A cloud service selection model using improved ranked voting method

Baranwal, Gaurav; Vidyarthi, Deo Prakash

doi:10.1002/cpe.3740

Cited by 28 publications

(14 citation statements)

References 28 publications

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“…Metric-based solutions rely on representing cloud resources and their capabilities in a certain way, such as using standardized KPIs (e.g. [27], [28], [29], [30]) or through benchmarking (e.g. [31], [32]).…”

Section: Related Workmentioning

confidence: 99%

Transferable Knowledge for Low-Cost Decision Making in Cloud Environments

Samreen

Blair

Elkhatib

2022

IEEE Trans. Cloud Comput.

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Users of Infrastructure as a Service (IaaS) are increasingly overwhelmed with the wide range of providers and services offered by each provider. As such, many users select services based on description alone. An emerging alternative is to use a decision support system (DSS), which typically relies on gaining insights from observational data in order to assist a customer in making decisions regarding optimal deployment of cloud applications. The primary activity of such systems is the generation of a prediction model (e.g. using machine learning), which requires a significantly large amount of training data. However, considering the varying architectures of applications, cloud providers, and cloud offerings, this activity is not sustainable as it incurs additional time and cost to collect data to train the models. We overcome this through developing a Transfer Learning (TL) approach where knowledge (in the form of a prediction model and associated data set) gained from running an application on a particular IaaS is transferred in order to substantially reduce the overhead of building new models for the performance of new applications and/or cloud infrastructures. In this paper, we present our approach and evaluate it through extensive experimentation involving three real world applications over two major public cloud providers, namely Amazon and Google. Our evaluation shows that our novel two-mode TL scheme increases overall efficiency with a factor of 60% reduction in the time and cost of generating a new prediction model. We test this under a number of cross-application and cross-cloud scenarios.

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Section: Related Workmentioning

confidence: 99%

Transferable Knowledge for Low-Cost Decision Making in Cloud Environments

Samreen

Blair

Elkhatib

2022

IEEE Trans. Cloud Comput.

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“…It also helps in understanding how the management of resources can enhance service selection, optimization and simplification. Baranwal et al in [3] proposed a cloud service selection model using ranked voting. In this method a normalized preference score is calculated for each cloud provider.…”

Section: Analysis Of Academic Workmentioning

confidence: 99%

“…These attributes can be subjective and different users and service providers may have different interpretation and expectation of the quality of cloud services. This problem is further complicated as there is no standard benchmark for measuring the quality of service of cloud services [3].…”

Section: Introductionmentioning

confidence: 99%

Trends and Directions in Cloud Service Selection

Eisa

Younas

Basu

et al. 2016

2016 IEEE Symposium on Service-Oriented System Engineering (SOSE)

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With the growing popularity of cloud computing the number of cloud service providers and services have significantly increased. Thus selecting the best cloud services becomes a challenging task for prospective cloud users. The process of selecting cloud services involves various factors such as characteristics and models of cloud services, user requirements and knowledge, and service level agreement (SLA), to name a few. This paper investigates into the cloud service selection tools, techniques and models by taking into account the distinguishing characteristics of cloud services. It also reviews and analyses academic research as well as commercial tools in order to identify their strengths and weaknesses in the cloud services selection process. It proposes a framework in order to improve the cloud service selection by taking into account services capabilities, quality attributes, level of user's knowledge and service level agreements. The paper also envisions various directions for future research.

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“…This work's focus is on the migration of the whole company ICT to cloud based on a set of general cloud service features. Baranwal et al [18] propose to use Improved Ranked Voting Method (IRVM). In this method, the services are compared and ranked first based on each metric individually and then the services are scored based on the preference of the customer and the ranking in the previous step.…”

Section: Related Workmentioning

confidence: 99%

“…Studies show that deploying cloud applications on federated clouds [18], can bring several benefits including cost effectiveness, scalability, fault tolerance, and reliability [26][27][28]. We therefore perform an initial search for the most similar service for the entire application from the available services of all providers, given that there is no customer preference for specific providers.…”

Section: Avg Wmentioning

confidence: 99%

A hybrid approach to automatic IaaS service selection

2018

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Cloud computing provides on-demand resources and removes the boundaries of resources' physical locations. By providing virtualized computing resources in an elastic manner over the internet, IaaS providers allow organizations to save upfront infrastructure costs and focus on features that discriminate their businesses. The growing number of providers makes manual selection of the most suitable configuration of IaaS resources, or IaaS services, difficult and time consuming while requiring a high level of expertise. In our previous paper we proposed QuARAM recommender, a general platform for automatic IaaS service selection. In this paper, we present in detail the hybrid approach to automatic service selection used in our platform. The selection process begins with automatic extraction of an application's features, requirements and preferences, which are then used to produce a list of potential services for the application's deployment. We use case-based reasoning and MCDM (Multi-criteria Decision Making) to provide a recommendation of suitable services for application deployment, clustering to handle the problem of a large search space and a service consolidation method to improve the resource utilization and decrease the total service price. We carry out a case study with a prototype implementation of our platform to demonstrate that automatic IaaS service selection using a combination of all the proposed approaches is both practical and achievable.

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A cloud service selection model using improved ranked voting method

Cited by 28 publications

References 28 publications

Transferable Knowledge for Low-Cost Decision Making in Cloud Environments

Transferable Knowledge for Low-Cost Decision Making in Cloud Environments

Trends and Directions in Cloud Service Selection

A hybrid approach to automatic IaaS service selection

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