Relevant bibliographies by topics / Multi-cloud Data

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Multi-cloud Data'

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Published: 7 June 2025
Last updated: 17 July 2025

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Journal articles on the topic "Multi-cloud Data"

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Dixit, Rucha. "Multi-Cloud for Improving Cloud Data Security." International Journal for Research in Applied Science and Engineering Technology V, no. XI (2017): 2696–700. http://dx.doi.org/10.22214/ijraset.2017.11371.

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Yadavalli, Tulasiram. "Managing Data Sovereignty and Compliance in Multi-Cloud Environments." International Journal of Science and Research (IJSR) 11, no. 10 (2022): 1473–76. https://doi.org/10.21275/sr221013093535.

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Researcher. "DATA GRAVITY IN MULTI-CLOUD: STRATEGIES FOR AI-POWERED DATA PLACEMENT AND RETRIEVAL." International Journal of Computer Engineering and Technology (IJCET) 15, no. 6 (2024): 845–53. https://doi.org/10.5281/zenodo.14243885.

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This paper seeks to examine how artificial intelligence (AI) can help organizations avoid the pitfalls of data gravity issues for organizations using multi-cloud systems. AI, thus can help in efficient placement and retrieval of data, improving system performance as well as achieving low latency across different cloud platforms. The examine concerns AI-based approaches for optimising data management, maintaining business operations and mitigating risks and compliance in the multi-cloud environment. The results show how technology Lid organizations are able to handle differentiation data better than it used, promoting adaptability, compatibility and utilization. Lastly, AI becomes a critical game-changer in the modern multi-cloud data management for enterprises.
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Munde, Amit V., and Dr Pranjali P. Deshmukh. "Multi Cloud Data Hosting with SIC Architecture." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (2022): 1830–33. http://dx.doi.org/10.22214/ijraset.2022.40999.

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Abstract: Data hosting on cloud decreases cost of IT maintenance and data reliability get enhance. Nowadays, customers can store their data on single cloud, which has some drawbacks. First is vendor lock in problem and second is security on cloud. The solution to this problem is to store the data on different cloud server without redundancy using encryption algorithm. Customers do not want to lose their sensitive data on cloud. Another issue of cloud computing is data thievery should be overcome to supply higher service. Multi-cloud environment has ability to scale back security risks. To avoid security risk we offer framework. Keywords: Cloud computing, cloud storage, data hosting, data intrusion, multi-cloud, single cloud.
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Kamal, Firas Qays, and Ahlam Abbas Betti. "Towards securing cloud data in the multi-cloud scenario." Periodicals of Engineering and Natural Sciences (PEN) 9, no. 2 (2021): 868. http://dx.doi.org/10.21533/pen.v9i2.1917.

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Suresh, K., and Kannan R. Jagadeesh. "Review of Advancements in Multi-tenant Framework in Cloud Computing." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 3 (2018): 1102–8. https://doi.org/10.11591/ijeecs.v11.i3.pp1102-1108.

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As the cloud computing is gaining more user base the problem of simultaneously catering computational resources to multitude of users or their application is on rise. It remains a critical problem and pose hindrance in scalability of cloud computing. Thus, in order to layout the proper solution for the mentioned problem; it is necessary to sum up a proper knowledge based of the existing solution, there drawbacks and a detail analysis of its performances. In this study we present a review of multi-tenant frameworks and approaches used in the industry which reaps advantages to facilitate multi-tenancy.
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Chandrakanth, Lekkala. "Best Practices for Data Governance and Security in a Multi-Cloud Environment." Journal of Scientific and Engineering Research 8, no. 12 (2021): 227–32. https://doi.org/10.5281/zenodo.11489200.

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Multi-cloud computing is one of the technologies that has received rapid adoption over the years as many organizations seek to leverage its benefits. From cost reduction to offering scalability, cloud computing has many benefits to offer to the businesses that integrate it into their operations. That said, the effective use of this technology calls for users to apply the best practices when it comes to data governance and security. This paper will explore the said best practices.
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Lalitha, Singh* Prof. Jyoti Malhotra Prof. Sayalee Narkhede. "SECURE DATA STORAGE IN MULTI CLOUD ENVIRONMENT USING APACHE HADOOP." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 5 (2016): 126–33. https://doi.org/10.5281/zenodo.51006.

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Cloud computing and Hadoop has become a new distributed storage model for most of the organizations, Industries etc. It provides a pay per use model in which customer has to only pay for the data he is storing on the cloud. However, relying on a single cloud storage provider has resulted in problems like vendor lock in. Therefore multi cloud environment is used to address the security and the data availability problems. In this paper, we proposed a system that uses Hadoop computing platform in multi cloud domain for storing customer’s data reliably. Hadoop is used to conquer single point of failure problem which has been main issue in centralized environment as well as to deal with remote uploading.
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GAYRATJANOVNA, AVAZOVA GULNAZA. "MODEL IN MULTI-CLOUD TELECOMMUNICATIONS NETWORKS." International Journal of Advance Scientific Research 4, no. 3 (2024): 55–58. http://dx.doi.org/10.37547/ijasr-04-03-12.

