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

MetOp Satellites Data Processing for Air Pollution Monitoring in Morocco

This paper presents a data processing system based on an architecture comprised of multiple stacked layers of computational processes that transforms Raw Binary Pollution Data coming directly from Two EUMETSAT MetOp satellites to our servers, into ready to interpret and visualise continuous data stream in near real time using techniques varying from task automation, data preprocessing and data analysis to machine learning using feedforward artificial neural networks. The proposed system handles the acquisition, cleaning, processing, normalizing, and predicting of Pollution Data in our area of interest of Morocco

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

SAT-CEP-monitor: An air quality monitoring software architecture combining complex event processing with satellite remote sensing

La contaminación del aire es un problema importante hoy en día que causa graves daños a la salud humana. Las áreas urbanas son las más afectadas por la degradación de la calidad del aire causada por las emisiones de gases antropogénicos. Aunque existen múltiples propuestas para el monitoreo de la calidad del aire, en la mayoría de los casos, se imponen dos limitaciones: la imposibilidad de procesar datos en tiempo casi real (NRT) para enfoques de teledetección y la imposibilidad de llegar a áreas de acceso limitado o baja cobertura de red para enfoques de datos terrestres. Proponemos una arquitectura de software que combina eficientemente el procesamiento de eventos complejos con datos de teledetección de varios sensores satelitales para monitorear la calidad del aire en NRT, brindando apoyo a los tomadores de decisiones. Ilustramos la solución propuesta calculando los niveles de calidad del aire para varias áreas de Marruecos y España, extrayendo y procesando información satelital en NRT. Este estudio también valida la calidad del aire medida por estaciones terrestres y datos de sensores satelitales.Air pollution is a major problem today that causes serious damage to human health. Urban areas are the most affected by the degradation of air quality caused by anthropogenic gas emissions. Although there are multiple proposals for air quality monitoring, in most cases, two limitations are imposed: the impossibility of processing data in Near Real-Time (NRT) for remote sensing approaches and the impossibility of reaching areas of limited accessibility or low network coverage for ground data approaches. We propose a software architecture that efficiently combines complex event processing with remote sensing data from various satellite sensors to monitor air quality in NRT, giving support to decision-makers. We illustrate the proposed solution by calculating the air quality levels for several areas of Morocco and Spain, extracting and processing satellite information in NRT. This study also validates the air quality measured by ground stations and satellite sensor data.This work was partially supported by the Spanish Ministry of Science and Innovation and the European Regional Development Fund (ERDF) under project FAME [RTI2018-093608-B-C33]. The corresponding author thanks the ERASMUS+ KA107 program for the grant and acknowledges the University of Cadiz for the academic supervision and their research facilities, grant number: 2017-1-ES01- KA107-037422 and 2018-1-ES01-KA107-049705. The authors of this work are also thankful to the Andalusian and Madrid regional governments for providing us with the NRT MGS data

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

Smarter Round Robin Scheduling Algorithm for Cloud Computing and Big Data

Cloud 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

Big data and remote sensing: A new software of ingestion

Currently, remote sensing is widely used in environmental monitoring applications, mostly air quality mapping and climate change supervision. However, satellite sensors occur massive volumes of data in near-real-time, stored in multiple formats and are provided with high velocity and variety. Besides, the processing of satellite big data is challenging. Thus, this study aims to approve that satellite data are big data and proposes a new big data architecture for satellite data processing. The developed software is enabling an efficient remote sensing big data ingestion and preprocessing. As a result, the experiment results show that 86 percent of the unnecessary daily files are discarded with a data cleansing of 20 percent of the erroneous and inaccurate plots. The final output is integrated into the Hadoop system, especially the HDFS, HBase, and Hive, for extra calculation and processing.Peer ReviewedPostprint (published version