Investigating decision support techniques for automating Cloud service selection

Zhang, Miranda; Ranjan, Rajiv; Haller, Armin; Georgakopoulos, Dimitrios; Strazdins, Peter

doi:10.1109/cloudcom.2012.6427501

Cited by 37 publications

(30 citation statements)

References 16 publications

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“…The services used for the selection are storage, and price_storage_requests, provider, compute, network, regional_price. Regional price include other components [25]. [25] Previous studies failed to provide a systematic method that enables selection of a suitable cloud service provider based on requirements as well as security and privacy requirements.…”

Section: Cloud Framework and Supporting Toolsmentioning

confidence: 99%

“…Regional price include other components [25]. [25] Previous studies failed to provide a systematic method that enables selection of a suitable cloud service provider based on requirements as well as security and privacy requirements. Mouratidisa et al [26] proposed a framework, according to the organisational context, identified goals, actors, tasks, resources, and plans.…”

Section: Cloud Framework and Supporting Toolsmentioning

confidence: 99%

“…The service selection technique has been applied by Zhang [25] by using Structured Query Language (SQL) and regular expressions to minimize the negative side and strengthen restrains of cloud migration. This way, an improved cloud selection comparison Web system is created and it is illustrated in Figure 7.…”

Section: Cloud Framework and Supporting Toolsmentioning

confidence: 99%

See 2 more Smart Citations

Advances in Higher Educational Resource Sharing and Cloud Services for KSA

Aljebreen¹,

Dahanayake²,

Syed³

2015

IJCSES

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show abstract

Section: Cloud Framework and Supporting Toolsmentioning

confidence: 99%

Section: Cloud Framework and Supporting Toolsmentioning

confidence: 99%

See 1 more Smart Citation

Advances in Higher Educational Resource Sharing and Cloud Services for KSA

Aljebreen¹,

Dahanayake²,

Syed³

2015

IJCSES

View full text Add to dashboard Cite

show abstract

“…By treating the Cloud service measurement as a decision-making problem, Zhang et al (2012) suggested the multi-criteria decision-making technique Analytic Hierarchy Process (AHP) to handle various and mixed qualitative and quantitative criteria. AHP is based on pair-wise comparisons of the criteria.…”

Section: Analytic Hierarchy Process (Ahp) Based Decisionmentioning

confidence: 99%

Boosting Metrics: Measuring Cloud Services From the Holistic Perspective

Li¹,

O’Brien²,

Zhang³

2014

STCC

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Studies have shown that Cloud services evaluation would be crucial and beneficial for both service customers and providers, and metrics would play a vital role in any evaluation implementation. Considering the numerous and various aspects of Cloud services, a frequent suggestion is to perform evaluation from a holistic view. The currently normal strategy of holistic evaluation is to use a set of metrics along with a suite of benchmarks to conduct separated experiments. Given the separated, diverse, and even possibly conflicting measurement criteria, it could be still hard for customers with such evaluation reports to understand an evaluated Cloud service from a global perspective. Inspired by the boosting approaches to machine learning, we proposed the concept Boosting Metrics to represent all the potential approaches that are able to deliver summary measurement of Cloud services. Essentially, the idea of boosting metrics is to holistically measure Cloud services with concern of service properties, which supplements the strategy of employing benchmark suites that is to holistically evaluate Cloud services with concern of different workloads. This paper introduces two types of preliminary approaches, and unifies a set of sophisticated measurements into the notion of boosting metrics. In particular, we show that boosting metrics can be used as a summary Response for applying experimental design to Cloud services evaluation. Although the concept Boosting Metrics was refined based on our work in the Cloud Computing domain, we believe it can be easily adapted to the evaluation work of other computing paradigms.

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“…web server, databases, message queuing systems and monitoring systems). Cloud service types can be abstracted into three layers: software as a service (SaaS), platform as a service (PaaS) and infrastructure as a service (IaaS) [4]. Hardware and software resources form the basis for delivering IaaS and PaaS.…”

Section: Introductionmentioning

confidence: 99%

A QoS-satisfied Prediction Model for Cloud-service Composition Based on Hidden Markov Model

Wu¹,

Zhang²,

Zheng³

et al. 2013

Int. J. Onl. Eng.

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Abstract-There are various significant issues in cloud computing, such as service provision, service matching and service assessment, which have attracted researchers' attentions recently. QoS play an increasingly important role during the procedure of cloud-based service provision for seamless and dynamic integration of cloud-service components. In this paper, we focus on the QoS-satisfied prediction for cloud-service composited components and present a QoS-satisfied prediction model based on hidden Markov model. For a general process of cloud-service provision, if the user's QoS could not be satisfied only by one cloud-service component, the component composition should be considered to provide to user, where the QoSsatisfied capability of composited components need to be proactively predicted to guarantee the user's QoS. We discuss the proposed model in detail and proof the model partly. The simulation results show that our model can obtain rather high prediction accuracy rate.

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Investigating decision support techniques for automating Cloud service selection

Cited by 37 publications

References 16 publications

Advances in Higher Educational Resource Sharing and Cloud Services for KSA

Advances in Higher Educational Resource Sharing and Cloud Services for KSA

Boosting Metrics: Measuring Cloud Services From the Holistic Perspective

A QoS-satisfied Prediction Model for Cloud-service Composition Based on Hidden Markov Model

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