Cloud Computing CPU Allocation and Scheduling Algorithms using CloudSim Simulator

In this paper, we describe the Cloud Computing basic compute resources scheduling and allocation algorithms, in addition to the working mechanism. This paper also presents a number of experiments conducted based on CloudSim simulation toolkit in order to assess and evaluate the performance of these scheduling algorithms on Cloud Computing like infrastructure. Furthermore, we introduced and explained the CloudSim simulator design, architecture and proposed two new scheduling algorithms to enhance the existent ones and highlight the weaknesses and/or effectiveness of these algorithms

Comparative Study of Neural Networks Algorithms for Cloud Computing CPU Scheduling

Cloud Computing is the most powerful computing model of our time. While the major IT providers and consumers are competing to exploit the benefits of this computing model in order to thrive their profits, most of the cloud computing platforms are still built on operating systems that uses basic CPU (Core Processing Unit) scheduling algorithms that lacks the intelligence needed for such innovative computing model. Correspdondingly, this paper presents the benefits of applying Artificial Neural Networks algorithms in regards to enhancing CPU scheduling for Cloud Computing model. Furthermore, a set of characteristics and theoretical metrics are proposed for the sake of comparing the different Artificial Neural Networks algorithms and finding the most accurate algorithm for Cloud Computing CPU Scheduling

Algorithme de planification intelligent Round Robin pour le Cloud Computing et Big Data

International audienceCloud Computing and Big Data are the upcoming Information Technology (IT) computing models. These groundbreaking paradigms are leading IT to a new set of rules that aims to change computing resources delivery and exploitation model, thus creating a novel business market that is exponentially growing and attracting more and more investments from both providers and end users that are looking forward to make profits from these innovative models of computing. In the same context, researchers and investigators are wrestling time in order to develop, test and optimize Cloud Computing and Big Data platforms, whereas several studies are ongoing to determine and enhance the essential aspects of these computing models especially compute resources allocation. The processing power scheduling is crucial when it comes to Cloud Computing and Big Data because of the data growth management and delivery design proposed by these new computing models, that requires faster responses from platforms and applications. Hence originates the importance of developing high efficient scheduling algorithms that are compliant with these computing models platforms and infrastructures requirement.Cloud Computing et Big Data sont les prochains modèles informatiques. Ces paradigmes révolutionnaires conduisent l'informatique à un nouveau jeu de règles qui vise à changer la livraison des ressources informatiques et le modèle d'exploitation, créant ainsi un monde d'affaires nouveau qui croît de façon exponentielle et attire de plus en plus d'investissements des fournisseurs et des utilisateurs finaux qui attendent Amener profit de ces modèles innovants de l'informatique. Dans le même contexte, les chercheurs combattent pour développer, tester et optimiser les plates-formes Cloud Computing et Big Data, alors que plusieurs études sont en cours pour déterminer et améliorer les aspects essentiels de ces modèles informatiques, en particulier l'allocation des ressources. La planification de la puissance de traitement est cruciale quand il s'agit de Cloud Computing et Big Data en raison de la gestion de la croissance des données et la conception de livraison proposée par ces nouveaux modèles informatiques, qui nécessite des réponses plus rapides des plates-formes et des applications. D'où l'origine de l'importance de développer des algorithmes d'ordonnancement efficaces qui sont conformes à ces plates-formes de modèles informatiques et aux exigences d'infrastructure

Transforming Healthcare: Leveraging Vision-Based Neural Networks for Smart Home Patient Monitoring

Image captioning is a promising technique for remote monitoring of patient behavior, enabling healthcare providers to identify changes in patient routines and conditions. In this study, we explore the use of transformer neural networks for image caption generation from surveillance camera footage, captured at regular intervals of one minute. Our goal is to develop and evaluate a transformer neural network model, trained and tested on the COCO (common objects in context) dataset, for generating captions that describe patient behavior. Furthermore, we will compare our proposed approach with a traditional convolutional neural network (CNN) method to highlight the prominence of our proposed approach. Our findings demonstrate the potential of transformer neural networks in generating natural language descriptions of patient behavior, which can provide valuable insights for healthcare providers. The use of such technology can allow for more efficient monitoring of patients, enabling timely interventions when necessary. Moreover, our study highlights the potential of transformer neural networks in identifying patterns and trends in patient behavior over time, which can aid in developing personalized healthcare plans