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This articleaddresses the critical challenge of developing an effective data protection model tailored for multi-cloud telecommunications networks, a pressing need in the era of ubiquitous cloud computing and escalating cybersecurity threats. As telecommunications infrastructure increasingly relies on multi-cloud environments to deliver services, traditional security models fall short, necessitating innovative approaches to protect sensitive data across dispersed cloud platforms.
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Baladari, Venkata. "Cloud Resiliency Engineering: Best Practices for Ensuring High Availability in Multi-Cloud Architectures." International Journal of Science and Research (IJSR) 11, no. 6 (2022): 2062–67. https://doi.org/10.21275/SR220610115023.

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Ensuring cloud resiliency through engineering is essential for maintaining high availability, fault tolerance, and disaster recovery within contemporary cloud infrastructures. As more businesses move towards multi - cloud environments, maintaining system reliability and efficiency while also controlling costs takes centre stage. This study delves into optimal strategies for bolstering cloud reliability via automated failover systems, real - time data duplication, load distribution, and self - restoring networks. The analysis focuses on strategies for disaster recovery, cost - effective resource management, and enhancing security resilience to minimize potential risks.The report draws attention to the difficulties involved in integrating multiple cloud systems, maintaining data consistency, and dealing with cyber threats. It also explores the development of new technologies like AI - powered automation, edge computing, and predictive analytics for identifying potential failures. The study offers valuable insights into how to optimally configure cloud infrastructure to achieve the highest levels of efficiency and dependability. Future developments in autonomous cloud systems, quantum encryption, and eco-friendly computing models to enhance cloud robustness. This paper provides a detailed guide for companies seeking to construct reliable cloud infrastructure that maintains operational stability and reduces the frequency of service interruptions.
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Dissertations / Theses on the topic "Multi-cloud Data"

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Fan, Qi. "Multi-Objective Optimization for Data Analytics in the Cloud." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAX069.

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Le traitement des requêtes Big Data est devenu de plus en plus important, ce qui a conduit au développement et au déploiement dans le cloud de nombreux systèmes. Cependant, le réglage automatique des nombreux paramètres de ces systèmes Big Data introduit une complexité croissante pour répondre aux objectifs de performance et aux contraintes budgétaires des utilisateurs. La détermination des configurations optimales est un défi en raison de la nécessité de prendre en compte : 1) plusieurs objectifs de performances et contraintes budgétaires concurrents, tels qu'une faible latence et un faible coût, 2) un espace de paramètres de grande dimension avec un contrôle de paramètres complexe, et 3) l'exigence d'une configuration élevée. efficacité de calcul dans l'utilisation du cloud, généralement en 1 à 2 secondes.Pour relever les défis ci-dessus, cette thèse propose des algorithmes d'optimisation multi-objectifs (MOO) efficaces pour un optimiseur de cloud afin de répondre à divers objectifs des utilisateurs. Il calcule les configurations Pareto optimales pour les requêtes Big Data dans un espace de paramètres de grande dimension tout en respectant des exigences strictes en matière de temps de résolution. Plus précisément, cette thèse présente les contributions suivantes.La première contribution de cette thèse est une analyse comparative des méthodes et solveurs MOO existants, identifiant leurs limites, notamment en termes d'efficacité et de qualité des solutions Pareto, lorsqu'elles sont appliquées à l'optimisation du cloud.La deuxième contribution présente les algorithmes MOO conçus pour calculer les solutions optimales de Pareto pour les étapes de requête, qui sont des unités définies par des limites de mélange. Dans le traitement du Big Data à l’échelle de la production, chaque étape opère dans un espace de paramètres de grande dimension, avec des milliers d’instances parallèles. Chaque instance nécessite des paramètres de ressources déterminés lors de l'affectation à l'une des milliers de machines, comme en témoignent des systèmes comme MaxCompute. Pour atteindre l’optimalité Pareto pour chaque étape de requête, nous proposons une nouvelle approche hiérarchique MOO. Cette méthode décompose le problème MOO au niveau de l'étape en plusieurs problèmes MOO parallèles au niveau de l'instance et dérive efficacement des solutions MOO au niveau de l'étape à partir de solutions MOO au niveau de l'instance. Les résultats de l'évaluation utilisant des charges de travail de production démontrent que notre approche hiérarchique MOO surpasse les méthodes MOO existantes de 4% à 77% en termes de performances et jusqu'à 48% en réduction des coûts tout en fonctionnant dans un délai de 0,02 à 0,23 secondes par rapport aux optimiseurs et planificateurs actuels.Notre troisième contribution vise à atteindre l’optimalité Pareto pour l’ensemble de la requête avec un contrôle plus fin des paramètres. Dans les systèmes Big Data comme Spark, certains paramètres peuvent être ajustés indépendamment pour chaque étape de la requête, tandis que d'autres sont partagés entre toutes les étapes, introduisant ainsi un espace de paramètres de grande dimension et des contraintes complexes. Pour relever ce défi, nous proposons une nouvelle approche appelée MOO hiérarchique avec contraintes (HMOOC). Cette méthode décompose le problème d’optimisation d’un grand espace de paramètres en sous-problèmes plus petits, chacun contraint d’utiliser les mêmes paramètres partagés. Étant donné que ces sous-problèmes ne sont pas indépendants, nous développons des techniques pour générer un ensemble suffisamment large de solutions candidates et les agréger efficacement pour former des solutions Pareto optimales globales. Les résultats de l'évaluation utilisant les benchmarks TPC-H et TPC-DS démontrent que HMOOC surpasse les méthodes MOO existantes, obtenant une amélioration de 4,7% à 54,1% de l'hypervolume et une réduction de 81% à 98,3% du temps de résolution<br>Big data query processing has become increasingly important, prompting the development and cloud deployment of numerous systems. However, automatically tuning the numerous parameters in these big data systems introduces growing complexity in meeting users' performance goals and budgetary constraints. Determining optimal configurations is challenging due to the need to address: 1) multiple competing performance goals and budgetary constraints, such as low latency and low cost, 2) a high-dimensional parameter space with complex parameter control, and 3) the requirement for high computational efficiency in cloud use, typically within 1-2 seconds.To address the above challenges, this thesis proposes efficient multi-objective optimization (MOO) algorithms for a cloud optimizer to meet various user objectives. It computes Pareto optimal configurations for big data queries within a high-dimensional parameter space while adhering to stringent solving time requirements. More specifically, this thesis introduces the following contributions.The first contribution of this thesis is a benchmarking analysis of existing MOO methods and solvers, identifying their limitations, particularly in terms of efficiency and the quality of Pareto solutions, when applied to cloud optimization.The second contribution introduces MOO algorithms designed to compute Pareto optimal solutions for query stages, which are units defined by shuffle boundaries. In production-scale big data processing, each stage operates within a high-dimensional parameter space, with thousands of parallel instances. Each instance requires resource parameters determined upon assignment to one of thousands of machines, as exemplified by systems like MaxCompute. To achieve Pareto optimality for each query stage, we propose a novel hierarchical MOO approach. This method decomposes the stage-level MOO problem into multiple parallel instance-level MOO problems and efficiently derives stage-level MOO solutions from instance-level MOO solutions. Evaluation results using production workloads demonstrate that our hierarchical MOO approach outperforms existing MOO methods by 4% to 77% in terms of performance and up to 48% in cost reduction while operating within 0.02 to 0.23 seconds compared to current optimizers and schedulers.Our third contribution aims to achieve Pareto optimality for the entire query with finer-granularity control of parameters. In big data systems like Spark, some parameters can be tuned independently for each query stage, while others are shared across all stages, introducing a high-dimensional parameter space and complex constraints. To address this challenge, we propose a new approach called Hierarchical MOO with Constraints (HMOOC). This method decomposes the optimization problem of a large parameter space into smaller subproblems, each constrained to use the same shared parameters. Given that these subproblems are not independent, we develop techniques to generate a sufficiently large set of candidate solutions and efficiently aggregate them to form global Pareto optimal solutions. Evaluation results using TPC-H and TPC-DS benchmarks demonstrate that HMOOC outperforms existing MOO methods, achieving a 4.7% to 54.1% improvement in hypervolume and an 81% to 98.3% reduction in solving time
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Jung, Gueyoung. "Multi-dimensional optimization for cloud based multi-tier applications." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37267.

