Enhancing cloud of things performance by avoiding unnecessary data through artificial intelligence tools

Mahfoudhi, Sami; Frehat, Musheera; Moulahi, Tarek

doi:10.1109/iwcmc.2019.8766790

Cited by 10 publications

(5 citation statements)

References 16 publications

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“…The ever-increasing demand for cloud ser-vices and the desire to provide a certain quality of services require providers to invest large amounts of capital in order to multiply their hosting offers in several geographical areas. With this large-scale deployment of huge data centers, the energy consumption of the cloud increases accordingly [3,4]. From a 2011 Digital Power Group report, scientist said that if the cloud were a country, it would be the world's 5th largest electricity consumer, about 750 trillion kWh, all in just one year.…”

Section: Introductionmentioning

confidence: 99%

Cloud-IoT Resource Management Based on Artificial Intelligence for Energy Reduction

Daoud

Mchergui

Moulahi

et al. 2022

Wireless Communications and Mobile Computing

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The rapid growth in demand for cloud services has led to the creation of large-scale data centers, which allows application service providers to lease data center capacity for application deployment as per user requirement in terms of quality of services (QoS). These data centers consume a lot of electrical power which contributes to increased operating costs and carbon dioxide emissions. In addition, modern cloud computing environments must provide QoS for their customers which leads them to a need to make a power-performance compromise that is to say in terms of energy consumption and service-level agreement (SLA) compliance. That is why, we introduce, in this paper, an intelligent resource management policy for cloud data centers. The goal is to dynamically allocate and continuously consolidate virtual machines taking advantage of live migration and disengage inactive nodes to minimize power feeding in this cloud environment while maintaining the quality of service. We integrate some artificial intelligence concepts to ensure a dynamic resource management and a better power-performance compromise and significantly reduce the consumed energy.

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Section: Introductionmentioning

confidence: 99%

Cloud-IoT Resource Management Based on Artificial Intelligence for Energy Reduction

Daoud

Mchergui

Moulahi

et al. 2022

Wireless Communications and Mobile Computing

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“…Since cloud computing offers excellent data management and storage potential, many researchers resort to using it in order to manage the enormous amount of data that things collect 15 . Nonetheless, the use of cloud computing for stuff faces some challenges, such as reducing energy consumption, providing the best quality of service, protecting and promoting data access, allowing efficient use of data, and avoiding unnecessary data 16,17 …”

Section: Overviewmentioning

confidence: 99%

“…15 Nonetheless, the use of cloud computing for stuff faces some challenges, such as reducing energy consumption, providing the best quality of service, protecting and promoting data access, allowing efficient use of data, and avoiding unnecessary data. 16,17 In this article, we focus on the usage of AI techniques for reducing and isolating redundant and useless data. This can thus help to avoid irrelevant loading of storage and processing resources of items as well as the the use of the cloud.…”

Section: Contributionmentioning

confidence: 99%

A fog computing data reduce level to enhance the cloud of things performance

Moulahi

Khediri

Khan

et al. 2021

Int J Communication

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Summary Devices in the Internet of Things (IoT) generate and gather a vast amount of data. Smart devices with constrained resources cannot execute large data‐based machine learning (ML) algorithms. Therefore, in this paper, we propose, analyze, and evaluate data reduction at the Fog level. As a matter of fact, the focus of this paper is twofold: The first objective is to process the reduced datasets by ML, and the second aim is to segregate the irrelevant data and preserve the quality of the ML models. The naïve Bayesian classifier is used for model analysis. For data (attributes) reduction, the state‐of‐the‐art approaches of CF Subset Evaluation, Info Gain Evaluation, Gain Ratio Attribute Evaluation, and the principal component analysis (PCA) approaches are employed. From the implementation point of view, the naïve Bayesian classifier is used to learn the class distribution and the correlation of the classes with the rest of the features. The naïve Bayesian model is generated using the full dataset without feature reduction. Feature selection algorithms then reduce features by 50%. This reduced set is also used to generate the naïve Bayesian model, which gives us a benchmark for comparing the results with the original unreduced dataset. From the results, we find that the performance of the reduced set is either increased or is almost similar to the full set of features. With this comprehensive experimental evaluation, we believe that data reduction can provide a blueprint for avoiding unnecessary data storage and processing. The performance of the ML models is not drastically affected by feature reduction and hence demonstrates the efficacy of the reduced ML models.

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“…Machine learning methodologies, such as support vector machines (SVM), artificial neural networks (ANN), random forest (RF), and principal component analysis (PCA) [4], cannot be used for processing and decision-making if the data are incomplete. Hence, data recovery is of high importance to facilitate the right outcomes.…”

Section: Introductionmentioning

confidence: 99%

Missing Data Recovery in the E-health Context Based on Machine Learning Models

Rahmany¹,

Mahfoudhi²,

Freihat³

et al. 2022

AAIML

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Diabetes mellitus is a set of metabolic illnesses characterized by abnormally high blood sugar levels. In 2017, 8.8% of the world’s population had diabetes. By 2045, it is expected that this percentage will have risen to approximately 10%. Missing data, a prevalent problem even in a well-designed and controlled study, can have a major impact on the conclusions that can be derived from the available data. Missing data may decrease a study’s statistical validity and lead to erroneous results due to distorted estimations. In this study, we hypothesize that (a) replacing missing values using machine learning techniques rather than the mean value and group mean value and (b) using SVM kernel RBF classifier will result in the highest level of accuracy in comparison to traditional techniques such as DT, RF, NB, SVM, AdaBoost, and ANN. The classification results improved significantly when using regression to replace the missing values over the group median or the mean. This is a 10% improvement over previously developed strategies that have been reported in the literature.

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Enhancing cloud of things performance by avoiding unnecessary data through artificial intelligence tools

Cited by 10 publications

References 16 publications

Cloud-IoT Resource Management Based on Artificial Intelligence for Energy Reduction

Cloud-IoT Resource Management Based on Artificial Intelligence for Energy Reduction

A fog computing data reduce level to enhance the cloud of things performance

Missing Data Recovery in the E-health Context Based on Machine Learning Models

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