose medium instance type of the Amazon EC2 cloud (in Ireland) and the standard instance type of the OpenNebula installation at MTA SZTAKI.

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“…The SMICloud would let users compare different Cloud offerings, according to their priorities and along several dimensions, and select whatever is appropriate to their needs. However, without having precise measurement models for each attribute, it is not possible to compare different Cloud services or even discover them [10]. Therefore, SMICloud uses historical measurements and combines them with promised values to find out the actual value of an attribute.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, SMICloud uses historical measurements and combines them with promised values to find out the actual value of an attribute. We also give [10].…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Quality Model for IaaS Provider with Non-Quantifiable Key Performance Indicators

Joshi¹,

Abdulhussein²,

Twinamatsiko³

et al. 2014

IJCCE

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Abstract-Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources Cloud computing is significant because it encapsulates a range of different technologies that have developed through the history of commercial computing. The model analyzed is known as Service Measurement Index (SMI) Model, which is a set of business-relevant Key Performance Indicators (KPI's) that provide a standardized method for measuring and comparing a business service regardless of whether that service is internally provided or sourced from an outside company. The SMI model analyzed is only applicable to the quantifiable attributes, thus in this research, we have concentrated on the Non-quantifiable attributes (KPIs) which, once incorporated, would not only enrich the customer experience but also provide more accurate customer results together with improved customer satisfaction.

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SMICloud: A Framework for Comparing and Ranking Cloud Services

Cited by 270 publications

References 7 publications

Framework for Cloud Usability

Framework for Cloud Usability

A Novel Approach for Performance Characterization of IaaS Clouds

An Enhanced Quality Model for IaaS Provider with Non-Quantifiable Key Performance Indicators

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