Дисертації з теми "Centre de données cloud"
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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
Alili, Hiba. "Intégration de données basée sur la qualité pour l'enrichissement des sources de données locales dans le Service Lake." Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLED019.
Повний текст джерелаIn the Big Data era, companies are moving away from traditional data-warehouse solutions whereby expensive and timeconsumingETL (Extract, Transform, Load) processes are used, towards data lakes in order to manage their increasinglygrowing data. Yet the stored knowledge in companies’ databases, even though in the constructed data lakes, can never becomplete and up-to-date, because of the continuous production of data. Local data sources often need to be augmentedand enriched with information coming from external data sources. Unfortunately, the data enrichment process is one of themanual labors undertaken by experts who enrich data by adding information based on their expertise or select relevantdata sources to complete missing information. Such work can be tedious, expensive and time-consuming, making itvery promising for automation. We present in this work an active user-centric data integration approach to automaticallyenrich local data sources, in which the missing information is leveraged on the fly from web sources using data services.Accordingly, our approach enables users to query for information about concepts that are not defined in the data sourceschema. In doing so, we take into consideration a set of user preferences such as the cost threshold and the responsetime necessary to compute the desired answers, while ensuring a good quality of the obtained results
Degoutin, Stéphane. "Société-nuage." Thesis, Paris Est, 2019. http://www.theses.fr/2019PESC1009.
Повний текст джерелаThis book unfolds, like a Chinese landscape painting through which the viewer’s gaze wanders slowly. I describe a panorama. It is not made of mountains in the mist or bushes swept by the wind, but of data centers, automated warehouses, social network feeds...I explore the hypothesis that the Internet is part of a general process that reduces society and materials to small-scale components, which allow its mechanisms to become more fluid. A chemist’s idea – the decomposition of matter into powder to facilitate its recomposition – is also applied to social relations, memory and humans in general.Just as the reduction of matter accelerates chemical reactions, the reduction of society to powder allows for an accelerated decomposition and recomposition of all from which humans are made. It allows to multiply the reactions within society, to accelerate the productions of humanity and the social chemistry : combination of human passions (Charles Fourier), hyperfragmentation of work (Mechanical Turk), decomposition of knowledge (Paul Otlet), Internet of neurons (Michael Chorost), agregation of micro affects (Facebook). This is what I call the « society as cloud »
Cerović, Danilo. "Architecture réseau résiliente et hautement performante pour les datacenters virtualisés." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS478.
Повний текст джерелаThe amount of traffic in data centers is growing exponentially and it is not expected to stop growing any time soon. This brings about a vast amount of advancements in the networking field. Network interface throughputs supported today are in the range of 40Gbps and higher. On the other hand, such high interface throughputs do not guarantee higher packet processing speeds which are limited due to the overheads imposed by the architecture of the network stack. Nevertheless, there is a great need for a speedup in the forwarding engine, which is the most important part of a high-speed router. For this reason, many software-based and hardware-based solutions have emerged recently with a goal of increasing packet processing speeds. The networking stack of an operating system is not conceived for high-speed networking applications but rather for general purpose communications. In this thesis, we investigate various approaches that strive to improve packet processing performance on server-class network hosts, either by using software, hardware, or the combination of the two. Some of the solutions are based on the Click modular router which offloads its functions on different types of hardware like GPUs, FPGAs or different cores among different servers with parallel execution. Furthermore, we explore other software solutions which are not based on the Click modular router. We compare software and hardware packet processing solutions based on different criteria and we discuss their integration possibilities in virtualized environments, their constraints and their requirements. As our first contribution, we propose a resilient and highly performant fabric network architecture. Our goal is to build a layer 2 mesh network that only uses directly connected hardware acceleration cards that perform packet processing instead of routers and switches. We have decided to use the TRILL protocol for the communication between these smart NICs as it provides a better utilization of network links while also providing least-cost pair-wise data forwarding. The data plane packet processing is offloaded on a programmable hardware with parallel processing capability. Additionally, we propose to use the ODP API so that packet processing application code can be reused by any other packet processing solution that supports the ODP API. As our second contribution, we designed a data plane of the TRILL protocol on theMPPA (Massively Parallel Processor Array) smart NIC which supports the ODP API. Our experimental results show that we can process TRILL frames at full-duplex line-rate (up to 40Gbps) for different packet sizes while reducing latency. As our third contribution, we provide a mathematical analysis of the impact of different network topologies on the control plane’s load. The data plane packet processing is performed on the MPPA smart NICs. Our goal is to build a layer 2 mesh network that only uses directly connected smart NIC cards instead of routers and switches. We have considered various network topologies and we compared their loads induced by the control plane traffic. We have also shown that hypercube topology is the most suitable for our PoP data center use case because it does not have a high control plane load and it has a better resilience than fat-tree while having a shorter average distance between the nodes
Dehdouh, Khaled. "Entrepôts de données NoSQL orientés colonnes dans un environnement cloud." Thesis, Lyon 2, 2015. http://www.theses.fr/2015LYO22018.
Повний текст джерелаThe work presented in this thesis aims at proposing approaches to build data warehouses by using the columnar NoSQL model. The use of NoSQL models is motivated by the advent of big data and the inability of the relational model, usually used to implement data warehousing, to allow data scalability. Indeed, the NoSQL models are suitable for storing and managing massive data. They are designed to build databases whose storage model is the "key/value". Other models, then, appeared to account for the variability of the data: column oriented, document oriented and graph oriented. We have used the column NoSQL oriented model for building massive data warehouses because it is more suitable for decisional queries that are defined by a set of columns (measures and dimensions) from warehouse. However, the NoSQL model columns do not offer online analysis operators (OLAP) for exploiting the data warehouse.We present in this thesis new solutions for logical and physical modeling of columnar NoSQL data warehouses. We have proposed a new approach that allows building data cubes by taking the characteristics of the columnar environment into account. Thus, we have defined new cube operators which allow building columnar cubes. C-CUBE (Columnar-CUBE) for columnar relational data warehouses. MC-CUBE (MapReduce Columnar-CUBE) for columnar NoSQL data warehouses when measures and dimensions are stored in different tables. Finally, CN-CUBE (Columnar NoSQL-CUBE) when measures and dimensions are gathered in the same table according a new logical model that we proposed. We have studied the NoSQL dimensional data model performance and our OLAP operators, and we have proposed a new star join index C-SJI (Columnar-Star join index) suitable for columnar NoSQL data warehouses which store measures and dimensions separately. To evaluate our contribution, we have defined a cost model to measure the impact of the use of this index. Furthermore, we have proposed a logic model called FLM (Flat Logical Model) to represent a data cube NoSQL oriented columns and enable a better management by columnar NoSQL DBMS.To validate our contributions, we have developed a software framework CG-CDW (Cube Generation for Data Warehouses Columnar) to generate OLAP cubes from columnar data warehouses. Also, we have developed a columnar NoSQL decisional benchmark CNSSB (Columnar NoSQL Star Schema Benchmark) based on the SSB and finally, we conducted several tests that have shown the effectiveness of different aggregation operators that we proposed
Demir, Levent. "Module de confiance pour externalisation de données dans le Cloud." Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAM083/document.
