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Статті в журналах з теми "Centre de données cloud"
Florentin N’guena Yamalet, Ulrich, César Toutourlou, Abdoulaye Diakité, Tiburce Yafondo, Pierre Bruneteau, Mathias Blangis, Jean françois Coste, and Jean François Lavalle. "Traitement Des Fractures Thalamiques Du Calcanéus Par Enclouage Verrouillé À Foyer Fermé « Type Calcanail » : Résultats Clinique, Fonctionnel Et Radiologique À Propos De 16 Cas Au Centre Hospitalier De La Côte Basque (France)." European Scientific Journal, ESJ 18, no. 17 (May 31, 2022): 68. http://dx.doi.org/10.19044/esj.2022.v18n17p68.
Повний текст джерелаHa, Nguyen Nhu. "Les principales questions juridiques posées par l’informatique en nuage." Science & Technology Development Journal - Economics - Law and Management 2, no. 3 (January 20, 2019): 80–88. http://dx.doi.org/10.32508/stdjelm.v2i3.522.
Повний текст джерелаHarvey, C. C., C. Huc, and M. Nonon-Latapie. "Centre de données de la physique des plasmas." Advances in Space Research 31, no. 5 (March 2003): 1291–95. http://dx.doi.org/10.1016/s0273-1177(02)00943-2.
Повний текст джерелаGervais, M. J., François Chagnon, and André Paccioni. "Augmenter l’utilisation des données probantes par les intervenants et les gestionnaires en centre jeunesse." Service social 57, no. 1 (October 20, 2011): 49–62. http://dx.doi.org/10.7202/1006247ar.
Повний текст джерелаRajput, Ravindra Kumar Singh, Dinesh Goyal, Anjali Pant, Gajanand Sharma, Varsha Arya, and Marjan Kuchaki Rafsanjani. "Cloud Data Centre Energy Utilization Estimation." International Journal of Cloud Applications and Computing 12, no. 1 (January 1, 2022): 1–16. http://dx.doi.org/10.4018/ijcac.311035.
Повний текст джерелаPerrault, Francine, and Daniel Fortin. "Vécu et perception des usagers d’un centre de crise." Santé mentale au Québec 18, no. 1 (September 11, 2007): 287–301. http://dx.doi.org/10.7202/032260ar.
Повний текст джерелаAndreoli, Rémi, Benoît Ducarouge, Jonathan Maura, Audrey Leopold, Pierre-Nicolas Mougel, Arnaud Durand, Cyril Marchand, et al. "L'imagerie spatiale à très haute résolution au coeur du dispositif de Geospatial Cloud Computing QëhnelöTM : application aux données Pléiades en Nouvelle-Calédonie." Revue Française de Photogrammétrie et de Télédétection, no. 209 (January 11, 2015): 47–57. http://dx.doi.org/10.52638/rfpt.2015.185.
Повний текст джерелаBarrandon, Jean-Noël. "L'expérience du centre Ernest Babelon dans le traitement des données." Le médiéviste et l'ordinateur 1, no. 1 (1990): 57–61. http://dx.doi.org/10.3406/medio.1990.1233.
Повний текст джерелаRijo da Fonseca Lino, Maria Teresa. "Base de données textuelles et terminographiques." Meta 39, no. 4 (September 30, 2002): 786–89. http://dx.doi.org/10.7202/003951ar.
Повний текст джерелаRey, Grégoire, Agathe Lamarche-Vadel, and Éric Jougla. "Comment mesure-t-on les causes de décès en France ?" Questions de santé publique, no. 21 (June 2013): 1–4. http://dx.doi.org/10.1051/qsp/2013021.
Повний текст джерелаДисертації з теми "Centre de données cloud"
Tudoran, Radu-Marius. "High-Performance Big Data Management Across Cloud Data Centers." Electronic Thesis or Diss., Rennes, École normale supérieure, 2014. http://www.theses.fr/2014ENSR0004.