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Emerging trends toward cloud computing and virtualization have been opening new avenues to meet enormous demands of space, resource utilization, and energy efficiency in modern data centers. By being allowed to host many multi-tier applications in consolidated environments, cloud infrastructure providers enable resources to be shared among these applications at a very fine granularity. Meanwhile, resource virtualization has recently gained considerable attention in the design of computer systems and become a key ingredient for cloud computing. It provides significant improvement of aggregated power efficiency and high resource utilization by enabling resource consolidation. It also allows infrastructure providers to manage their resources in an agile way under highly dynamic conditions. However, these trends also raise significant challenges to researchers and practitioners to successfully achieve agile resource management in consolidated environments. First, they must deal with very different responsiveness of different applications, while handling dynamic changes in resource demands as applications' workloads change over time. Second, when provisioning resources, they must consider management costs such as power consumption and adaptation overheads (i.e., overheads incurred by dynamically reconfiguring resources). Dynamic provisioning of virtual resources entails the inherent performance-power tradeoff. Moreover, indiscriminate adaptations can result in significant overheads on power consumption and end-to-end performance. Hence, to achieve agile resource management, it is important to thoroughly investigate various performance characteristics of deployed applications, precisely integrate costs caused by adaptations, and then balance benefits and costs. Fundamentally, the research question is how to dynamically provision available resources for all deployed applications to maximize overall utility under time-varying workloads, while considering such management costs. Given the scope of the problem space, this dissertation aims to develop an optimization system that not only meets performance requirements of deployed applications, but also addresses tradeoffs between performance, power consumption, and adaptation overheads. To this end, this dissertation makes two distinct contributions. First, I show that adaptations applied to cloud infrastructures can cause significant overheads on not only end-to-end response time, but also server power consumption. Moreover, I show that such costs can vary in intensity and time scale against workload, adaptation types, and performance characteristics of hosted applications. Second, I address multi-dimensional optimization between server power consumption, performance benefit, and transient costs incurred by various adaptations. Additionally, I incorporate the overhead of the optimization procedure itself into the problem formulation. Typically, system optimization approaches entail intensive computations and potentially have a long delay to deal with a huge search space in cloud computing infrastructures. Therefore, this type of cost cannot be ignored when adaptation plans are designed. In this multi-dimensional optimization work, scalable optimization algorithm and hierarchical adaptation architecture are developed to handle many applications, hosting servers, and various adaptations to support various time-scale adaptation decisions.
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Schmidt, Eric Otto. "Cloud properties as inferred from HIRS/2 multi-spectral data." Diss., Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/26817.