Повний текст джерелаData outsourcing to the Cloud has led to new security threats. The main concerns of this thesis are to protect the user data and privacy. In particular, it follows two principles : to decrease the necessary amount of trust towards the Cloud, and to design an architecture based on a trusted module between the Cloud and the clients. Both principles are derived from a new design approach : "Trust The Module, Not The Cloud ".Gathering all the cryptographic operations in a dedicated module allows several advantages : a liberation from internal and external attacks on client side ; the limitation of software to the essential needs offers a better control of the system ; using co-processors for cryptographic operations leads to higher performance.The thesis work is structured into three main sections. In the first section , we confront challenges of a personal Cloud, designed to protect the users’ data and based on a common and cheap single-board computer. The architecture relies on two main foundations : a transparent encryption scheme based on Full Disk Encryption (FDE), initially used for local encryption (e.g., hard disks), and a transparent distribution method that works through iSCSI network protocol in order to outsource containers in Cloud.In the second section we deal with the performance issue related to FDE. By analysing the XTS-AES mode of encryption, the Linux kernel module dm-crypt and the cryptographic co-processors, we introduce a new approach called extReq which extends the cryptographic requests sent to the co-processors. This optimisation has doubled the encryption and decryption throughput.In the final third section we establish a Cloud for enterprises based on a more powerful and certified Hardware Security Module (HSM) which is dedicated to data encryption and keys protection. Based on the TTM architecture, we added "on-the-shelf" features to provide a solution for enterprise
Tos, Uras. "Réplication de données dans les systèmes de gestion de données à grande échelle." Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30066/document.
Повний текст джерелаIn recent years, growing popularity of large-scale applications, e.g. scientific experiments, Internet of things and social networking, led to generation of large volumes of data. The management of this data presents a significant challenge as the data is heterogeneous and distributed on a large scale. In traditional systems including distributed and parallel systems, peer-to-peer systems and grid systems, meeting objectives such as achieving acceptable performance while ensuring good availability of data are major challenges for service providers, especially when the data is distributed around the world. In this context, data replication, as a well-known technique, allows: (i) increased data availability, (ii) reduced data access costs, and (iii) improved fault-tolerance. However, replicating data on all nodes is an unrealistic solution as it generates significant bandwidth consumption in addition to exhausting limited storage space. Defining good replication strategies is a solution to these problems. The data replication strategies that have been proposed for the traditional systems mentioned above are intended to improve performance for the user. They are difficult to adapt to cloud systems. Indeed, cloud providers aim to generate a profit in addition to meeting tenant requirements. Meeting the performance expectations of the tenants without sacrificing the provider's profit, as well as managing resource elasticities with a pay-as-you-go pricing model, are the fundamentals of cloud systems. In this thesis, we propose a data replication strategy that satisfies the requirements of the tenant, such as performance, while guaranteeing the economic profit of the provider. Based on a cost model, we estimate the response time required to execute a distributed database query. Data replication is only considered if, for any query, the estimated response time exceeds a threshold previously set in the contract between the provider and the tenant. Then, the planned replication must also be economically beneficial to the provider. In this context, we propose an economic model that takes into account both the expenditures and the revenues of the provider during the execution of any particular database query. Once the data replication is decided to go through, a heuristic placement approach is used to find the placement for new replicas in order to reduce the access time. In addition, a dynamic adjustment of the number of replicas is adopted to allow elastic management of resources. Proposed strategy is validated in an experimental evaluation carried out in a simulation environment. Compared with another data replication strategy proposed in the cloud systems, the analysis of the obtained results shows that the two compared strategies respond to the performance objective for the tenant. Nevertheless, a replica of data is created, with our strategy, only if this replication is profitable for the provider
Lewtas, Joan. "Radio structure and associated molecular environment at the galactic centre." Thesis, University of Cambridge, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.346434.
Повний текст джерелаSegalini, Andrea. "Alternatives à la migration de machines virtuelles pour l'optimisation des ressources dans les centres informatiques hautement consolidés." Thesis, Université Côte d'Azur, 2021. http://www.theses.fr/2021COAZ4085.
Повний текст джерелаServer virtualization is a technology of prime importance in contemporary data centers. Virtualization provides two key mechanisms, virtual instances and migration, that enable the maximization of the resource utilization to decrease the capital expenses in a data center. In this thesis, we identified and studied two contexts where traditional virtual instance migration falls short in providing the optimal tools to utilize at best the resource available in a cluster: idle virtual machines and largescale hypervisor upgrades.Idle virtual machines permanently lock the resources they are assigned only to await incoming user requests. Indeed, while they are most of the time idle, they cannot be shut down, which would release resources for more demanding services. To address this issue, we propose SEaMLESS, a solution that leverages a novel VM-to-container migration that transforms idle Linux virtual machines into resource-less proxies. SEaMLESS intercepts new user requests while virtual machines are disabled, transparently resuming their execution upon new signs of activity. Furthermore, we propose an easy-to-adopt technique to disable virtual machines based on the traditional hypervisor memory swapping. With our novel suspend-to-swap, we are able to release the majority of the memory and CPU seized by the idle instances, yet providing a fast resume.In the second part of the thesis, we tackle the problem of large-scale upgrades of the hypervisor software. Hypervisor upgrades often require a machine reboot, forcing data center administrators to evacuate the hosts, relocating elsewhere the virtual machines to protect their execution. As this evacuation is costly, both in terms of network transfers and spare resources needed in the data center, hypervisor upgrades hardly scale. We propose Hy-FiX and Multi-FiX, two in-place upgrade that do not consume resource external to the host. Both solutions leverage a zero-copy migration of virtual machines within the host, preserving their execution state across the hypervisor upgrade. Hy-FiX and Multi-FiX achieve scalable upgrades, with only limited impact on the running instances
Moussa, Hadjer. "Traitement automatique de données océanographiques pour l'interpolation de la ∫CO₂ de surface dans l'océan Atlantique tropical, en utilisant les données satellitaires." Thesis, Perpignan, 2016. http://www.theses.fr/2016PERP0025/document.
Повний текст джерелаThis thesis work consists of using satellite data of SST (sea surface temperature), SSS (sea surface salinity), and Chl-a (chlorophyll-a), in order to interpolate the CO2 fugacity (fCO2) in the surface of the tropical Atlantic ocean, for seasons of the period 2002-2013. Three data types were used: in situ (SOCAT V.3 DB (database)); satellite (MODIS-A, Sea-WIFS, and SMOS sensors); and assimilated (SODA V.2.2.4 DB). The first step was the data classification based on SST. The second step was the fCO2 interpolation (for each class of each season), using feedforward NNs (artificial neural networks) with a backpropagation learning method. Obtained results (RMSEs (root mean square error) between 8,8 and 15,7 µatm) confirm the importance of: process each season separately, pass through data classification step, and choose the best NN on the basis of generalization step results. This allowed the development of 138 monthly fCO2 CSV (Comma-separated values) file, with 4 km x 4 km spatial resolution, for the period from July 2002 to December 2013
Kaaniche, Nesrine. "Cloud data storage security based on cryptographic mechanisms." Thesis, Evry, Institut national des télécommunications, 2014. http://www.theses.fr/2014TELE0033/document.