Повний текст джерелаThe easily accessible computing power offered by cloud infrastructures, coupled with the "Big Data" revolution, are increasing the scale and speed at which data analysis is performed. Cloud computing resources for compute and storage are spread across multiple data centers around the world. Enabling fast data transfers becomes especially important in scientific applications where moving the processing close to data is expensive or even impossible. The main objectives of this thesis are to analyze how clouds can become "Big Data - friendly", and what are the best options to provide data management services able to meet the needs of applications. In this thesis, we present our contributions to improve the performance of data management for applications running on several geographically distributed data centers. We start with aspects concerning the scale of data processing on a site, and continue with the development of MapReduce type solutions allowing the distribution of calculations between several centers. Then, we present a transfer service architecture that optimizes the cost-performance ratio of transfers. This service is operated in the context of real-time data streaming between cloud data centers. Finally, we study the viability, for a cloud provider, of the solution consisting in integrating this architecture as a service based on a flexible pricing paradigm, qualified as "Transfer-as-a-Service"
Dumont, Frédéric. "Analyses et préconisations pour les centres de données virtualisés." Thesis, Nantes, Ecole des Mines, 2016. http://www.theses.fr/2016EMNA0249/document.
Повний текст джерелаThis thesis presents two contributions. The first contribution is the study of key performance indicators to monitor physical and virtual machines activity running on VMware and KVM hypervisors. This study highlights performance metrics and provides advanced analysis with the aim to prevent or detect abnormalities related to the four main resources of a datacenter: CPU, memory, disk and network. Thesecond contribution relates to a tool for virtual machines with pre-determined and / or atypical behaviors detection. The detection of these virtual machines has several objectives. First, optimize the use of hardware resources by freeing up resources by removing unnecessary virtual machines or by resizing those oversized. Second, optimize infrastructure performance by detecting undersized or overworked virtual machines and those having an atypical activity
Rostirolla, Gustavo. "Ordonnancement dans un centre de calculs alimenté par des sources d'énergie renouvelables sans connexion au réseau avec une charge de travail mixte basée sur des phases." Thesis, Toulouse 3, 2019. http://www.theses.fr/2019TOU30160.
Повний текст джерелаDue to the increase of cloud, web-services and high performance computing demands all over the world, datacenters are now known to be one of the biggest actors when talking about energy consumption. In 2006 alone, datacenters were responsible for consuming 61.4 billion kWh in the United States. When looking at the global scenario, datacenters are currently consuming more energy than the entire United Kingdom, representing about 1.3\% of world's electricity consumption, and being even called the factories of the digital age. Supplying datacenters with clean-to-use renewable energy is therefore essential to help mitigate climate change. The vast majority of cloud provider companies that claim to use green energy supply on their datacenters consider the classical grid, and deploy the solar panels/wind turbines somewhere else and sell the energy to electricity companies, which incurs in energy losses when the electricity travels throughout the grid. Even though several efforts have been conducted at the computing level in datacenters partially powered by renewable energy sources, the scheduling considering on site renewable energy sources and its variations, without connection to the grid can still be widely explored. Since energy efficiency in datacenters is directly related to the resource consumption of the computing nodes, performance optimization and an efficient load scheduling are essential for energy saving. Today, we observe the use of cloud computing as the basis of datacenters, either in a public or private fashion. The main particularity of our approach is that we consider a power envelope composed only by renewable energy as a constraint, hence with a variable amount of power available at each moment. The scheduling under this kind of constraint becomes more complex: without further checks, we are not ensured that a running task will run until completion. We start by addressing the IT load scheduling of batch tasks, which are characterized by their release time, due date and resource demand, in a cloud datacenter while respecting the aforementioned power envelope. The data utilized for the batch tasks comes from datacenter traces, containing CPU, memory and network values. The power envelopes considered, represent an estimation which would be provided by a power decision module and is the expected power production based on weather forecasts. The aim is to maximize the Quality of Service with a variable constraint on electrical power. Furthermore, we explore a workload composed by batch and services, where the resources consumption varies over time. The traces utilized for the service tasks originate from business critical datacenter. In this case we rely on the concept of phases, where each significant resource change in the resources consumption constitutes a new phase of the given task. In this task model phases could also receive less resources than requested. The reduction of resources can impact the QoS and consequently the datacenter profit. In this approach we also include the concept of cross-correlation to evaluate where to place a task under a power curve, and what is the best node to place tasks together (i.e. sharing resources). Finally, considering the previous workload of batch tasks and services, we present an approach towards handling unexpected events in the datacenter. More specifically we focus on IT related events such as tasks arriving at any given time, demanding more or less resources than expected, or having a different finish time than what was initially expected. We adapt the proposed algorithms to take actions depending on which event occurs, e.g. task degradation to reduce the impact on the datacenter profit
Pastor, Jonathan. "Contributions à la mise en place d'une infrastructure de Cloud Computing à large échelle." Thesis, Nantes, Ecole des Mines, 2016. http://www.theses.fr/2016EMNA0240/document.