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Xhagjika, Vamis. "Resource, data and application management for cloud federations and multi-clouds." Doctoral thesis, Universitat Politècnica de Catalunya, 2017. http://hdl.handle.net/10803/409728.

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Distributed Real-Time Media Processing refers to classes of highly distributed, delay no-tolerant applications that account for the majority of the data traffic generated in the world today. Real-Time audio/video conferencing and live content streaming are of particular research interests as technology forecasts predict video traffic surpassing every other type of data traffic in the world in the near future. These applications are very sensitive to both communication properties such as latency, jitter, packet loss, bit rate as well as backend stream processing load profiles. In this work we provide a novel and generalized large-scale Multi-Cloud architectural blueprint for ISP and Carrier providers, that permits smart geo-distributed service placement in order to optimize latency/locality of stream processing applications. We provide as a well self-managed Intra-Cloud federation algorithm based on gradient topologies in order to optimize routes in a live media streaming backend. Additionally we introduce a novel distributed Network Bandwidth Manager that optimizes system stability by arbitrating network bandwidth between multiple Cloud services sharing the same network infrastructure. At last, an empirical study is provided connecting media quality parameters and Cloud backend load profiles, including an algorithm for stream allocation on Cloud Selective Forwarding units.<br>El procesamiento de medios en tiempo real distribuido se refiere a clases de aplicaciones altamente distribuidas, no tolerantes al retardo, que representan la mayoría del tráfico de datos generado en el mundo actual. Las conferencias de audio y video en tiempo real y la transmisión de contenido en vivo tienen especial interés en investigación, ya que la prospectiva tecnológica estima que el tráfico de video supere a cualquier otro tipo de tráfico de datos en el futuro cercano. La transmisión en vivo se refiere a aplicaciones en las que flujos de audio/vídeo de una fuente se han de entregar a un conjunto de destinos en lugares geográficos diferentes mientras se mantiene baja la latencia de entrega del flujo (como por ejemplo la cobertura de eventos en vivo). Las plataformas de conferencia en tiempo real son plataformas de aplicación que implementan comunicaciones de audio/video en tiempo real entre muchos participantes. Ambas categorías presentan una alta sensibilidad tanto al estado de la red (latencia, jitter, pérdida de paquetes, velocidad de bits) como a los perfiles de carga de la infraestructura de procesamiento de flujo (latencia y jitter introducidos durante el procesamiento en la nube de paquetes de datos multimedia). Esta tesis trata de mejorar el procesamiento de datos multimedia en tiempo real tanto en los parámetros de nivel de red como en las optimizaciones en la nube.
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Mohamad, Baraa. "Medical Data Management on the cloud." Thesis, Clermont-Ferrand 2, 2015. http://www.theses.fr/2015CLF22582.

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Résumé indisponible<br>Medical data management has become a real challenge due to the emergence of new imaging technologies providing high image resolutions.This thesis focuses in particular on the management of DICOM files. DICOM is one of the most important medical standards. DICOM files have special data format where one file may contain regular data, multimedia data and services. These files are extremely heterogeneous (the schema of a file cannot be predicted) and have large data sizes. The characteristics of DICOM files added to the requirements of medical data management in general – in term of availability and accessibility- have led us to construct our research question as follows:Is it possible to build a system that: (1) is highly available, (2) supports any medical images (different specialties, modalities and physicians’ practices), (3) enables to store extremely huge/ever increasing data, (4) provides expressive accesses and (5) is cost-effective .In order to answer this question we have built a hybrid (row-column) cloud-enabled storage system. The idea of this solution is to disperse DICOM attributes thoughtfully, depending on their characteristics, over both data layouts in a way that provides the best of row-oriented and column-oriented storage models in one system. All with exploiting the interesting features of the cloud that enables us to ensure the availability and portability of medical data. Storing data on such hybrid data layout opens the door for a second research question, how to process queries efficiently over this hybrid data storage with enabling new and more efficient query plansThe originality of our proposal comes from the fact that there is currently no system that stores data in such hybrid storage (i.e. an attribute is either on row-oriented database or on column-oriented one and a given query could interrogate both storage models at the same time) and studies query processing over it.The experimental prototypes implemented in this thesis show interesting results and opens the door for multiple optimizations and research questions
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Pagliari, Alessio. "Network as an On-Demand Service for Multi-Cloud Workloads." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.