Повний текст джерелаRecent technological advances have given rise to the popularity and success of cloud. This new paradigm is gaining an expanding interest, since it provides cost efficient architectures that support the transmission, storage, and intensive computing of data. However, these promising storage services bring many challenging design issues, considerably due to the loss of data control. These challenges, namely data confidentiality and data integrity, have significant influence on the security and performances of the cloud system. This thesis aims at overcoming this trade-off, while considering two data security concerns. On one hand, we focus on data confidentiality preservation which becomes more complex with flexible data sharing among a dynamic group of users. It requires the secrecy of outsourced data and an efficient sharing of decrypting keys between different authorized users. For this purpose, we, first, proposed a new method relying on the use of ID-Based Cryptography (IBC), where each client acts as a Private Key Generator (PKG). That is, he generates his own public elements and derives his corresponding private key using a secret. Thanks to IBC properties, this contribution is shown to support data privacy and confidentiality, and to be resistant to unauthorized access to data during the sharing process, while considering two realistic threat models, namely an honest but curious server and a malicious user adversary. Second, we define CloudaSec, a public key based solution, which proposes the separation of subscription-based key management and confidentiality-oriented asymmetric encryption policies. That is, CloudaSec enables flexible and scalable deployment of the solution as well as strong security guarantees for outsourced data in cloud servers. Experimental results, under OpenStack Swift, have proven the efficiency of CloudaSec in scalable data sharing, while considering the impact of the cryptographic operations at the client side. On the other hand, we address the Proof of Data Possession (PDP) concern. In fact, the cloud customer should have an efficient way to perform periodical remote integrity verifications, without keeping the data locally, following three substantial aspects : security level, public verifiability, and performance. This concern is magnified by the client’s constrained storage and computation capabilities and the large size of outsourced data. In order to fulfill this security requirement, we first define a new zero-knowledge PDP proto- col that provides deterministic integrity verification guarantees, relying on the uniqueness of the Euclidean Division. These guarantees are considered as interesting, compared to several proposed schemes, presenting probabilistic approaches. Then, we propose SHoPS, a Set-Homomorphic Proof of Data Possession scheme, supporting the 3 levels of data verification. SHoPS enables the cloud client not only to obtain a proof of possession from the remote server, but also to verify that a given data file is distributed across multiple storage devices to achieve a certain desired level of fault tolerance. Indeed, we present the set homomorphism property, which extends malleability to set operations properties, such as union, intersection and inclusion. SHoPS presents high security level and low processing complexity. For instance, SHoPS saves energy within the cloud provider by distributing the computation over multiple nodes. Each node provides proofs of local data block sets. This is to make applicable, a resulting proof over sets of data blocks, satisfying several needs, such as, proofs aggregation
Kumar, Sathiya Prabhu. "Cohérence de données répliquées partagées adaptative pour architectures de stockage à fort degré d’élasticité." Thesis, Paris, CNAM, 2016. http://www.theses.fr/2016CNAM1035/document.
Повний текст джерелаThe main contributions of this thesis are three folds. The first contribution of the thesis focuses on an efficient way to control stale reads in modern database systems with the help of a new consistency protocol called LibRe. LibRe is an acronym for Library for Replication. The main goal of the LibRe protocol is to ensure data consistency by contacting a minimum number of replica nodes during read and write operations with the help of a library information. According to the protocol, during write operations each replica node updates a registry (library) asynchronously with the recent version identifier of the updated data. Forwarding the read requests to a right replica node referring the registry information helps to control stale reads during read operations. Evaluation of data consistency remains challenging both via simulation as well as in a real world setup. Hence, we implemented a new simulation toolkit called Simizer that helps to evaluate the performance of different consistency policies in a fast and efficient way. We also extended an existing benchmark tool YCSB that helps to evaluate the consistency-latency tradeoff offered by modern database systems. The codebase of the simulator and the extended YCSB are made open-source for public access. The performance of the LibRe protocol is validated both via simulation as well as in a real setup with the help of extended YCSB.Although the modern database systems adapt the consistency guarantees of the system per query basis, anticipating the consistency level of an application query in advance during application development time remains challenging for the application developers. In order to overcome this limitation, the second contribution of the thesis focuses on enabling the database system to override the application-defined consistency options during run time with the help of an external input. The external input could be given by a data administrator or by an external service. The thesis validates the proposed model with the help of a prototype implementation inside the Cassandra distributed storage system.The third contribution of the thesis focuses on resolving update conflicts. Resolving update conflicts often involve maintaining all possible values and perform the resolution via domain-specific knowledge at the client side. This involves additional cost in terms of network bandwidth and latency, and considerable complexity. In this thesis, we discuss the motivation and design of a novel data type called priority register that implements a domain-specific conflict detection and resolution scheme directly at the database side, while leaving open the option of additional reconciliation at the application level. Our approach uses the notion of an application-defined replacement ordering and we show that a data type parameterized by such an order can provide an efficient solution for applications that demand domain-specific conflict resolution. We also describe the proof of concept implementation of the priority register inside Cassandra. The conclusion and perspectives of the thesis work are summarized at the end
Sellami, Rami. "Supporting multiple data stores based applications in cloud environments." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLL002/document.
Повний текст джерелаThe production of huge amount of data and the emergence of Cloud computing have introduced new requirements for data management. Many applications need to interact with several heterogeneous data stores depending on the type of data they have to manage: traditional data types, documents, graph data from social networks, simple key-value data, etc. Interacting with heterogeneous data models via different APIs, and multiple data stores based applications imposes challenging tasks to their developers. Indeed, programmers have to be familiar with different APIs. In addition, the execution of complex queries over heterogeneous data models cannot, currently, be achieved in a declarative way as it is used to be with mono-data store application, and therefore requires extra implementation efforts. Moreover, developers need to master and deal with the complex processes of Cloud discovery, and application deployment and execution. In this manuscript, we propose an integrated set of models, algorithms and tools aiming at alleviating developers task for developing, deploying and migrating multiple data stores applications in cloud environments. Our approach focuses mainly on three points. First, we provide a unified data model used by applications developers to interact with heterogeneous relational and NoSQL data stores. This model is enriched by a set of refinement rules. Based on that, we define our query algebra. Developers express queries using OPEN-PaaS-DataBase API (ODBAPI), a unique REST API allowing programmers to write their applications code independently of the target data stores. Second, we propose virtual data stores, which act as a mediator and interact with integrated data stores wrapped by ODBAPI. This run-time component supports the execution of single and complex queries over heterogeneous data stores. It implements a cost model to optimally execute queries and a dynamic programming based algorithm to generate an optimal query execution plan. Finally, we present a declarative approach that enables to lighten the burden of the tedious and non-standard tasks of (1) discovering relevant Cloud environments and (2) deploying applications on them while letting developers to simply focus on specifying their storage and computing requirements. A prototype of the proposed solution has been developed and implemented use cases from the OpenPaaS project. We also performed different experiments to test the efficiency and accuracy of our proposals
Kemp, Gavin. "CURARE : curating and managing big data collections on the cloud." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSE1179/document.
Повний текст джерелаThe emergence of new platforms for decentralized data creation, such as sensor and mobile platforms and the increasing availability of open data on the Web, is adding to the increase in the number of data sources inside organizations and brings an unprecedented Big Data to be explored. The notion of data curation has emerged to refer to the maintenance of data collections and the preparation and integration of datasets, combining them to perform analytics. Curation tasks include extracting explicit and implicit meta-data; semantic metadata matching and enrichment to add quality to the data. Next generation data management engines should promote techniques with a new philosophy to cope with the deluge of data. They should aid the user in understanding the data collections’ content and provide guidance to explore data. A scientist can stepwise explore into data collections and stop when the content and quality reach a satisfaction point. Our work adopts this philosophy and the main contribution is a data collections’ curation approach and exploration environment named CURARE. CURARE is a service-based system for curating and exploring Big Data. CURARE implements a data collection model that we propose, used for representing their content in terms of structural and statistical meta-data organised under the concept of view. A view is a data structure that provides an aggregated perspective of the content of a data collection and its several associated releases. CURARE provides tools focused on computing and extracting views using data analytics methods and also functions for exploring (querying) meta-data. Exploiting Big Data requires a substantial number of decisions to be performed by data analysts to determine which is the best way to store, share and process data collections to get the maximum benefit and knowledge from them. Instead of manually exploring data collections, CURARE provides tools integrated in an environment for assisting data analysts determining which are the best collections that can be used for achieving an analytics objective. We implemented CURARE and explained how to deploy it on the cloud using data science services on top of which CURARE services are plugged. We have conducted experiments to measure the cost of computing views based on datasets of Grand Lyon and Twitter to provide insight about the interest of our data curation approach and environment
Rabah, Mazouzi. "Approches collaboratives pour la classification des données complexes." Thesis, Paris 8, 2016. http://www.theses.fr/2016PA080079.