Повний текст джерелаThe continuous increase of computing power needs has favored the triumph of the Cloud Computing model. Customers asking for computing power will receive supplies via Internet resources hosted by providers of Cloud Computing infrastructures. To make economies of scale, Cloud Computing that are increasingly large and concentrated in few attractive places, leading to problems such energy supply, fault tolerance and the fact that these infrastructures are far from most of their end users. During this thesis we studied the implementation of an fully distributed and decentralized IaaS system operating a network of micros data-centers deployed in the Internet backbone, using a modified version of OpenStack that leverages non relational databases. A prototype has been experimentally validated onGrid’5000, showing interesting results, however limited by the fact that OpenStack doesn’t take advantage of a geographically distributed functioning. Thus, we focused on adding the support of network locality to improve performance of Cloud Computing services by favoring collaborations between close nodes. A prototype of the DVMS algorithm, working with an unstructured topology based on the Vivaldi algorithm, has been validated on Grid’5000. This prototype got the first prize at the large scale challenge of the Grid’5000 spring school in 2014. Finally, the work made with DVMS enabled us to participate at the development of the VMPlaceS simulator
Dab, Boutheina. "Optimization of routing and wireless resource allocation in hybrid data center networks." Thesis, Paris Est, 2017. http://www.theses.fr/2017PESC1068/document.
Повний текст джерелаThe high proliferation of smart devices and online services allows billions of users to connect with network while deploying a vast range of applications. Particularly, with the advent of the future 5G technology, it is expected that a tremendous mobile and data traffic will be crossing Internet network. In this regard, Cloud service providers are urged to rethink their data center architectures in order to cope with this unprecedented traffic explosion. Unfortunately, the conventional wired infrastructures struggle to resist to such a traffic growth and become prone to serious congestion problems. Therefore, new innovative techniques are required. In this thesis, we investigate a recent promising approach that augments the wired Data Center Network (DCN) with wireless communications. Indeed, motivated by the feasibility of the new emerging 60 GHz technology, offering an impressive data rate (≈ 7 Gbps), we envision, a Hybrid (wireless/wired) DCN (HDCN) architecture. Our HDCN is based on i) Cisco’s Massively Scalable Data Center (MSDC) model and ii) IEEE 802.11ad standard. Servers in the HDCN are regrouped into racks, where each rack is equipped with a: i) Ethernet top-of-rack (ToR) switch and ii) set of wireless antennas. Our research aims to optimize the routing and the allocation of wireless resources for inter-rack communications in HDCN while enhancing network performance and minimizing congestion. The problem of routing and resource allocation in HDCN is NP-hard. To deal with this difficulty, we will tackle the problem into three stages. In the first stage, we consider only one-hop inter-rack communications in HDCN, where all communicating racks are in the same transmission range. We will propound a new wireless channel allocation approach in HDCN to hardness both wireless and wired interfaces for incoming flows while enhancing network throughput. In the second stage, we deal with the multi-hop communications in HDCN where communicating racks can not communicate in one single-hop wireless path. We propose a new approach to jointly route and allocate channels for each single communication flow, in an online way. Finally, in the third stage, we address the batched arrival of inter-rack communications to the HDCN so as to further optimize the usage of wireless and wired resources. For that end, we propose: i) a heuristic-based and ii) an approximate, solutions, to solve the joint batch routing and channel assignment. Based on extensive simulations conducted in QualNet simulator while considering the full protocol stack, the obtained results for both real workload and uniform traces, show that our proposals outperform the prominent related strategies
Chkirbene, Zina. "Network topologies for cost reduction and QoS improvement in massive data centers." Thesis, Bourgogne Franche-Comté, 2017. http://www.theses.fr/2017UBFCK002/document.