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The PrEstoCloud project aims to enable on-demand resource scaling of Big Data applications to the cloud. In this context, we have to deal with the huge amount of data processed and more, in particular, its transportation between one cloud and another. The scope of this thesis is to develop a network-level architecture that could easily deal with Big Data application challenges and could be integrated into the PrEstoCloud consortium staying transparent to the application level. However, the connection between multiple cloud providers in this context presents a series of challenges: the architecture should adapt to the variable number of clouds to connect, it have to bypass the limitations of the cloud infrastructure and most importantly, it must have a general design able to work in every cloud provider. In this report, we present a general VPN-based Inter-Cloud architecture able to work in every kind of environment. We implemented a prototype with IPSec and OpenVPN, connecting the i3s laboratory with Amazon AWS and Azure, we evaluate our architecture and the used tools in two ways: (i) we test the stability over time of the architecture via latency tests; (ii) we perform non-intrusive Pathload tests in the Amazon, showing the usability of the available bandwidth estimator in the cloud, the AWS network characteristics discovered through the tests and a final comparison of the VPN tools overhead.
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Xu, Zichen. "Energy Modeling and Management for Data Services in Multi-Tier Mobile Cloud Architectures." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1468272637.

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de, Carvalho Tiago Filipe Rodrigues. "Integrated Approach to Dynamic and Distributed Cloud Data Center Management." Research Showcase @ CMU, 2016. http://repository.cmu.edu/dissertations/739.

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Management solutions for current and future Infrastructure-as-a-Service (IaaS) Data Centers (DCs) face complex challenges. First, DCs are now very large infrastructures holding hundreds of thousands if not millions of servers and applications. Second, DCs are highly heterogeneous. DC infrastructures consist of servers and network devices with different capabilities from various vendors and different generations. Cloud applications are owned by different tenants and have different characteristics and requirements. Third, most DC elements are highly dynamic. Applications can change over time. During their lifetime, their logical architectures evolve and change according to workload and resource requirements. Failures and bursty resource demand can lead to unstable states affecting a large number of services. Global and centralized approaches limit scalability and are not suitable for large dynamic DC environments with multiple tenants with different application requirements. We propose a novel fully distributed and dynamic management paradigm for highly diverse and volatile DC environments. We develop LAMA, a novel framework for managing large scale cloud infrastructures based on a multi-agent system (MAS). Provider agents collaborate to advertise and manage available resources, while app agents provide integrated and customized application management. Distributing management tasks allows LAMA to scale naturally. Integrated approach improves its efficiency. The proximity to the application and knowledge of the DC environment allow agents to quickly react to changes in performance and to pre-plan for potential failures. We implement and deploy LAMA in a testbed server cluster. We demonstrate how LAMA improves scalability of management tasks such as provisioning and monitoring. We evaluate LAMA in light of state-of-the-art open source frameworks. LAMA enables customized dynamic management strategies to multi-tier applications. These strategies can be configured to respond to failures and workload changes within the limits of the desired SLA for each application.
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Liu, Kun. "Multi-View Oriented 3D Data Processing." Thesis, Université de Lorraine, 2015. http://www.theses.fr/2015LORR0273/document.

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Le raffinement de nuage de points et la reconstruction de surface sont deux problèmes fondamentaux dans le traitement de la géométrie. La plupart des méthodes existantes ont été ciblées sur les données de capteur de distance et se sont avérées être mal adaptées aux données multi-vues. Dans cette thèse, deux nouvelles méthodes sont proposées respectivement pour les deux problèmes avec une attention particulière aux données multi-vues. La première méthode permet de lisser les nuages de points provenant de la reconstruction multi-vue sans endommager les données. Le problème est formulé comme une optimisation non-linéaire sous contrainte et ensuite résolu par une série de problèmes d’optimisation sans contrainte au moyen d’une méthode de barrière. La seconde méthode effectue une triangulation du nuage de points d’entrée pour générer un maillage en utilisant une stratégie de l’avancement du front pilotée par un critère de l’empilement compact de sphères. L’algorithme est simple et permet de produire efficacement des maillages de haute qualité. Les expérimentations sur des données synthétiques et du monde réel démontrent la robustesse et l’efficacité des méthodes proposées. Notre méthodes sont adaptées aux applications qui nécessitent des informations de position précises et cohérentes telles que la photogrammétrie et le suivi des objets en vision par ordinateur<br>Point cloud refinement and surface reconstruction are two fundamental problems in geometry processing. Most of the existing methods have been targeted at range sensor data and turned out be ill-adapted to multi-view data. In this thesis, two novel methods are proposed respectively for the two problems with special attention to multi-view data. The first method smooths point clouds originating from multi-view reconstruction without impairing the data. The problem is formulated as a nonlinear constrained optimization and addressed as a series of unconstrained optimization problems by means of a barrier method. The second method triangulates point clouds into meshes using an advancing front strategy directed by a sphere packing criterion. The method is algorithmically simple and can produce high-quality meshes efficiently. The experiments on synthetic and real-world data have been conducted as well, which demonstrates the robustness and the efficiency of the methods. The developed methods are suitable for applications which require accurate and consistent position information such photogrammetry and tracking in computer vision
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Breschi, Valentina. "Model learning from data: from centralized multi-model regression to distributed cloud-aided single-model estimation." Thesis, IMT Alti Studi Lucca, 2018. http://e-theses.imtlucca.it/256/1/Breschi_phdthesis.pdf.