Повний текст джерелаThis thesis focuses on the collaborative classification in the context of complex data, in particular the context of Big Data, we used some computational paradigms to propose new approaches based on HPC technologies. In this context, we aim at offering massive classifiers in the sense that the number of elementary classifiers that make up the multiple classifiers system can be very high. In this case, conventional methods of interaction between classifiers is no longer valid and we had to propose new forms of interaction, where it is not constrain to take all classifiers predictions to build an overall prediction. According to this, we found ourselves faced with two problems: the first is the potential of our approaches to scale up. The second, is the diversity that must be created and maintained within the system, to ensure its performance. Therefore, we studied the distribution of classifiers in a cloud-computing environment, this multiple classifiers system can be massive and their properties are those of a complex system. In terms of diversity of data, we proposed a training data enrichment approach for the generation of synthetic data from analytical models that describe a part of the phenomenon studied. so, the mixture of data reinforces learning classifiers. The experimentation made have shown the great potential for the substantial improvement of classification results
Jlassi, Aymen. "Optimisation de la gestion des ressources sur une plate-forme informatique du type Big Data basée sur le logiciel Hadoop." Thesis, Tours, 2017. http://www.theses.fr/2017TOUR4042.
Повний текст джерела"Cyres-Group" is working to improve the response time of his clusters Hadoop and optimize how the resources are exploited in its data center. That is, the goals are to finish work as soon as possible and reduce the latency of each user of the system. Firstly, we decide to work on the scheduling problem in the Hadoop system. We consider the problem as the problem of scheduling a set of jobs on a homogeneous platform. Secondly, we decide to propose tools, which are able to provide more flexibility during the resources management in the data center and ensure the integration of Hadoop in Cloud infrastructures without unacceptable loss of performance. Next, the second level focuses on the review of literature. We conclude that, existing works use simple mathematical models that do not reflect the real problem. They ignore the main characteristics of Hadoop software. Hence, we propose a new model ; we take into account the most important aspects like resources management and the relations of precedence among tasks and the data management and transfer. Thus, we model the problem. We begin with a simplistic model and we consider the minimisation of the Cmax as the objective function. We solve the model with mathematical solver CPLEX and we compute a lower bound. We propose the heuristic "LocFirst" that aims to minimize the Cmax. In the third level, we consider a more realistic modelling of the scheduling problem. We aim to minimize the weighted sum of the following objectives : the weighted flow time ( ∑ wjCj) and the makespan (Cmax). We compute a lower bound and we propose two heuristics to resolve the problem
Attasena, Varunya. "Secret sharing approaches for secure data warehousing and on-line analysis in the cloud." Thesis, Lyon 2, 2015. http://www.theses.fr/2015LYO22014/document.
Повний текст джерелаCloud business intelligence is an increasingly popular solution to deliver decision support capabilities via elastic, pay-per-use resources. However, data security issues are one of the top concerns when dealing with sensitive data. Many security issues are raised by data storage in a public cloud, including data privacy, data availability, data integrity, data backup and recovery, and data transfer safety. Moreover, security risks may come from both cloud service providers and intruders, while cloud data warehouses should be both highly protected and effectively refreshed and analyzed through on-line analysis processing. Hence, users seek secure data warehouses at the lowest possible storage and access costs within the pay-as-you-go paradigm.In this thesis, we propose two novel approaches for securing cloud data warehouses by base-p verifiable secret sharing (bpVSS) and flexible verifiable secret sharing (fVSS), respectively. Secret sharing encrypts and distributes data over several cloud service providers, thus enforcing data privacy and availability. bpVSS and fVSS address five shortcomings in existing secret sharing-based approaches. First, they allow on-line analysis processing. Second, they enforce data integrity with the help of both inner and outer signatures. Third, they help users minimize the cost of cloud warehousing by limiting global share volume. Moreover, fVSS balances the load among service providers with respect to their pricing policies. Fourth, fVSS improves secret sharing security by imposing a new constraint: no cloud service provide group can hold enough shares to reconstruct or break the secret. Five, fVSS allows refreshing the data warehouse even when some service providers fail. To evaluate bpVSS' and fVSS' efficiency, we theoretically study the factors that impact our approaches with respect to security, complexity and monetary cost in the pay-as-you-go paradigm. Moreover, we also validate the relevance of our approaches experimentally with the Star Schema Benchmark and demonstrate its superiority to related, existing methods
Hamadache, Clarisse. "Recherche d'effets de microlentille gravitationnelle vers le centre galactique avec les données d'EROS-II." Phd thesis, Université Louis Pasteur - Strasbourg I, 2004. http://tel.archives-ouvertes.fr/tel-00008874.
Повний текст джерелаDumas, feris Barbara Pilar. "Réseaux optiques en mode paquet pour les connexions internes à un centre de données." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2017. http://www.theses.fr/2017IMTA0057/document.
Повний текст джерелаData-center energy consumption is nowadays a major issue. Intra-data-center networking accounts almost for a quarter of the data-center total power consumption. Optical switching technologies could provide higher power efficiency than current solutions based on electrical-packet switching. This work focuses on optical-packet-switched networks for small- and medium-size data centers. It takes part of the EPOC (Energy-Proportional and Opportunistic Computing) project, which main interest consists on reducing the overall power consumption of a data center partially powered by renewable sources. A key assumption is that our data center does not rely on a dedicated storage network, in order to reduce the consumption of those interconnections. In addition, with the aim of being able to turn off some servers according to the workload and the available energy, the bit rate must be close to 100 Gbit/s. We have chosen, after studying the state of the art of data-center interconnects, a purely passive network architecture based on fast-wavelength-tunable transmitters under the name of POPI.We study POPI's limitations due to its components (insertion loss, tuning range and channel spacing). We then propose an extension called E-POPI that allows to increase the number of connected servers by using several transmission bands. For larger data centers, we propose POPI+, a two-stage infrastructure for intra- and inter-rack communications operating in the C and L bands, respectively. The connection between both stages is done via a transparent gateway in one direction and an opaque one in the other. We discuss different control solutions for both stages.The feasibility of these architectures depends on, among other factors, dealing with bit-rate increasing and power losses of a passive interconnect. Coherent long-distance-transmission techniques are not currently suited to data centers. We therefore studied PAM 4 and 8 modulation formats with direct detection. On one hand, by simulation, with different bit rates (up to 112 Gbit/s) and receivers (PIN, APD and SOA-PIN) and, on the other hand, experimentally, at 12 and 18 Gbit/s. We have developed a method for compensating the distortions generated by the different network components. Our method takes into account a good tradeoff between correction accuracy and computation time.Simulation results allow us to determine the amount of insertion loss that may be supported. We then combine these results with the limitations of transmitters-tuning range and channel spacing using multiple of 12.5 GHz slots for dimensioning the proposed architectures. POPI, E-POPI and POPI+ interconnects allow the connection of 48, 99 and 2352 entities, respectively, at 112 Gbit/s. Our assessments take into account a potential dispersion of the characteristics of the main architecture components
Hamadache, Clarisse. "Recherches d'effets de microlentille gravitationnelle vers le centre galactique avec les données d'Eros II." Université Louis Pasteur (Strasbourg) (1971-2008), 2004. https://publication-theses.unistra.fr/public/theses_doctorat/2004/HAMADACHE_Clarisse_2004.pdf.