Повний текст джерелаData centers (DC) are being built around the world to provide various cloud computing services. One of the fundamental challenges of existing DC is to design a network that interconnects massive number of nodes (servers)1 while reducing DC' cost and energy consumption. Several solutions have been proposed (e.g. FatTree, DCell and BCube), but they either scale too fast (i.e., double exponentially) or too slow. Effcient DC topologies should incorporate high scalability, low latency, low Average Path Length (APL), high Aggregated Bottleneck Throughput (ABT) and low cost and energy consumption. Therefore, in this dissertation, different solutions have been proposed to overcome these problems. First, we propose a novel DC topology called LCT (Linked Cluster Topology) as a new solution for building scalable and cost effective DC networking infrastructures. The proposed topology reduces the number of redundant connections between clusters of nodes, while increasing the numbers of nodes without affecting the network bisection bandwidth. Furthermore, in order to reduce the DCs cost and energy consumption, we propose first a new static energy saving topology called VacoNet (Variable Connection Network) that connects the needed number of servers while reducing the unused materials (cables, switches). Also, we propose a new approach that exploits the correlation in time of internode communication and some topological features to maximize energy saving without too much impacting the average path length
Ikken, Sonia. "Efficient placement design and storage cost saving for big data workflow in cloud datacenters." Thesis, Evry, Institut national des télécommunications, 2017. http://www.theses.fr/2017TELE0020/document.
Повний текст джерелаThe typical cloud big data systems are the workflow-based including MapReduce which has emerged as the paradigm of choice for developing large scale data intensive applications. Data generated by such systems are huge, valuable and stored at multiple geographical locations for reuse. Indeed, workflow systems, composed of jobs using collaborative task-based models, present new dependency and intermediate data exchange needs. This gives rise to new issues when selecting distributed data and storage resources so that the execution of tasks or job is on time, and resource usage-cost-efficient. Furthermore, the performance of the tasks processing is governed by the efficiency of the intermediate data management. In this thesis we tackle the problem of intermediate data management in cloud multi-datacenters by considering the requirements of the workflow applications generating them. For this aim, we design and develop models and algorithms for big data placement problem in the underlying geo-distributed cloud infrastructure so that the data management cost of these applications is minimized. The first addressed problem is the study of the intermediate data access behavior of tasks running in MapReduce-Hadoop cluster. Our approach develops and explores Markov model that uses spatial locality of intermediate data blocks and analyzes spill file sequentiality through a prediction algorithm. Secondly, this thesis deals with storage cost minimization of intermediate data placement in federated cloud storage. Through a federation mechanism, we propose an exact ILP algorithm to assist multiple cloud datacenters hosting the generated intermediate data dependencies of pair of files. The proposed algorithm takes into account scientific user requirements, data dependency and data size. Finally, a more generic problem is addressed in this thesis that involve two variants of the placement problem: splittable and unsplittable intermediate data dependencies. The main goal is to minimize the operational data cost according to inter and intra-job dependencies
Salazar, Javier. "Resource allocation optimization algorithms for infrastructure as a service in cloud computing." Thesis, Sorbonne Paris Cité, 2016. http://www.theses.fr/2016USPCB074.
