Conference Presentations by Sofiane Benabbes
ASD 2018: Big data & Applications 12th edition of the Conference on Advances of Decisional Systems, 2018
CloudIoT est un nouveau paradigme qui, a émergé suite à l'intégra-tion du Cloud Computing et l'In... more CloudIoT est un nouveau paradigme qui, a émergé suite à l'intégra-tion du Cloud Computing et l'Internet des Objets. Dans le CloudIoT, les cap-teurs physiques sont chargés de détecter et de transmettre les données vers le Cloud afin qu’elles soient traitées et stockées. La quantité de données à traiter augmente de jour en jour ce qui nécessite un mécanisme d'équilibrage de charge afin de répartir les données capturées entre les différentes ressources du CloudIoT.
Dans ce papier, nous proposons une architecture pour équilibrer la charge entre les machines virtuelles (MVs) d’un CloudIot. Cette architecture se compose de quatre composants essentiels: le Classiffieur, le routeur, l’équilibreur local et l’équilibreur général. Ce modèle se base sur le pire temps d’exécution et la taille d’une tâche, et sur le centre de gravité des classes. Les résultats obtenus à travers l’étude de cas montrent que notre modèle permet de réguler la charge entre les MVs.
Papers by Sofiane Benabbes
Computing and Informatics, 2023
CloudIoT is a new paradigm, which has emerged as a result of the combination of Cloud Computing (... more CloudIoT is a new paradigm, which has emerged as a result of the combination of Cloud Computing (CC) and the Internet of Things (IoT). It has experienced a growing and rapid development, and it has become more popular in information and technology (IT) environments because of the advantages it offers. However, due to a strong use of this paradigm, especially in smart cities, the problem of imbalance load has emerged. Indeed, to satisfy the needs of the user, the intelligent objects send the collected data to the virtual machines (VMs) of the cloud in order to be processed. So, it is necessary to have an idea about the load of its VM. Thus, the problem of load balancing between VMs is strongly related to the technique used for the VMs selection. To tackle this problem, we propose in this paper a task scheduler called Scheduler Genetic Grasshopper Algorithm (SGGA). It allows to ensure a dynamic load balancing, as well as the optimization of the makespan and the resource usage. Our proposed SGGA is based on the combination of Genetic Algorithm (GA) and Grasshopper Optimization Algorithm (GOA). First, the tasks sent by the IoTs are mapped to the VMs in order to build the initial population, then SGGA performs the genetic algorithm, which has expressed a considerable performance. However, the weakness of the GA is marked by its heaviness caused by the mutation operator, especially when the number of tasks increases. Because of this insufficiency, we have replaced the mutation operator with the grasshopper optimization algorithm. The results of the experiments show that our approach (SGGA) is the most efficient, compared to the recent approaches, in terms of the response time to obtain the optimal solution, makespan, throughput, an average resource utilization rate and the hypervolume indicator.
Lecture Notes in Networks and Systems
CloudIoT is a new paradigm that has emerged as a result of the integration of Cloud Computing and... more CloudIoT is a new paradigm that has emerged as a result of the integration of Cloud Computing and the Internet of Things. It provides a set of intelligent services and applications that can strongly influence on our daily lives. In the CloudIoT, physical sensors are responsible for detecting and transmitting data to the cloud in order to be treated and stored. In this context, the amount of data to be processed through the CloudIoT is in increasing usually and, in this situation, the load balancing mechanism is needed in order to distribute the captured data between the different CloudIoT resources. In this paper, we propose an approach that allows to balance the load between the different virtual machines of a CloudIoT. The proposed approach is composed of four essential components: The Classifier which assigns Tasks to its corresponding class and assigns the Virtual Machines (VM) to its corresponding class too, the Dispatcher which sends the tasks to the VM classes, the Local Balancer whose role is to balance the tasks between different VMs belonging to the same class used the spooling method for duplicate same VM, finally, the Manager component or general balancer that is responsible to balance the load between VMs classes with the test of the maximum cloud allowance bar to re-lease VM. The results obtained through the proposed the case study show that our approach allows effectively the load balancing between VM.
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Conference Presentations by Sofiane Benabbes
Dans ce papier, nous proposons une architecture pour équilibrer la charge entre les machines virtuelles (MVs) d’un CloudIot. Cette architecture se compose de quatre composants essentiels: le Classiffieur, le routeur, l’équilibreur local et l’équilibreur général. Ce modèle se base sur le pire temps d’exécution et la taille d’une tâche, et sur le centre de gravité des classes. Les résultats obtenus à travers l’étude de cas montrent que notre modèle permet de réguler la charge entre les MVs.
Papers by Sofiane Benabbes
Dans ce papier, nous proposons une architecture pour équilibrer la charge entre les machines virtuelles (MVs) d’un CloudIot. Cette architecture se compose de quatre composants essentiels: le Classiffieur, le routeur, l’équilibreur local et l’équilibreur général. Ce modèle se base sur le pire temps d’exécution et la taille d’une tâche, et sur le centre de gravité des classes. Les résultats obtenus à travers l’étude de cas montrent que notre modèle permet de réguler la charge entre les MVs.