Повний текст джерелаThe systematic search for gravitational microlensing effect towards the galactic center makes it possible to probe the galactic structure. The thesis work presented here concerns the analysis of all galactic center data collected by the Eros2 experiment during 7 years (1996-2003) : the survey of 66 square degrees located on both sides of the galactic plane has allowed to build the lightcurves of approximately 50 million stars in two filters. Gravitational microlensing events with a duration ranging between 4 days and 500 days and whose maximum magnification is higher than 2. 18 were required ; this makes it possible to select convincing candidates and constitutes an originality compared to the previous analyses (Eros2 and other experiment) where maximum magnification was required to be higher than 1. 34. The analysis revealed 139 microlensing candidates. This sample contains 91 candidates whose source is a clump red giant star with an associated detection efficiency of 56%. The optical depth obtained for the clump red giant sources is (1,79 +/- 0,20). 10e-6. This value is in good agreement with predicted values as well as with the latest result of the Macho group but it is lower than the Ogle and Moa group results which are 2 to 3 times higher than the predicted one. In addition, the large statistics of galactic center data collected by Eros2 made it possible to calculate the optical depth for various galactic latitudes, and to detect the gradient of optical depth expected in galactic models
Lefebvre, Sylvain. "Services de répartition de charge pour le Cloud : application au traitement de données multimédia." Phd thesis, Conservatoire national des arts et metiers - CNAM, 2013. http://tel.archives-ouvertes.fr/tel-01062823.
Повний текст джерелаGuo, Chaopeng. "Allocation de ressources efficace en énergie pour les bases de données dans le cloud." Thesis, Toulouse 3, 2019. http://www.theses.fr/2019TOU30065.
Повний текст джерелаToday a lot of cloud computing and cloud database techniques are adopted both in industry and academia to face the arrival of the big data era. Meanwhile, energy efficiency and energy saving become a major concern in data centers, which are in charge of large distributed systems and cloud databases. However, energy efficiency and service-level agreement of cloud databases are suffering from resource provisioning, resource over-provisioning and resource under-provisioning, namely that there is a gap between resource provided and resource required. Since the usage of cloud database is dynamical, resource of the system should be provided according to its workload. In this thesis, we present our work on energy-efficient resource provisioning for cloud databases that utilizes dynamic voltage and frequency scaling (DVFS) technique to cope with resource provisioning issues. Additionally, a migration approach is introduced to improve the energy efficiency of cloud database systems further. Our contribution can be summarized as follows: At first, the behavior of energy efficiency of the cloud database system under DVFS technique is analyzed. Based on the benchmark result, two frequency selection approaches are proposed. Then, a frequency selection approach with bounded problem is introduced, in which the power consumption and migration cost are treated separately. A linear programming algorithm and a multi-phases algorithm are proposed. Because of the huge solution space, both algorithms are compared within a small case, while the multi-phases algorithm is evaluated with larger cases. Further, a frequency selection approach with optimization problem is introduced, in which the energy consumption for executing the workload and migration cost are handled together. Two algorithms, a genetic based algorithm and a monte carlo tree search based algorithm are proposed. Both algorithms have their pros and cons. At last, a migration approach is introduced to migrate data according to the given frequencies and current data layout. A migration plan can be obtained within polynomial time by the proposed Constrained MHTM algorithm
Sobati, Moghadam Somayeh. "Contributions to Data Privacy in Cloud Data Warehouses." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSE2020.
Повний текст джерелаNowadays, data outsourcing scenarios are ever more common with the advent of cloud computing. Cloud computing appeals businesses and organizations because of a wide variety of benefits such as cost savings and service benefits. Moreover, cloud computing provides higher availability, scalability, and more effective disaster recovery rather than in-house operations. One of the most notable cloud outsourcing services is database outsourcing (Database-as-a-Service), where individuals and organizations outsource data storage and management to a Cloud Service Provider (CSP). Naturally, such services allow storing a data warehouse (DW) on a remote, untrusted CSP and running on-line analytical processing (OLAP).Although cloud data outsourcing induces many benefits, it also brings out security and in particular privacy concerns. A typical solution to preserve data privacy is encrypting data locally before sending them to an external server. Secure database management systems use various encryption schemes, but they either induce computational and storage overhead or reveal some information about data, which jeopardizes privacy.In this thesis, we propose a new secure secret splitting scheme (S4) inspired by Shamir’s secret sharing. S4 implements an additive homomorphic scheme, i.e., additions can be directly computed over encrypted data. S4 addresses the shortcomings of existing approaches by reducing storage and computational overhead while still enforcing a reasonable level of privacy. S4 is efficient both in terms of storage and computing, which is ideal for data outsourcing scenarios that consider the user has limited computation and storage resources. Experimental results confirm the efficiency of S4 in terms of computation and storage overhead with respect to existing solutions.Moreover, we also present new order-preserving schemes, order-preserving indexing (OPI) and wrap-around order-preserving indexing (waOPI), which are practical on cloud outsourced DWs. We focus on the problem of performing range and exact match queries over encrypted data. In contrast to existing solutions, our schemes prevent performing statistical and frequency analysis by an adversary. While providing data privacy, the proposed schemes bear good performance and lead to minimal change for existing software
El, Sibai Rayane. "Sampling, qualification and analysis of data streams." Thesis, Sorbonne université, 2018. http://www.theses.fr/2018SORUS170/document.
Повний текст джерелаAn environmental monitoring system continuously collects and analyzes the data streams generated by environmental sensors. The goal of the monitoring process is to filter out useful and reliable information and to infer new knowledge that helps the network operator to make quickly the right decisions. This whole process, from the data collection to the data analysis, will lead to two keys problems: data volume and data quality. On the one hand, the throughput of the data streams generated has not stopped increasing over the last years, generating a large volume of data continuously sent to the monitoring system. The data arrival rate is very high compared to the available processing and storage capacities of the monitoring system. Thus, permanent and exhaustive storage of data is very expensive, sometimes impossible. On the other hand, in a real world such as sensor environments, the data are often dirty, they contain noisy, erroneous and missing values, which can lead to faulty and defective results. In this thesis, we propose a solution called native filtering, to deal with the problems of quality and data volume. Upon receipt of the data streams, the quality of the data will be evaluated and improved in real-time based on a data quality management model that we also propose in this thesis. Once qualified, the data will be summarized using sampling algorithms. In particular, we focus on the analysis of the Chain-sample algorithm that we compare against other reference algorithms such as probabilistic sampling, deterministic sampling, and weighted sampling. We also propose two new versions of the Chain-sample algorithm that significantly improve its execution time. Data streams analysis is also discussed in this thesis. We are particularly interested in anomaly detection. Two algorithms are studied: Moran scatterplot for the detection of spatial anomalies and CUSUM for the detection of temporal anomalies. We have designed a method that improves the estimation of the start time and end time of the anomaly detected in CUSUM. Our work was validated by simulations and also by experimentation on two real and different data sets: The data issued from sensors in the water distribution network provided as part of the Waves project and the data relative to the bike sharing system (Velib)
Cornejo-Ramirez, Mario. "Security for the cloud." Thesis, Paris Sciences et Lettres (ComUE), 2016. http://www.theses.fr/2016PSLEE049/document.
Повний текст джерелаCryptography has been a key factor in enabling services and products trading over the Internet. Cloud computing has expanded this revolution and it has become a highly demanded service or utility due to the advantages of high computing power, cheap cost of services, high performance, scalability, accessibility as well as availability. Along with the rise of new businesses, protocols for secure computation have as well emerged. The goal of this thesis is to contribute in the direction of securing existing Internet protocols by providing an analysis of the sources of randomness of these protocols and to introduce better protocols for cloud computing environments. We propose new constructions, improving the efficiency of current solutions in order to make them more accessible and practical. We provide a detailed security analysis for each scheme under reasonable assumptions. We study the security in a cloud computing environment in different levels. On one hand, we formalize a framework to study some popular real-life pseudorandom number generators used in almost every cryptographic application. On the other, we propose two efficient applications for cloud computing. The first allows a user to publicly share its high-entropy secret across different servers and to later recover it by interacting with some of these servers using only his password without requiring any authenticated data. The second, allows a client to securely outsource to a server an encrypted database that can be searched and modified later
Kaced, Yazid. "Études du refroidissement par free cooling indirect d’un bâtiment exothermique : application au centre de données." Thesis, Lorient, 2018. http://www.theses.fr/2018LORIS499/document.