Повний текст джерелаThe cloud architecture offers on-demand computing, storage and applications. Within this structure, Cloud Providers (CPs) not only administer infrastructure resources but also directly benefit from leasing them. In this thesis, we propose three optimization models to assist CPs reduce the costs incurred in the resource allocation process when serving users’ demands. Implementing the proposed models will not only increase the CP’s revenue but will also enhance the quality of the services offered, benefiting all parties. We focus on Infrastructure as a Service (IaaS) resources which constitute the physical infrastructure of the cloud and are contained in datacenters (DCs). Following existing research in DC design and cloud computing applications, we propose the implementation of smaller DCs (Edge DCs) be located close to end users as an alternative to large centralized DCs. Lastly, we use the Column Generation optimization technique to handle large scale optimization models efficiently. The proposed formulation optimizes both the communications and information technology resources in a single phase to serve IaaS requests. Based on this formulation, we also propose a second model that includes QoS guarantees under the same Infrastructure as a Service resource allocation perspective, to provide different solutions to diverse aspects of the resource allocation problem such as cost and delay reduction while providing different levels of service. Additionally, we consider the multimedia cloud computing scenario. When Edge DCs architecture is applied to this scenario it results in the creation of the Multimedia Edge Cloud (MEC) architecture. In this context we propose a resource allocation approach to help with the placement of these DCs to reduce communication related problems such as jitter and latency. We also propose the implementation of optical fiber network technologies to enhance communication between DCs. Several studies can be found proposing new methods to improve data transmission and performance. For this study, we decided to implement Wavelength Division Multiplexing (WDM) to strengthen the link usage and light-paths and, by doing so, group different signals over the same wavelength. Using a realistic simulation environment, we evaluate the efficiency of the approaches proposed in this thesis using a scenario specifically designed for the DCs, comparing them with different benchmarks and also simulating the effect of the optical formulation on the network performance. The numerical results obtained show that by using the proposed models, a CP can efficiently reduce allocation costs while maintaining satisfactory request acceptance and QoS ratios
Politaki, Dimitra. "Vers la modélisation de clusters de centres de données vertes." Thesis, Université Côte d'Azur (ComUE), 2019. http://www.theses.fr/2019AZUR4116.
Повний текст джерелаData center clusters energy consumption is rapidly increasing making them the fastest-growing consumers of electricity worldwide. Renewable electricity sources and especially solar energy as a clean and abundant energy can be used, in many locations, to cover their electricity needs and make them "green" namely fed by photovoltaics. This potential can be explored by predicting solar irradiance and assessing the capacity provision for data center clusters. In this thesis we develop stochastic models for solar energy; one at the surface of the Earth and a second one which models the photovoltaic output current. We then compare them to the state of the art on-off model and validate them against real data. We conclude that the solar irradiance model can better capture the multiscales correlations and is suitable for small scale cases. We then propose a new job life-cycle of a complex and real cluster system and a model for data center clusters that supports batch job submissions and cons iders both impatient and persistent customer behavior. To understand the essential computer cluster characteristics, we analyze in detail two different workload type traces; the first one is the published complex Google trace and the second, simpler one, which serves scientific purposes, is from the Nef cluster located at the research center Inria Sophia Antipolis. We then implement the marmoteCore-Q, a tool for the simulation of a family of queueing models based on our multi-server model for data center clusters with abandonments and resubmissions
Božić, Nikola. "Blockchain technologies and their application to secure virtualized infrastructure control." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS596.