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This thesis presents a collection of methods for learning models from data, looking at this problem from two perspectives: learning multiple models from a single data source and how to switch among them, and learning a single model from data collected from multiple sources. Regarding the first, to describe complex phenomena with simple but yet complete models, we propose a computationally efficient method for Piecewise Affine (PWA) regression. This approach relies on the combined use (i) multi-model Recursive Least-Squares (RLS) and (ii) piecewise linear multi- category discrimination, and shows good performances when used for the identification of Piecewise Affine dynamical systems with eXogenous inputs (PWARX) and Linear Parameter Varying (LPV) models. The technique for PWA regression is then extended to handle the problem of black-box identification of Discrete Hybrid Automata (DHA) from input/output observations, with hidden operating modes. The method for DHA identification is based on multi-model RLS and multicategory discrimination and it can approximate both the continuous affine dynamics and the Finite State Machine (FSM) governing the logical dynamics of the DHA. Two more approaches are presented to tackle the problem of learning models that jump over time. While the technique designed to learn Rarely Jump Models (RJMs) from data relies on the combined solution of a convex optimization problem and the use of Dynamic Programming, the method proposed for Markov Jump Models (MJMs) learning is based on the joint use of clustering plus multi-model RLS and a probabilistic clustering technique. The results of the tests performed on the method for RJMs learning have motivated the design of two techniques for Non-Intrusive Load Monitoring, i.e., to estimate the power consumed by the appliances in an household from aggregated measurements, which are also presented in the thesis. In particular, methods based on (i) the optimization of a least-square error cost function, modified to account for the changes in the appliances operating regime, and relying on (ii) multi-model Kalman filters are proposed. Regarding the second perspective, we propose methods for cloud-aided consensus-based parameter estimation over a multitude of similar devices (such as a mass production). In particular, we focus on the design of RLS-based estimators, which allow to handle (i) linear and (ii) nonlinear consensus constraints and (iii) multi-class estimation.
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Books on the topic "Multi-cloud Data"

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Narasayya, Vivek, and Surajit Chaudhuri. Cloud Data Services: Workloads, Architectures and Multi-Tenancy. Now Publishers, 2021.

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Multi-Cloud Architecture and Governance: Leverage Azure, AWS, GCP, and VMware VSphere to Build Effective Multi-Cloud Solutions. de Gruyter GmbH, Walter, 2020.

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Multi-Cloud Architecture and Governance: Leverage Azure, AWS, GCP, and VMware VSphere to Build Effective Multi-Cloud Solutions. Packt Publishing, Limited, 2020.

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Paz, José Rolando Guay. Microsoft Azure Cosmos DB Revealed: A Multi-Model Database Designed for the Cloud. Apress, 2018.

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Schaffner, Jan. Multi Tenancy for Cloud-Based In-Memory Column Databases: Workload Management and Data Placement. Springer, 2013.

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Schaffner, Jan. Multi Tenancy for Cloud-Based In-Memory Column Databases: Workload Management and Data Placement. Springer, 2015.

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Schaffner, Jan. Multi Tenancy for Cloud-Based In-Memory Column Databases: Workload Management and Data Placement (In-Memory Data Management Research). Springer, 2013.

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Nitto, Elisabetta Di, Dana Petcu, and Peter Matthews. Model-Driven Development and Operation of Multi-Cloud Applications: The MODAClouds Approach. Saint Philip Street Press, 2020.

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Architecting a Modern Data Warehouse for Large Enterprises: Build a Multi-Cloud-Based Modern Distributed Data Warehouse with Azure and AWS. Apress L. P., 2023.

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Smiraglia, Richard P., and Andrea Scharnhorst, eds. Linking Knowledge. Ergon – ein Verlag in der Nomos Verlagsgesellschaft, 2021. http://dx.doi.org/10.5771/9783956506611.

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The growth and population of the Semantic Web, especially the Linked Open Data (LOD) Cloud, has brought to the fore the challenges of ordering knowledge for data mining on an unprecedented scale. The LOD Cloud is structured from billions of elements of knowledge and pointers to knowledge organization systems (KOSs) such as ontologies, taxonomies, typologies, thesauri, etc. The variant and heterogeneous knowledge areas that comprise the social sciences and humanities (SSH), including cultural heritage applications are bringing multi-dimensional richness to the LOD Cloud. Each such application arrives with its own challenges regarding KOSs in the Cloud. With contributions by Sören Auer, Gerard Coen, Kathleen Gregory, Mohamad Yaser Jaradeh, Daniel Martínez Ávila, Philipp Mayr, Allard Oelen, Cristina Pattuelli, Tobias Renwick, Andrea Scharnhorst, Ronald Siebes, Aida Slavic, Richard P Smiraglia, Markus Stocker, Rick Szostak, Marnix van Berchum, Charles van den Heuvel, J. Bradford Young, Veruska Zamborlini and Marcia Zeng.
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Book chapters on the topic "Multi-cloud Data"

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Thenmozhi, K., M. Pyingkodi, and K. Ramesh. "Hybrid Machine Learning Models for Distributed Biological Data in Multi-Cloud Environment." In Operationalizing Multi-Cloud Environments. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74402-1_2.

