A framework for ranking of cloud computing services

Garg, Saurabh; Versteeg, Steve; Buyya, Rajkumar

doi:10.1016/j.future.2012.06.006

Cited by 744 publications

(458 citation statements)

References 16 publications

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“…To solve this metric and assuming the QoS parameters are carefully selected, multi-attribute decision-making algorithms [42] such as simple additive weighting technique for order of preference by similarity to ideal solution or AHP can be used. Garg et al [38] present an AHP-based cloud ranking and selection model. This model can be beneficial for evaluating right cloud resources for multi-tiered health care applications.…”

Section: Qos For Real-time Patient Monitoringmentioning

confidence: 99%

Remote health care cyber‐physical system: quality of service (QoS) challenges and opportunities

Shah¹,

Yavari²,

Mitra

et al. 2016

IET cyber-phys. syst.

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There is a growing emphasis to find alternative non-traditional ways to manage patients to ease the burden on health care services largely fuelled by a growing demand from sections of population that is ageing. In-home remote patient monitoring applications harnessing technological advancements in the area of Internet of things (IoT), semantic web, data analytics, and cloud computing have emerged as viable alternatives. However, such applications generate large amounts of real-time data in terms of volume, velocity, and variety thus making it a big data problem. Hence, the challenge is how to combine and analyse such data with historical patient data to obtain meaningful diagnoses suggestions within acceptable time frames (considering quality of service (QoS)). Despite the evolution of big data processing technologies (e.g. Hadoop) and scalable infrastructure (e.g. clouds), there remains a significant gap in the areas of heterogeneous data collection, real-time patient monitoring, and automated decision support (semantic reasoning) based on well-defined QoS constraints. In this study, the authors review the state-of-the-art in enabling QoS for remote health care applications. In particular, they investigate the QoS challenges required to meet the analysis and inferencing needs of such applications and to overcome the limitations of existing big data processing tools.

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Section: Qos For Real-time Patient Monitoringmentioning

confidence: 99%

Remote health care cyber‐physical system: quality of service (QoS) challenges and opportunities

Shah¹,

Yavari²,

Mitra

et al. 2016

IET cyber-phys. syst.

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“…Garg et al [9] opined to evaluate cloud providers in an objective way to find the most suitable ones. The proposed framework SMICloud, based on ISO SMI [17], is a cloud services evaluation framework that addresses the issue of cloud provider selection based on benchmarking of services and feedback from user experiences.…”

Section: Related Workmentioning

confidence: 99%

A price-performance analysis of EC2, Google Compute and Rackspace cloud providers for scientiﬁc computing

Ullah¹,

Awan²,

Khiyal³

2016

J. Math. Computer Sci.

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One of the recently emerging areas in cloud computing is deployment of virtual machines across multiple clouds based on providers' ranking. This involves benchmarking of different cloud providers, development of different techniques for selection of candidate providers and frameworks for ranking cloud providers. Existing benchmarking studies are mostly focused on selection of best-fit cloud provider among a set of cloud providers for a particular set of quality attributes based on industry best standard tools and techniques. However, most of the researches are focused on performance of IaaS cloud providers and price-performance analysis is normally ignored while benchmarking IaaS metrics. In this work, we propose a novel QoS based ranking methodology along with priceperformance analysis that can be used as an input for selecting candidate cloud providers. Our proposed mechanism allows cloud consumers to find the most cost effective virtual machines for a given set of user preferences. As a case study, we present performance evaluation and benchmarking results of three major cloud providers: Google, Amazon and Rackspace.

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“…For example, authors in [16] present analytic hierarchy process based method for cloud ranking and selection. However, there is a dearth of literature that considers joint cloud and network selection.…”

Section: Qos-aware Cloud and Network Selection And Orchestrationmentioning

confidence: 99%

Orchestrating Quality of Service in the Cloud of Things Ecosystem

Jayaraman¹,

Mitra²,

Saguna³

et al. 2015

2015 IEEE International Symposium on Nanoelectronic and Information Systems

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Abstract-Cloud of Things (CoT) is a vision inspired byInternet of Things (IoT) and cloud computing where the IoT devices are connected to the clouds via the Internet for data storage, processing, analytics and visualization. CoT ecosystem will encompass heterogeneous clouds, networks and devices to provide seamless service delivery, for example, in smart cites. To enable efficient service delivery, there is a need to guarantee a certain level of quality of service from both cloud and networks perspective. This paper discusses the Cloud of Things, cloud computing, networks and new quality of service management research issues arising due to realisation of CoT ecosystem vision.

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A framework for ranking of cloud computing services

Cited by 744 publications

References 16 publications

Remote health care cyber‐physical system: quality of service (QoS) challenges and opportunities

Remote health care cyber‐physical system: quality of service (QoS) challenges and opportunities

A price-performance analysis of EC2, Google Compute and Rackspace cloud providers for scientiﬁc computing

Orchestrating Quality of Service in the Cloud of Things Ecosystem

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