Повний текст джерелаBlockchain is a technology making the shared registry concept from distributed systems a reality for a number of application domains, from the cryptocurrency one to potentially any industrial system requiring decentralized, robust, trusted and automated decision making in a multi-stakeholder situation. Nevertheless, the actual advantages in using blockchain instead of any other traditional solution (such as centralized databases) are not completely understood to date, or at least there is a strong need for a vademecum guiding designers toward the right decision about when to adopt blockchain or not, which kind of blockchain better meets use-case requirements, and how to use it. At first, we aim at providing the community with such a vademecum, while giving a general presentation of blockchain that goes beyond its usage in Bitcoin and surveying a selection of the vast literature that emerged in the last few years. We draw the key requirements and their evolution when passing from permissionless to permissioned blockchains, presenting the differences between proposed and experimented consensus mechanisms, and describing existing blockchain platforms. Furthermore, we present the B-VMOA blockchain to secure virtual machine orchestration operations for cloud computing and network functions virtualization systems applying the proposed vademecum logic. Using tutorial examples, we describe our design choices and draw implementation plans. We further develop the vademecum logic applied to cloud orchestration and how it can lead to precise platform specifications. We capture the key system operations and complex interactions between them. We focus on the last release of Hyperledger Fabric platform as a way to develop B-VMOA system. Besides, Hyperledger Fabric optimizes conceived B-VMOA network performance, security, and scalability by way of workload separation across: (i) transaction execution and validation peers, and (ii) transaction ordering nodes. We study and use a distributed execute-order-validate architecture which differentiates our conceived B-VMOA system from legacy distributed systems that follow a traditional state-machine replication architecture. We parameterize and validate our model with data collected from a realistic testbed, presenting an empirical study to characterize system performance and identify potential performance bottlenecks. Furthermore, we present the tools we used, the network setup and the discussion on empirical observations from the data collection. We examine the impact of various configurable parameters to conduct an in-dept study of core components and benchmark performance for common usage patterns. Namely, B-VMOA is meant to be run within data center. Different data center interconnection topologies scale differently due to communication protocols. Enormous challenges appear to efficiently design the network interconnections so that the deployment and maintenance of the infrastructure is cost-effective. We analyze the structural properties of several DCN topologies and also present some comparison among these network architectures with the aim to reduce B-VMOA overhead costs. From our analysis, we recommend the hypercube topology as a solution to address the performance bottleneck in the B-VMOA control plane caused by gossip dissemination protocol along with an estimate of performance improvement
Книги з теми "Centre de données cloud"
Ruprecht, Jaenicke, Deutsche Forschungsgemeinschaft, and Collaborative Research Centre 233 "Dynamik und Chemie der Hydrometeore", eds. Dynamics and chemistry of hydrometeors: Final report of the Collaborative Research Centre 233 "Dynamik und Chemie der Hydrometeore". Weinheim: Wiley-VCH, 2001.
Знайти повний текст джерелаWorkshop on Cloud Processes and Cloud Feedbacks in Large-scale Models (1999 Reading, Berkshire, United Kingdom). Workshop on Cloud Processes and Cloud Feedbacks in Large-scale Models, European Centre for Medium-range Weather Forecasts, Reading, Berkshire, United Kingdom, 9-13 November 1999. Geneva, Switzerland: Joint Planning Staff for WCRP, World Meteorological Organization, 2000.
Знайти повний текст джерелаSauveur, Paula. La protection du secret commercial dans les nuages publics de l'infonuagique (cloud computing). Cowansville, Québec: Éditions Y. Blais, 2013.
Знайти повний текст джерелаInc, ebrary, ed. Microsoft SQL Azure: Enterprise application development ; build enterprise-ready applications and projects with SQL Azure. Birmingham, U.K: Packt, 2010.
Знайти повний текст джерелаPatrick, Boucheron, Broise Henri, and Thébert Yvon, eds. La brique antique et médiévale: Production et commercialisation d'un materiau : actes du colloque international organisé par le Centre d'histoire urbaine de l'Ecole normale supérieure de Fontenay/Saint Cloud et l'Ecole française de Rome, Saint-Cloud, 16-18 novembre 1995. Rome: Ecole française de Rome, 2000.
Знайти повний текст джерелаColloque La condition des femmes immigrantes : en savoir davantage (1989 Montréal, Québec). Actes du Colloque La condition des femmes immigrantes : en savoir davantage: Faits actuels et données récentes [organisé par] le Centre des femmes de Montréal, tenu le 21 avril 1989. Montréal: Éditions Communiqu'Elles, 1990.
Знайти повний текст джерелаJean-Louis, Biget, Boissière Jean, and Hervé Jean-Claude, eds. Le bois et la ville, du Moyen Age au XXe siècle: Colloque organisé à Saint-Cloud les 18 et 19 novembre 1988, par le Centre d'histoire urbaine de l'Ecole normale supérieure de Fontenay/Saint-Cloud et le Groupe d'histoire des forêts françaises. Fontenay-aux-Roses: ENS de Fontenay/Saint-Cloud, 1991.
