Aggregating Node Level Risk Assessment in Grids Using an R-out-of-N Model

Sangrasi, Asif; Djemame, Karim; Jokhio, Imran Ali

doi:10.1007/978-3-642-28962-0_42

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…They take into consideration the failures that affect the whole system. Alsoghayer and Djemame [10] extended the model proposed by Alsoghayer and Djemame [12], and they introduced RA aggregation model build at the node level based on R-out-of-N model. The proposed model provides the risk estimates for any number of chosen nodes and estimates the risk for those failures.…”

Section: Related Workmentioning

confidence: 99%

“…The main drawback of the presented methodologies is that they do not include the human factor as a risk factor [8]. RA in grid computing has been addressed by many researchers [1,[9][10][11]. Although a significant number of researchers have proposed RA methods, the risk information in grid computing is limited, due to the dependability of RA efforts on the node or machine level [9].…”

Section: Related Workmentioning

confidence: 99%

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Risk Assessment for Grid Computing Using Meta-Learning Ensembles

Abdelwahab

Abraham

2015

Pattern Analysis, Intelligent Security and the Internet of Things

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Assessing risk associated with computational grid is an essential need for both the resource providers and the users who runs applications in grid environments. In this chapter, we modeled the prediction process of risk assessment (RA) in grid computing utilizing meta-learning approaches in order to improve the performance of the individual predictive models. In this chapter, four algorithms were selected as base classifiers, namely isotonic regression, instance base knowledge (IBK), randomizable filtered classified tree, and extra tree. Two meta-schemes, known as voting and multi schemes, were adopted to perform an ensemble risk prediction model in order to have better performance. The combination of prediction models was compared based on root mean-squared error (RMSE) to find out the best suitable algorithm. The performance of the prediction models is measured using percentage split. Experiments and assessments of these methods are performed using nine datasets for grid computing risk factors. Empirical results illustrate that the prediction performance is enhanced by predictive model using ensemble methods.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Risk Assessment for Grid Computing Using Meta-Learning Ensembles

Abdelwahab

Abraham

2015

Pattern Analysis, Intelligent Security and the Internet of Things

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A Brokering Framework for Assessing Legal Risks in Big Data and the Cloud

Compagnucci

Djemame

2017

New Technology, Big Data and the Law

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Cloud computing" and "Big Data" are amongst the most hyped-up terms and buzzwords of the moment. After decades in which individuals and companies used to host their data and applications using their own IT infrastructure, the world has seen the stunning transformation of the Internet. Major shifts occurred when these infrastructures began to be outsourced to public Cloud providers to match commercial expectations. Storing, sharing and transferring data and databases over the Internet is convenient, yet legal risks cannot be 1 eliminated. 1 Legal risk is a fast growing area of research and covers various aspects of law. Current studies and research on Cloud computing legal risk assessment have been, however, limited in scope and focused mainly on security and privacy aspects. 2 There is little systematic research on the risks, threats and impact of the legal issues inherent to database rights 3 and "ownership" rights of data. Database rights seem to be outdated and there is a significant gap in the scientific literature when it comes to the understanding of how to apply its provisions in the Big Data era. This means that we need a whole new framework for understanding, protecting and sharing data in the Cloud. The scheme we propose in this chapter is based on a risk assessment brokering framework that works side by side with service level agreements (SLAs). This proposed framework will provide better control for Cloud users and will go a long way to increase confidence and reinforce trust in Cloud computing transactions.

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Towards a Legal Risk Assessment

Compagnucci

2019

Big Data, Databases and "Ownership" Rights in the Cloud

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Aggregating Node Level Risk Assessment in Grids Using an R-out-of-N Model

Cited by 4 publications

References 17 publications

Risk Assessment for Grid Computing Using Meta-Learning Ensembles

Risk Assessment for Grid Computing Using Meta-Learning Ensembles

A Brokering Framework for Assessing Legal Risks in Big Data and the Cloud

Towards a Legal Risk Assessment

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