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Start Over Topic cloud computing Topic computer science
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1. Introduction to Deep Learning

Author

David Paper

Subjects
ComputingMilieux_GENERAL, World Wide Web, business.industry, Computer science, Deep learning, Cloud computing, Artificial intelligence, Python (programming language), business, computer, computer.programming_language, Coding (social sciences)
Abstract

We introduce the basic concepts of deep learning. We use TensorFlow 2.x, the Google cloud service, and Google Drive interaction to make the concepts come alive with Python coding examples.

Published
2021

2. Build Your First Neural Network with Google Colab

Author

David Paper

Subjects
World Wide Web, Work (electrical), Artificial neural network, business.industry, Computer science, Deep learning, Cloud computing, Artificial intelligence, Python (programming language), business, computer, computer.programming_language
Abstract

We work through a complete deep learning example with Python’s TensorFlow 2.x library in the Google Colab cloud service. We also demonstrate how to link your Google Drive with the Colab cloud service.

Published
2021

3. An Efficient Approach for Multi-tenant Elastic Business Processes Management in Cloud Computing Environment

Author

François Charoy, Guillaume Rosinosky, Samir Youcef, Bonitasoft, Web Scale Trustworthy Collaborative Service Systems (COAST), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Networks, Systems and Services (LORIA - NSS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), CIFRE Bonitasoft, Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), and The authors would like to thank Gurobi for the usage of their optimizer, and Amazon Web Services for the EC2 instances credits (this paper is supported by an AWS in Education Research Grant award).

Subjects
Computer science, Business process, 0211 other engineering and technologies, Cloud computing, Multi-tenancy, [INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE], 02 engineering and technology, [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI], Utility computing, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, ACM: C.: Computer Systems Organization/C.2: COMPUTER-COMMUNICATION NETWORKS/C.2.4: Distributed Systems/C.2.4.1: Distributed applications, Elasticity (economics), Multitenancy, 021103 operations research, Management science, business.industry, Bi-criteria optimization, Quality of service, [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO], [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Elasticity, [INFO.INFO-PF]Computer Science [cs]/Performance [cs.PF], BPM, Risk analysis (engineering), [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], business, Cloud
Abstract

International audience; Even though the proven benefits of cloud computing paradigm, it must face a serious problem that can compromise its commercial success. It concerns the lack of efficient approach for using optimally the available resources. For this, several approaches have been proposed. However,they suffer from several shortcomings. For instance, often only one objective is taken into account expressing all operations in terms of cost. Furthermore, business processes should be insured with elasticity and multitenancy mechanism while adjusting the available resources to the dynamic load distribution. The proposed approach aims to optimize two conflicting objectives, namely the number of migrated tenants and the cost incurred using a set of resources. It allows to take into account the multi-tenancy property and the Cloud computing elasticity, and is efficient as shown by an extensive experimentation based on real data from Bonita BPM customers.

Published
2016

4. Cost Reduction Through SLA-driven Self-Management

Author

Jean-Louis Pazat, Nikos Parlavantzas, André Lage Freitas, Design and Implementation of Autonomous Distributed Systems (MYRIADS), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SYSTÈMES LARGE ÉCHELLE (IRISA-D1), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), The research leading to these results has received funding from the European Community's Seventh Framework Programme [FP7/2007-2013] under grant greement 215483 (S-CUBE). Experiments presented in this paper were carried out using the Grid'5000 experimental testbed, being developed under the INRIA ALADDIN development action with support from CNRS, RENATER and several Universities as well as other funding bodies (see https://www.grid5000.fr)., European Project: 215483,EC:FP7:ICT,FP7-ICT-2007-1,S-CUBE(2008), Inria, GRID'5000, CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)

Subjects
Computer science, computer.internet_protocol, Distributed computing, QoS, Cloud computing, 02 engineering and technology, computer.software_genre, grid, self-sadptation, Profit (economics), 020204 information systems, self-adaptation, 0202 electrical engineering, electronic engineering, information engineering, cloud, service-oriented computing, business.industry, Quality of service, Service-oriented architecture, Service provider, Grid, Cost reduction, web services, Risk analysis (engineering), 020201 artificial intelligence & image processing, Web service, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], business, computer
Abstract

International audience; A main challenge for service providers is managing service-level agreements (SLAs) with their customers while satisfying their business objectives, such as maximizing profits. Most current systems fail to consider business objectives and thus to provide a complete SLA management solution. This work proposes an SLA-driven management solution that aims to maximize the provider's profit by reducing resource costs as well as fines owning to SLA violations. Specifically, this work proposes a framework that comprises multiple, configurable control loops and supports automatically adjusting service configurations and resource usage in order to maintain SLAs in the most cost-effective way. The framework targets services implemented on top of large-scale distributed infrastructures, such as clouds. Experimental results demonstrate its effectiveness in maintaining SLAs while reducing provider costs.

Published
2011