Знайти повний текст джерелаJournet, Charles. Commentaire de la première lettre de saint Jean et de ses récits sur la résurrection: Conférences données par le cardinal Journet à Genève au Centre universitaire catholique, du 1er novembre 1969 au 13 juin 1970. [s.l.]: Fondation du cardinal Journet, 1987.
Знайти повний текст джерелаNicole, Jacques-Lefèvre, Regard Frédéric, and École normale supérieure de Fontenay-Saint-Cloud. Centre de recherche Li Di Sa., eds. Une Histoire de la "fonction-auteur" est-elle possible?: Actes du colloque organisé par le Centre de recherche LiDiSa (Littérature et Discours du Savoir) 11-13 mai 2000, ENS Fontenay-Saint-Cloud. Saint-Etienne: Publications de l'Université de Saint-Étienne, 2001.
Знайти повний текст джерелаJones, Gareth, Campbell John, Chris Crone, Surekha Parekh, and Jay Yothers. Db2 11: The Ultimate Database for Cloud, Analytics, and Mobile. MC Press, LLC, 2014.
Знайти повний текст джерелаЧастини книг з теми "Centre de données cloud"
Dubey, Kalka, Aida A. Nasr, S. C. Sharma, Nirmeen El-Bahnasawy, Gamal Attiya, and Ayman El-Sayed. "Efficient VM Placement Policy for Data Centre in Cloud Environment." In Advances in Intelligent Systems and Computing, 301–9. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0751-9_28.
Повний текст джерелаIsmaeel, Salam, and Ali Miri. "Multivariate Time Series ELM for Cloud Data Centre Workload Prediction." In Lecture Notes in Computer Science, 565–76. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39510-4_52.
Повний текст джерелаReißer, Sabin. "Plans for a German Grid Operations and Support Centre." In Managed Grids and Cloud Systems in the Asia-Pacific Research Community, 293–98. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-6469-4_21.
Повний текст джерелаSingh, Nongmaithem Ajith, and M. Hemalatha. "Reduce Energy Consumption through Virtual Machine Placement in Cloud Data Centre." In Mining Intelligence and Knowledge Exploration, 466–74. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03844-5_47.
Повний текст джерелаGomes, Sheridan, Adel N. Toosi, and Barrett Ens. "Digital Twin of a Cloud Data Centre: An OpenStack Cluster Visualisation." In Internet of Things, 209–25. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05528-7_8.
Повний текст джерелаArulkumar, V., R. Lathamanju, and A. Sandanakaruppan. "Assurance on data integrity in cloud data centre using PKI built RDIC method." In Recent Trends in Communication and Electronics, 98–102. London: CRC Press, 2021. http://dx.doi.org/10.1201/9781003193838-19.
Повний текст джерелаSathiya, R. R., and P. Prakash. "Cloud Management: An Autoencoder-Based Clustering of Virtual Machines for Data Centre Workload." In Advances in Automation, Signal Processing, Instrumentation, and Control, 3113–21. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8221-9_290.
Повний текст джерелаVenkata Subba Reddy, Kothapuli, Jagirdar Srinivas, and Ahmed Abdul Moiz Qyser. "A Dynamic Hierarchical Load Balancing Service Architecture for Cloud Data Centre Virtual Machine Migration." In Smart Intelligent Computing and Applications, 9–17. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1921-1_2.
Повний текст джерелаRamesh, Dharavath, Rahul Mishra, and Amitesh Kumar Pandit. "An Efficient Stream Cipher Based Secure and Dynamic Updation Method for Cloud Data Centre." In Soft Computing Systems, 505–16. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1936-5_53.
Повний текст джерелаSrinivasulu Reddy, J., and P. Supraja. "Flow Distribution-Aware Load Balancing for the Data Centre over Cloud Services with Virtualization." In New Trends in Computational Vision and Bio-inspired Computing, 863–71. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41862-5_87.