Повний текст джерелаA data center is a warehouse that contains telecommunication equipment, network infrastructure, servers, and computers. This equipment leads to a very high heat dissipation which must be compensated by the use of cooling systems. Telecommunication standards impose restricted climatic ranges (temperatures and humidity) leading to a very high energy consumption devote to air conditioning. The reduction of this energy consumption constitutes a real challenge which should be raised and solved. Many cooling solutions are proposed as the free cooling solution, which consists in cooling equipment by using external air in propitious climatic conditions. The work carried out during this thesis is based on experiments conducted within a building in real climatic conditions in order to study the cooling of telecom cabinets. During this study, the building configuration was modified, an indirect "free cooling" system was set up and a significant instrumentation was implemented. The objectives are to establish performance factors issued from measurements, to develop and to validate a numerical model in order to predict the thermoaeraulic behavior for this type of solution. Initially, experiments are carried out with a power dissipated inside the building and a cooling provided only by an outside air circulation. Then, significant modifications were made into the building to introduce an internal air circulation in a closed loop in order to evacuate the heat dissipated inside cabinets by a crossing airflow. In order to get a convincing database, measurements were conducted by using one and then several cabinets in different conditions. Modifications are made to operating parameters in order to better understand the installation operation and to define the energy optimization parameters. Numerical models are developed through TRNSYS / TRNFLOW. The confrontation of simulations with measurements shows the implemented approach relevance
Ladjel, Riad. "Secure distributed computations for the personal cloud." Electronic Thesis or Diss., université Paris-Saclay, 2020. http://www.theses.fr/2020UPASG043.
Повний текст джерелаThanks to smart disclosure initiatives and new regulations like GDPR, individuals are able to get the control back on their data and store them locally in a decentralized way. In parallel, personal data management system (PDMS) solutions, also called personal clouds, are flourishing. Their goal is to empower users to leverage their personal data for their own good. This decentralized way of managing personal data provides a de facto protection against massive attacks on central servers and opens new opportunities by allowing users to cross their data gathered from different sources. On the other side, this approach prevents the crossing of data from multiple users to perform distributed computations. The goal of this thesis is to design a generic and scalable secure decentralized computing framework which allows the crossing of personal data of multiple users while answering the following two questions raised by this approach. How to preserve individuals' trust on their PDMS when performing global computations crossing data from multiple individuals? And how to guarantee the integrity of the final result when it has been computed by a myriad of collaborative but independent PDMSs?
Marchadier, Elodie Sylviane Germaine. "Etude fonctionnelle d'un centre d'interactions protéiques chez Bacillus subtilis par une approche intégrée." Paris 11, 2009. http://www.theses.fr/2009PA112047.
Повний текст джерелаThe entire complement of proteins expressed by a genome forms the proteome. The proteome is organized in structured networks of protein interactions: the interactome. In these networks, most of the proteins have few interactions whereas a few proteins have many connections: these proteins are called centres of interactions or hubs. This thesis focused on an important biological question: understanding the biological function of a cluster of hubs (CoH), discovered in Bacillus subtilis, and which is located at the interface of several essential cellular processes: DNA replication, cell division, chromosome segregation, stress response and biogenesis of the bacterial cell wall. The partners of the protein of the cluster of hubs were first identified by the technique of two-hybrid in yeast, which helped us to define it rigorously in a network composed of 287 proteins connected by 787 interactions. This network shows many proteins in a new context, thereby facilitate functional analysis of individual proteins and links between the major cellular processes. After conducting a study of the genomic context of genes of the CoH, an integrative biology approach has been initiated by analyzing heterogeneous transcriptome data available in public databases. Statistical analysis of these data identified groups of genes co-regulated with the genes of the cluster of hubs. At first, the analysis of correlations between the expression of genes across various conditions has been performed on the basis of classical statistics such as the unsupervised classification. This first analysis allowed us to associate genes in the CoH to functional groups, to validate and to identify regulons. It also enabled us to highlight the limitations of this approach and the need to resort to methods allowing identification of the conditions in which genes are co-regulated. To this end, we have (i) generated transcriptome data to promote the differential expression of genes coding for proteins CoH and (ii) used bi-clustering methods, to identify groups of genes co -expressed in a wide range of conditions. This led us to identify associations of expression in specific conditions among the genes of the CoH. Therefore, it has been possible to combine two approaches: the study of the transcriptome and the interactome, both of them were conducted in a systematic manner in the whole genome. The integration of these two kinds of data allowed us to clarify the functional context of genes of interest and to make assumptions about the nature of interactions between proteins cluster hub. It appears finally composed of a few groups of co-expressed proteins (party hubs) which can interact together and other proteins expressed in an uncorrelated manner (date hubs). The CoH could form a large group of date hubs whose function could be to ensure the connection between basic cellular processes, whatever the environmental conditions B. Subtilis could be exposed. Generation and processing of such a data set is a major scientific challenge, it require the mobilization of skills, knowledge, and tools to access to a better understanding of living organisms. The constituted data set may be used to implement other statistical methods. All of this will provide methods to ultimately extract information from large data sets which are currently produced. This is the major issue of integrative biology
Jagueneau, Liliane. "Structuration de l'espace linguistique entre Loire et Gironde : analyse dialectométrique des données phonétiques de l'"Atlas linguistique et ethnographique de l'Ouest"." Toulouse 2, 1987. http://www.theses.fr/1987TOU20082.
Повний текст джерелаThis study deals with the geolinguistic structuration of phonetic features between loire and gironde, in the "centre-ouest" (vendee, deux-sevres, vienne, charente-maritime, charente, and some surrounding points) - a boundary area between northern and southern languages of france ("oil" and "oc"). It first presents the phonetic description of this area, derived from the maps of the atlas linguistique et ethnographique de l'ouest (b. Horiot-g. Massignon). And then, the space distribution of these phonetic features is analysed: actually they are neither spread about nor ordered according to strict dialect limits. After the automatic analysis of these data, a new structuration of linguistic space is put forward: on the one hand, the space structuration which results from the cluster analysis of the languages (eighty-two points) is quite similar to the geological one, and partly corresponds to historical, cultural or economic areas; but it always differs from administrative divisions. On the other hand, the cluster analysis of phonetic features reveals a new geolinguistic scheme: these phonetic features are distributed in a "nucleus", and then diffuse into an "area of influence". (theory of geolinguistic nuclei) finally, through the multivariate analysis, attention is drawn to the relations between the points themselves, and to the relations between the points and the phonetic modalities
Tourne, Elise. "Le phénomène de circulation des données à caractère personnel dans le cloud : étude de droit matériel dans le contexte de l'Union européenne." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSE3012/document.
Повний текст джерелаThe legal framework applicable to the gathering and processing by cloud service providers of the personal data of their users raises questions for such users. De facto, there does not now exist an organized legal framework allowing for the regulation, at the European Union level and as a whole, of the flow of personal data in the cloud, whether directly or indirectly. It thus seems necessary to question the way law organized itself consequently and analyze the complementary and/or alternative treatments offered by law, which are less structurally organized and are mosaical, but are more pragmatic, realistic and politically sustainable. Historically, the flow of personal data has been dealt almost exclusively via the specific right to the protection of personal data, which derives from the European Union. Such right, often considered in opposition to the right to the free circulation of data, was initially an emanation of the right to privacy before being established as a fundamental right of the European Union. The treatment provided by the right to the protection of personal data, if it targets directly the data within the flow phenomena, only partly covers such phenomena. In addition, despite the entry into force of the Regulation 2016/679 on the protection of individuals with regard to the processing of personal data and on the free movement of such data, its effectiveness is questionable, not offering any harmonized solution within the European Union and being highly dependent on the goodwill and the financial, organizational and human means of the Member States. The complementary and/or alternative treatments to the right to the protection of personal data that exist within the European Union, which may be allocated among technical, contractual and regulatory tools, only approach the data flow phenomena indirectly by providing a framework to its environment. Individually, they only target one very limited aspect of the data flow phenomena, with more or less effectiveness. Furthermore, technical and contractual tools have not the legitimacy attached to the regulatory tools. However, associated one with another, they allow a more global and efficient targeting of the data flow phenomena
Dumas, Stéphane. "Développement d'un système de veille stratégique dans un centre technique." Aix-Marseille 3, 1994. http://www.theses.fr/1994AIX30063.