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de Oliveira, Daniel C. M., Ji Liu, and Esther Pacitti. "Workflow Execution in a Multi-Site Cloud." In Data-Intensive Workflow Management. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-031-01872-5_4.

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Hiriyannaiah, Srinidhi, G. M. Siddesh, and K. G. Srinivasa. "Neural Networks for Multi-Modal Data Analytics." In Cloud-based Multi-Modal Information Analytics. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003215974-7.

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Hiriyannaiah, Srinidhi, G. M. Siddesh, and K. G. Srinivasa. "Multi-Modal Data Analytics and Lifecycle using Cloud." In Cloud-based Multi-Modal Information Analytics. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003215974-2.

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Le, Meixing, Krishna Kant, and Sushil Jajodia. "Cooperative Data Access in Multi-cloud Environments." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22348-8_4.

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Wang, Pengwei, Yi Wei, and Zhaohui Zhang. "Optimizing Data Placement in Multi-cloud Environments Considering Data Temperature." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78612-0_14.

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Hong, Cheng, Yifu Li, Min Zhang, and Dengguo Feng. "Fast Multi-keywords Search over Encrypted Cloud Data." In Web Information Systems Engineering – WISE 2016. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48740-3_32.

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Shi, Tao, Hui Ma, and Gang Chen. "Multi-objective Container Consolidation in Cloud Data Centers." In AI 2018: Advances in Artificial Intelligence. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03991-2_71.

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Strizhov, Mikhail, and Indrajit Ray. "Multi-keyword Similarity Search over Encrypted Cloud Data." In ICT Systems Security and Privacy Protection. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55415-5_5.

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Ma, Youzhong, Xiaofeng Meng, Shaoya Wang, Weisong Hu, Xu Han, and Yu Zhang. "An Efficient Index Method for Multi-Dimensional Query in Cloud Environment." In Cloud Computing and Big Data. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28430-9_23.

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Conference papers on the topic "Multi-cloud Data"

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Baulig, Gerald, and Jiun-In Guo. "MIC-OPCC: Multi-Indexed Convolution Model for Octree Point Cloud Compression." In 2025 Data Compression Conference (DCC). IEEE, 2025. https://doi.org/10.1109/dcc62719.2025.00048.

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Yeboah-Ofori, Abel, Alameen Jafar, Toluwaloju Abisogun, Ian Hilton, Waheed Oseni, and Ahmad Musa. "Data Security and Governance in Multi-Cloud Computing Environment." In 2024 11th International Conference on Future Internet of Things and Cloud (FiCloud). IEEE, 2024. http://dx.doi.org/10.1109/ficloud62933.2024.00040.

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Asery, Rakesh, Burungale Swapnil Birudev, and Pankaj Saini. "Multi-Sensor Satellite Image Cloud Classification with Deep Learning." In 2025 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI). IEEE, 2025. https://doi.org/10.1109/icdsaai65575.2025.11011732.

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Banipal, Indervir Singh, and Shubhi Asthana. "Smart System for Multi-Cloud Pathways." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10021041.

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Montes, Javier Diaz, Mengsong Zou, Rahul Singh, Shu Tao, and Manish Parashar. "Data-Driven Workflows in Multi-cloud Marketplaces." In 2014 IEEE 7th International Conference on Cloud Computing (CLOUD). IEEE, 2014. http://dx.doi.org/10.1109/cloud.2014.32.

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Libardi, Rafael M. de O., Stephan Reiff-Marganiec, Luiz Henrique Nunes, Lucas J. Adami, Carlos H. G. Ferreira, and Julio C. Estrella. "MSSF: User-Friendly Multi-cloud Data Dispersal." In 2015 IEEE 8th International Conference on Cloud Computing (CLOUD). IEEE, 2015. http://dx.doi.org/10.1109/cloud.2015.53.

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Wojtowicz, Damien T., Shaoyi Yin, Jorge Martinez-Gil, Franck Morvan, and Abdelkader Hameurlain. "Multi-Cloud Query Optimisation with Accurate and Efficient Quoting." In 2022 IEEE International Conference on Big Data (Big Data). IEEE, 2022. http://dx.doi.org/10.1109/bigdata55660.2022.10020835.

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Rao, M. Varaprasad, G. Vishnu Murthy, and V. Vijaya Kumar. "Multi-Tenancy authorization system in multi cloud services." In 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC). IEEE, 2017. http://dx.doi.org/10.1109/icbdaci.2017.8070873.

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"Multi-cloud and Multi-data Stores - The Challenges Behind Heterogeneous Data Models." In Special Session on Multi-Clouds. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004974607030713.

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Zhu, Zekai, Peiquan Jin, Xiaoliang Wang, Yigui Yuan, and Shouhong Wan. "Adaptive Buffer Replacement Policies for Multi-Tenant Cloud Services." In 2023 6th International Conference on Data Storage and Data Engineering (DSDE). IEEE, 2023. http://dx.doi.org/10.1109/dsde58527.2023.00024.

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Reports on the topic "Multi-cloud Data"

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Dudhia, Jimy. Study of Multi-Scale Cloud Processes Over the Tropical Western Pacific Using Cloud-Resolving Models Constrained by Satellite Data. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1068146.

