Resource failures risk assessment modelling in distributed environments

Alsoghayer, Raid; Djemame, Karim

doi:10.1016/j.jss.2013.09.017

Cited by 10 publications

(9 citation statements)

References 20 publications

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“…SLA violation [33], [34] SLA violation handling approach using incremental time impact analysis -Time inconsistency and unsatisfactory condition. -Change of service due to the service replacement.…”

Section: Fault Fault Tolerant Approach Fault Assumption/cause Remarksmentioning

confidence: 99%

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Extended Fault Taxonomy of SOA-Based Systems

Bhandari

Gupta

2018

CIT

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Section: Fault Fault Tolerant Approach Fault Assumption/cause Remarksmentioning

confidence: 99%

“…Whenever an SLA violation occurs to a service it can impact dependent services. Ismail et al [33], [34] have proposed SLA violation handling approach using incremental time impact analysis. Their approach can efficiently recover and handle the violation in relation to the strategy of minimizing the number of service changes.…”

Section: Binding Faultmentioning

confidence: 99%

Extended Fault Taxonomy of SOA-Based Systems

Bhandari

Gupta

2018

CIT

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“…There have been several efforts for quantifying the reliability of resources in distributed systems, like in [36,25,17,3]. These efforts are mainly based on architectural considerations of distributed systems.…”

Section: Related Workmentioning

confidence: 99%

Ranking Grid-sites based on their Reliability for Successfully Executing Jobs of Given Durations

Nadeem

2015

IJCNIS

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Abstract-Today's Grids include resources (referred as Grid-site s) from different domains including dedicated production resources, resources from university labs, and even P2P environments. Grid high level services, like schedulers, resource managers, etc. need to know the reliability of the available Grid-sites to select the most suitable from them. Modeling reliability of a Grid-site for successful execution of a job requires prediction of Grid-site availability for the given duration of job execution as well as possibility of successful execution of the job. Predicting Grid-site availability is complex due to different availability patterns, resource sharing policies implemented by resource owners, nature of domain the resource belongs to (e.g. P2P etc.), and its maintenance etc. To give a solution, we model reliability of Grid-site in terms of prediction of its availability and possibility of job success. Our availability predictions incorporate past patterns of the Grid-site availability using pattern recognition methods. To estimate possibility of job success, we consider historical traces of job execution. The experiments conducted on a trace of real Grid demonstrate the effectiveness of our approach for ranking Grid-sites based on their reliability for executing jobs successfully.

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“…However the main drawback of the provided probability models that highlighted in the literature is that, all provided models are built on unrealistic assumption that the resource failure represents poisson process [13]. Alsoghayer and Djemame [13] proposed a mathematical model, by using historical and discrete time analytical model (Markov model), to predict the risk of resource failure in grid environment. However, most of proposed methods [9,13] do not address the key issue of security risk that threatens the grid environment.…”

Section: Related Workmentioning

confidence: 99%

“…Alsoghayer and Djemame [13] proposed a mathematical model, by using historical and discrete time analytical model (Markov model), to predict the risk of resource failure in grid environment. However, most of proposed methods [9,13] do not address the key issue of security risk that threatens the grid environment. A significant amount of the literature on grid computing addresses the problem of RA by providing hybrid model [14].…”

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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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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Resource failures risk assessment modelling in distributed environments

Cited by 10 publications

References 20 publications

Extended Fault Taxonomy of SOA-Based Systems

Extended Fault Taxonomy of SOA-Based Systems

Ranking Grid-sites based on their Reliability for Successfully Executing Jobs of Given Durations

Risk Assessment for Grid Computing Using Meta-Learning Ensembles

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