Повний текст джерелаMahboubi, Sakina. "Préservation de la confidentialité des données externalisées dans le traitement des requêtes top-k." Thesis, Montpellier, 2018. http://www.theses.fr/2018MONTS026/document.
Повний текст джерелаOutsourcing corporate or individual data at a cloud provider, e.g. using Database-as-a-Service, is practical and cost-effective. But it introduces a major problem: how to preserve the privacy of the outsourced data, while supporting powerful user queries. A simple solution is to encrypt the data before it is outsourced. Then, to answer a query, the user client can retrieve the encrypted data from the cloud, decrypt it, and evaluate the query over plaintext (non encrypted) data. This solution is not practical, as it does not take advantage of the computing power provided by the cloud for evaluating queries.In this thesis, we consider an important kind of queries, top-k queries,and address the problem of privacy-preserving top-k query processing over encrypted data in the cloud.A top-k query allows the user to specify a number k, and the system returns the k tuples which are most relevant to the query. The relevance degree of tuples to the query is determined by a scoring function.We first propose a complete system, called BuckTop, that is able to efficiently evaluate top-k queries over encrypted data, without having to decrypt it in the cloud. BuckTop includes a top-k query processing algorithm that works on the encrypted data, stored at one cloud node,and returns a set that is proved to contain the encrypted data corresponding to the top-k results. It also comes with an efficient filtering algorithm that is executed in the cloud on encypted data and removes most of the false positives included in the set returned.When the outsourced data is big, it is typically partitioned over multiple nodes in a distributed system. For this case, we propose two new systems, called SDB-TOPK and SD-TOPK, that can evaluate top-k queries over encrypted distributed data without having to decrypt at the nodes where they are stored. In addition, SDB-TOPK and SD-TOPK have a powerful filtering algorithm that filters the false positives as much as possible in the nodes, and returns a small set of encrypted data that will be decrypted in the user side. We analyze the security of our system, and propose efficient strategies to enforce it.We validated our solutions through implementation of BuckTop , SDB-TOPK and SD-TOPK, and compared them to baseline approaches over synthetic and real databases. The results show excellent response time compared to baseline approaches. They also show the efficiency of our filtering algorithm that eliminates almost all false positives. Furthermore, our systems yieldsignificant reduction in communication cost between the distributed system nodes when computing the query result
Ahmed-Nacer, Mehdi. "Méthodologie d'évaluation pour les types de données répliqués." Thesis, Université de Lorraine, 2015. http://www.theses.fr/2015LORR0039/document.
Повний текст джерелаTo provide a high availability from any where, at any time, with low latency, data is optimistically replicated. This model allows any replica to apply updates locally, while the operations are later sent to all the others. In this way, all replicas eventually apply all updates, possibly even in different order. Optimistic replication algorithms are responsible for managing the concurrent modifications and ensure the consistency of the shared object. In this thesis, we present an evaluation methodology for optimistic replication algorithms. The context of our study is collaborative editing. We designed a tool that implements our methodology. This tool integrates a mechanism to generate a corpus and a simulator to simulate sessions of collaborative editing. Through this tool, we made several experiments on two different corpus: synchronous and asynchronous. In synchronous collaboration, we evaluate the performance of optimistic replication algorithms following several criteria such as execution time, memory occupation, message's size, etc. After analysis, some improvements were proposed. In addition, in asynchronous collaboration, when replicas synchronize their modifications, more conflicts can appear in the document. In this case, the system cannot merge the modifications until a user resolves them. In order to reduce the conflicts and the user's effort, we propose an evaluation metric and we evaluate the different algorithms on this metric. Afterward, we analyze the quality of the merge to understand the behavior of the users and the collaboration cases that create conflicts. Then, we propose algorithms for resolving the most important conflicts, therefore reducing the user's effort. Finally, we propose a new architecture for supporting cloud-based collaborative editing system. This architecture is based on two optimistic replication algorithms. Unlike current architectures, the proposed one removes the problems of the centralization and consensus between data centers, is simple and accessible for any developers
Liu, Ji. "Gestion multisite de workflows scientifiques dans le cloud." Thesis, Montpellier, 2016. http://www.theses.fr/2016MONTT260/document.
Повний текст джерелаLarge-scale in silico scientific experiments generally contain multiple computational activities to process big data. Scientific Workflows (SWfs) enable scientists to model the data processing activities. Since SWfs deal with large amounts of data, data-intensive SWfs is an important issue. In a data-intensive SWf, the activities are related by data or control dependencies and one activity may consist of multiple tasks to process different parts of experimental data. In order to automatically execute data-intensive SWfs, Scientific Work- flow Management Systems (SWfMSs) can be used to exploit High Performance Computing (HPC) environments provided by a cluster, grid or cloud. In addition, SWfMSs generate provenance data for tracing the execution of SWfs.Since a cloud offers stable services, diverse resources, virtually infinite computing and storage capacity, it becomes an interesting infrastructure for SWf execution. Clouds basically provide three types of services, i.e. Infrastructure-as-a-Service (IaaS), Platform- as-a-Service (PaaS) and Software-as-a-Service (SaaS). SWfMSs can be deployed in the cloud using Virtual Machines (VMs) to execute data-intensive SWfs. With a pay-as-you- go method, the users of clouds do not need to buy physical machines and the maintenance of the machines are ensured by the cloud providers. Nowadays, a cloud is typically made of several sites (or data centers), each with its own resources and data. Since a data- intensive SWf may process distributed data at different sites, the SWf execution should be adapted to multisite clouds while using distributed computing or storage resources.In this thesis, we study the methods to execute data-intensive SWfs in a multisite cloud environment. Some SWfMSs already exist while most of them are designed for computer clusters, grid or single cloud site. In addition, the existing approaches are limited to static computing resources or single site execution. We propose SWf partitioning algorithms and a task scheduling algorithm for SWf execution in a multisite cloud. Our proposed algorithms can significantly reduce the overall SWf execution time in a multisite cloud.In particular, we propose a general solution based on multi-objective scheduling in order to execute SWfs in a multisite cloud. The general solution is composed of a cost model, a VM provisioning algorithm, and an activity scheduling algorithm. The VM provisioning algorithm is based on our proposed cost model to generate VM provisioning plans to execute SWfs at a single cloud site. The activity scheduling algorithm enables SWf execution with the minimum cost, composed of execution time and monetary cost, in a multisite cloud. We made extensive experiments and the results show that our algorithms can reduce considerably the overall cost of the SWf execution in a multisite cloud
Duranthon, Sophie. "Intoxications par les produits agricoles : bilan sur 5 années de données recueillies au centre anti-poison de Bordeaux (1990-1994)." Bordeaux 2, 1996. http://www.theses.fr/1996BOR2P007.
Повний текст джерелаBondiombouy, Carlyna. "Query Processing in Multistore Systems." Thesis, Montpellier, 2017. http://www.theses.fr/2017MONTS056/document.