Повний текст джерелаТези доповідей конференцій з теми "Centre de données cloud"
Cucinotta, Tommaso, Diego Lugones, Davide Cherubini, and Eric Jul. "Data Centre Optimisation Enhanced by Software Defined Networking." In 2014 IEEE 7th International Conference on Cloud Computing (CLOUD). IEEE, 2014. http://dx.doi.org/10.1109/cloud.2014.28.
Повний текст джерелаIsmaeel, Salam, Ali Miri, and Ayman Al-Khazraji. "Energy-consumption clustering in cloud data centre." In 2016 3rd MEC International Conference on Big Data and Smart City (ICBDSC). IEEE, 2016. http://dx.doi.org/10.1109/icbdsc.2016.7460373.
Повний текст джерелаSankari, Subbiah, Perumal Varalakshmi, and Boopathi Divya. "Network Traffic Analysis of cloud data centre." In 2015 International Conference on Computing and Communications Technologies (ICCCT). IEEE, 2015. http://dx.doi.org/10.1109/iccct2.2015.7292785.
Повний текст джерелаYu, Yale, Guojian Cheng, and Xinjian Qiang. "Data centre transformation: Integrated business model framework of cloud computing oriented data centre." In 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). IEEE, 2016. http://dx.doi.org/10.1109/imcec.2016.7867538.
Повний текст джерелаTsafara, Amalia, Christos Tryfonopoulos, and Spiros Skiadopoulos. "CloudStudy: A cloud-based system for supporting multi-centre studies." In 2013 IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE, 2013. http://dx.doi.org/10.1109/bibe.2013.6701549.
Повний текст джерелаVasile, Ionut, Dragos Ciobanu-Zabet, and Mihnea Dulea. "The new operations centre of the Romanian grid infrastructure." In 2015 Conference Grid, Cloud & High Performance Computing in Science (ROLCG). IEEE, 2015. http://dx.doi.org/10.1109/rolcg.2015.7367429.
Повний текст джерелаDe Maio, Vincenzo, Gabor Kecskemeti, and Radu Prodan. "An improved model for live migration in data centre simulators." In UCC '16: 9th International Conference on Utility and Cloud Computing. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2996890.2996892.
Повний текст джерелаLIN, Chih-hsun, George Chu, Pei-rong Tsai, Chan-hin Iong, Felix Lee, Eric Yen, and Shih-chang Lee. "A single rack cloud centre with unprecedented power and thermal efficiency." In International Symposium on Grids and Clouds (ISGC) 2017. Trieste, Italy: Sissa Medialab, 2017. http://dx.doi.org/10.22323/1.293.0016.
Повний текст джерелаde Frein, Ruairi. "The data-centre whisperer: Relative attribute usage estimation for cloud servers." In 2016 24th European Signal Processing Conference (EUSIPCO). IEEE, 2016. http://dx.doi.org/10.1109/eusipco.2016.7760336.
Повний текст джерелаTsafara, Amalia, Christos Tryfonopoulos, Spiros Skiadopoulos, and Lefteris Zervakis. "Cloud-Based Data and Knowledge Management for Multi-Centre Biomedical Studies." In K-CAP 2015: Knowledge Capture Conference. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2815833.2816949.
Повний текст джерелаЗвіти організацій з теми "Centre de données cloud"
Dudley, J. P., and S. V. Samsonov. Système de traitement automatisé du gouvernement canadien pour la détection des variations et l'analyse des déformations du sol à partir des données de radar à synthèse d'ouverture de RADARSAT-2 et de la mission de la Constellation RADARSAT : description et guide de l'utilisateur. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/329134.
Повний текст джерелаSaulais, Laure, and Maurice Doyon. Impact du design de questions sur la perception des compensations proposées et les intentions de participation au pad: étude de préfaisabilité. CIRANO, August 2022. http://dx.doi.org/10.54932/ziga3839.
Повний текст джерелаRousseau, Henri-Paul. Gutenberg, L’université et le défi numérique. CIRANO, December 2022. http://dx.doi.org/10.54932/wodt6646.
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