Khadija Bousselmi

Khadija Bousselmi
Université Savoie Mont Blanc | UdS · IUT d' Annecy

PhD in Computer Sciences
Enseignante-chercheuse au sein du laboratoire LISTIC, domaine de Représentation, Gestion et tRaitement des Données

About

9
Publications
3,435
Reads
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61
Citations
Introduction
Khadija Bousselmi currently works as lecturer at the University of Haute Savoie Mont Blanc in France and a permanent researcher at the LISTIC research laboratory. She got her PHD on July 2017 from the Department of Informatics at the University of Tunis El Manar, Tunisia. Khadija does research in Parallel Computing, Distributed Computing and Computer Communications (Networks).
Additional affiliations
September 2020 - present
Université Savoie Mont Blanc
Position
  • Maitre de conférence
September 2018 - August 2020
University Paris Nanterre
Position
  • Research Assistant
September 2016 - August 2017
ENSUP
Position
  • Research Assistant
Education
September 2013 - July 2017
University of Tunis El Manar
Field of study
  • PHD in computer science

Publications

Publications (9)
Conference Paper
Full-text available
Scientific workflows are used to model scalable, portable, and reproducible big data analyses and scientific experiments with low development costs. To optimize their performances and ensure data resources efficiency, scientific workflows handling big volumes of data need to be executed on scalable distributed environments like the Cloud infrastruc...
Article
Full-text available
Workflows management systems (WfMS) are aimed for designing, scheduling, executing, reusing, and sharing workflows in distributed environments like the Cloud computing. With the emergence of e-science workflows, which are used in different domains like astronomy, life science, and physics, to model and execute vast series of dependents functionalit...
Presentation
Full-text available
SCC 2016, June 28 - July 2, San Francisco, California, USA
Conference Paper
Energy consumption is emerging as a new crucial issue of the Cloud Computing environments such as data centers.The problem of power consumption is more challenging especially in the context of Scientific workflows deployment in the Cloud as they trigger intensive computational tasks and data manipulation steps which begets excessive data movement o...
Presentation
Full-text available
In this work, we propose a QoSaware algorithm for Scientific Workflows scheduling that aims to improve the overall quality of service (QoS) by considering the metrics of execution time, data transmission time, cost, resources availability and data placement constraints. We extended the Parallel Cat Swarm Optimization (PCSO) algorithm to implement o...
Conference Paper
Full-text available
Cloud Computing has emerged as a service model that enables on-demand network access to a large number of available virtualized resources and applications with a minimal management effort and a minor price. The spread of Cloud Computing technologies allowed dealing with complex applications such as Scientific Workflows, which consists of a set of i...
Presentation
Full-text available
Cloud Computing is emerging today as a service model used to relocate locally-based data and applications to virtualized services available via Internet at a lower cost. A key to exploit the benefits of this model is orchestration which consists in coordinating effectively the deployment of a set of virtualized services in order to fulfill operatio...
Conference Paper
Full-text available
Cloud Computing is emerging today as a service model used to relocate locally-based data and applications to virtualized services available via Internet at a lower cost. A key to exploit the benefits of this model is orchestration which consists in coordinating effectively the deployment of a set of virtualized services in order to fulfill operationa...

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Cited By

Projects

Projects (2)
Project
Improving Evolutionary Algorithms performance when solving hard combinatorial problems, specialy ordering problems such VRP and its variants and scheduling problems.
Project
Scheduling of data-intensive Workflows in Cloud Computing Cloud Computing has emerged as a service model that enables on-demand network access to a large number of available virtualized resources and applications with a minimal management effort and a minor price. The spread of Cloud Computing technologies allowed dealing with complex applications such as Scientific Workflows, which consists of a set of computational operations and data manipulation steps. Cloud Computing helps such Workflows to dynamically provision compute and storage resources necessary for the execution of its tasks thanks to the elasticity asset of these resources. However, the dynamic nature of the Cloud can incur new challenges, as some allocated resources may be overloaded or out of access during the execution of the Workflow. Moreover, for data intensive tasks, the allocation strategy should consider the data placement constraints since data transmission time could increase notably in this case, which implicates the increase of the overall execution time and cost of the Workflow. Yet, a critical challenge is how to efficiently schedule Workflow tasks on Cloud resources to optimize its quality of service. In this work, we propose a QoS-aware algorithm for Scientific Workflows scheduling that aims to improve the overall quality of service (QoS) by considering the metrics of execution time, data transmission time, cost, resources availability and data placement constraints. your goal : i) using Parallel Cat Swarm Optimization (Parallel CSO) algorithm tto propose a solution for Scientific Workflows. ii) propose an orchestration engine based on the temporal reconfigurable computing.