Повний текст джерелаCloud computing is having a major impact on data management, with a proliferation of new, scalable data management solutions such as distributed file and object storage, NoSQL databases and big data processing frameworks. This also leads to a wide diversification of DBMS interfaces and the loss of a common programming paradigm, making it very hard for a user to integrate its data sitting in specialized data stores, e.g. relational, documents and graph data stores.In this thesis, we address the problem of query processing with multiple cloud data stores, where the data stores have different models, languages and APIs. This thesis has been prepared in the context of the CoherentPaaS European project and, in particular, the CloudMdsQL multistore system. CloudMdsQL is a functional query language able to exploit the full power of local data stores, by simply allowing some local data store native queries to be called as functions, and at the same time be optimized, e.g. by pushing down select predicates, using bind join, performing join ordering, or planning intermediate data shipping.In this thesis, we propose an extension of CloudMdsQL to take full advantage of the functionality of the underlying data processing frameworks such as Spark by allowing the ad-hoc usage of user defined map/filter/reduce (MFR) operators in combination with traditional SQL statements. This allows performing joins between relational and HDFS big data. Our solution allows for optimization by enabling subquery rewriting so that bind join can be used and filter conditions can be pushed down and applied by the data processing framework as early as possible.We validated our solution by implementing the MFR extension as part of the CloudMdsQL query engine. Based on this prototype, we provide an experimental validation of multistore query processing in a cluster to evaluate the impact on performance of optimization. More specifically, we explore the performance benefit of using bind join and select pushdown under different conditions. Overall, our performance evaluation illustrates the CloudMdsQL query engine’s ability to optimize a query and choose the most efficient execution strategy
Carpen-Amarie, Alexandra. "Utilisation de BlobSeer pour le stockage de données dans les Clouds: auto-adaptation, intégration, évaluation." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2011. http://tel.archives-ouvertes.fr/tel-00696012.
Повний текст джерелаDiallo, Thierno. "La fibre en support du Mobile Cloud." Thesis, Limoges, 2016. http://www.theses.fr/2016LIMO0122/document.
Повний текст джерелаIn Europe, the competition between the mobile operators is so strong that the profitability of the mobile network has decreased. The cost to implement, to operate and to upgrade the mobile network is increasing while the revenues generated by the latter are not sufficient. Therefore, the operators should find the way to reduce the CAPEX and the OPEX. To keep competitive, the operators have begun to think about a novel RAN architecture. This new architecture is called Centralized or Cloud Radio Access Network. The traditional antenna site consists of the Radio Remote Head (RRH) which performs the radio processing, and the Base Band Unit (BBU) which carries out the digital processing. The principle of C-RAN consists to move the BBU from antenna site towards the local secured belonging to an operator called Central Office (CO). The move of BBU from antenna site to CO leads to the appearance of a new network segment called fronthaul. During this thesis, the different solutions to the deployment of fronthaul are studied and also the effects and the impacts of jitter on the fronthaul interface
SANCIN, LJUBA. "SEISMIC VULNERABILITY EVALUATION OF R.C. AND MASONRY BUILDINGS IN THE CENTRE OF GORIZIA." Doctoral thesis, Università degli Studi di Trieste, 2021. http://hdl.handle.net/11368/2998137.
Повний текст джерелаThe aim of this research study is to investigate the vulnerability of the building heritage in Gorizia, a town in north-eastern Italy, on the border with Slovenia. This town has not been considered seismic until the year 2003 and then in 2010 it has been classified in a higher seismicity class. For this reason, most of the buildings are not designed to resist seismic action at all and an even lower percentage fulfils the requirements of the current technical standard. Four real existing buildings are analysed as case study buildings, representative of the main structural types that can be found in the town. Two of them are high - rise (11 and 12 storeys) reinforced concrete (RC) framed buildings with a brittle concrete stairwell, designed for gravitational load only and built in the 60’s-70’s. In the last years, a growing attention has been payed to the seismic vulnerability of existing RC framed structures, but this type of buildings, with a core of concrete walls, has been investigated much less, although it is a structural type that is very spread. The other two case studies are masonry buildings built in 1740 and in 1903, respectively. One of the masonry buildings is the city hall of Gorizia, on which many in-situ tests have been performed within a project of the Department of Engineering and Architecture with the Municipality of Gorizia. For both RC buildings, some considerations are made about the influence of the masonry infills on the seismic behaviour of the building and of the numerical model. The vibration periods found with numerical modelling are also compared to the periods evaluated with vibrational measurements. The two numerical models without infills are then analysed with non-linear static and dynamic analyses. The results are processed with a cloud analysis in order to calculate fragility curves of the buildings, that show a very brittle behaviour. The two masonry buildings are analysed also with pushover analysis. For the evaluation of the seismic vulnerability of the analysed buildings, two types of seismic hazard assessments have been considered for the extraction of the seismic inputs: Probabilistic Seismic Hazard Assessment (PSHA) method, by the Italian code response spectra and Neo Deterministic Seismic Hazard Assessment (NDSHA) method, with response spectra of two specific possible scenarios for the town of Gorizia. The physics-based scenarios are calculated for the two faults that are the closest to Gorizia: Idrija and Medea. A comparison is made between the demand given by the seismic inputs defined with the two methods. The importance of using both methods for the design of low-damage retrofitting solutions is highlighted. At last, a theoretical study has been carried out within the present research study in order to find an innovative and effective solution for the retrofit of the RC high-rise brittle buildings. It consists in the application of an exo - or endo - skeleton, with the additional introduction of a sliding system at the base of the RC building, in order to decouple its motion from the ground motion. In this way, the exo- or endo-skeleton can be designed independently from the features of the existing building, that remains undamaged. The characteristics of the exo/endo-skeleton can be calibrated on the seismic input of the site of interest, with the possibility to adapt it to new seismic classifications of the territory.
Muresan, Adrian. "Ordonnancement et déploiement d'applications de gestion de données à grande échelle sur des plates-formes de type Clouds." Phd thesis, Ecole normale supérieure de lyon - ENS LYON, 2012. http://tel.archives-ouvertes.fr/tel-00793092.
Повний текст джерелаChihoub, Houssem Eddine. "Managing consistency for big data applications : tradeoffs and self-adaptiveness." Thesis, Cachan, Ecole normale supérieure, 2013. http://www.theses.fr/2013DENS0059/document.
Повний текст джерелаIn the era of Big Data, data-intensive applications handle extremely large volumes of data while requiring fast processing times. A large number of such applications run in the cloud in order to benefit from cloud elasticity, easy on-demand deployments, and cost-efficient Pays-As-You-Go usage. In this context, replication is an essential feature in the cloud in order to deal with Big Data challenges. Therefore, replication therefore, enables high availability through multiple replicas, fast data access to local replicas, fault tolerance, and disaster recovery. However, replication introduces the major issue of data consistency across different copies. Consistency management is a critical for Big Data systems. Strong consistency models introduce serious limitations to systems scalability and performance due to the required synchronization efforts. In contrast, weak and eventual consistency models reduce the performance overhead and enable high levels of availability. However, these models may tolerate, under certain scenarios, too much temporal inconsistency. In this Ph.D thesis, we address this issue of consistency tradeoffs in large-scale Big Data systems and applications. We first, focus on consistency management at the storage system level. Accordingly, we propose an automated self-adaptive model (named Harmony) that scale up/down the consistency level at runtime when needed in order to provide as high performance as possible while preserving the application consistency requirements. In addition, we present a thorough study of consistency management impact on the monetary cost of running in the cloud. Hereafter, we leverage this study in order to propose a cost efficient consistency tuning (named Bismar) in the cloud. In a third direction, we study the consistency management impact on energy consumption within the data center. According to our findings, we investigate adaptive configurations of the storage system cluster that target energy saving. In order to complete our system-side study, we focus on the application level. Applications are different and so are their consistency requirements. Understanding such requirements at the storage system level is not possible. Therefore, we propose an application behavior modeling that apprehend the consistency requirements of an application. Based on the model, we propose an online prediction approach- named Chameleon that adapts to the application specific needs and provides customized consistency