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Mishra, Aditi. Cloud infrastructure for multi-sensor remote data acquisition system for precision agriculture (CSR-DAQ). Iowa State University, 2020. http://dx.doi.org/10.31274/cc-20240624-369.

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Vesselinov, Velimir, Daniel O'Malley, Luke Frash, et al. Geo Thermal Cloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1782607.

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Gavryliuk, Olga. Comparison of available cloud-based learning technologies for the preparation of Bachelor of Statistics. RVV TSDPU im. V. Vynnychenka, 2019. https://doi.org/10.33407/lib.naes.id/eprint/717067.

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In the article the conceptual-terminological apparatus of the research is presented, such concepts as cloud technologies, cloud services, technologies of training, cloud-oriented learning technologies, competence, professional competence are considered. The advantages of cloud technologies, which consist of the dynamism of the provision of computing resources and software-hardware, the ability to customize it to personal usage needs. Besides, access to multi-sectoral e-learning resources may be provided on a specially set cloud server or placed on other electronic data carriers that are accessible using the Internet (public service). The conditions for defining cloud services are singled out. It is revealed that the structure of professional competence should include: special, social, personal and individual competence.
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Gavryliuk, Olga. The use of cloud-based learning technologies to shape the professional competencies of future bachelors of statistics: a conceptual terminology research tool. National Pedagogical Dragomanov University, 2019. https://doi.org/10.33407/lib.naes.id/eprint/715382.

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In the article the conceptual-terminological apparatus of the research is presented, such concepts as cloud technologies, cloud services, technologies of training, cloud-oriented learning technologies, competence, professional competence are considered. The advantages of cloud technologies, which consist of the dynamism of the provision of computing resources and software-hardware, the ability to customize it to personal usage needs. Besides, access to multi-sectoral e-learning resources may be provided on a specially set cloud server or placed on other electronic data carriers that are accessible using the Internet (public service). The conditions for defining cloud services are singled out. It is revealed that the structure of professional competence should include: special, social, personal and individual competence.
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Semerikov, Serhiy, Illia Teplytskyi, Yuliia Yechkalo, Oksana Markova, Vladimir Soloviev, and Arnold Kiv. Computer Simulation of Neural Networks Using Spreadsheets: Dr. Anderson, Welcome Back. [б. в.], 2019. http://dx.doi.org/10.31812/123456789/3178.

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The authors of the given article continue the series presented by the 2018 paper “Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot”. This time, they consider mathematical informatics as the basis of higher engineering education fundamentalization. Mathematical informatics deals with smart simulation, information security, long-term data storage and big data management, artificial intelligence systems, etc. The authors suggest studying basic principles of mathematical informatics by applying cloud-oriented means of various levels including those traditionally considered supplementary – spreadsheets. The article considers ways of building neural network models in cloud-oriented spreadsheets, Google Sheets. The model is based on the problem of classifying multi-dimensional data provided in “The Use of Multiple Measurements in Taxonomic Problems” by R. A. Fisher. Edgar Anderson’s role in collecting and preparing the data in the 1920s-1930s is discussed as well as some peculiarities of data selection. There are presented data on the method of multi-dimensional data presentation in the form of an ideograph developed by Anderson and considered one of the first efficient ways of data visualization.
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Vesselinov, Velimir. Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration and Development of Hidden Geothermal Resources. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1781345.

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Frash, Luke, Bulbul Ahmmed, Maruti Mudunuru, and Daniel Tartakovsky. Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources. Office of Scientific and Technical Information (OSTI), 2024. https://doi.org/10.2172/2480434.

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Frash, Luke, Bulbul Ahmmed, Maruti Mudunuru, and Daniel Tartakovsky. GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2290287.

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Pasupuleti, Murali Krishna. Securing AI-driven Infrastructure: Advanced Cybersecurity Frameworks for Cloud and Edge Computing Environments. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv225.

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Abstract: The rapid adoption of artificial intelligence (AI) in cloud and edge computing environments has transformed industries by enabling large-scale automation, real-time analytics, and intelligent decision-making. However, the increasing reliance on AI-powered infrastructures introduces significant cybersecurity challenges, including adversarial attacks, data privacy risks, and vulnerabilities in AI model supply chains. This research explores advanced cybersecurity frameworks tailored to protect AI-driven cloud and edge computing environments. It investigates AI-specific security threats, such as adversarial machine learning, model poisoning, and API exploitation, while analyzing AI-powered cybersecurity techniques for threat detection, anomaly prediction, and zero-trust security. The study also examines the role of cryptographic solutions, including homomorphic encryption, federated learning security, and post-quantum cryptography, in safeguarding AI models and data integrity. By integrating AI with cutting-edge cybersecurity strategies, this research aims to enhance resilience, compliance, and trust in AI-driven infrastructures. Future advancements in AI security, blockchain-based authentication, and quantum-enhanced cryptographic solutions will be critical in securing next-generation AI applications in cloud and edge environments. Keywords: AI security, adversarial machine learning, cloud computing security, edge computing security, zero-trust AI, homomorphic encryption, federated learning security, post-quantum cryptography, blockchain for AI security, AI-driven threat detection, model poisoning attacks, anomaly prediction, cyber resilience, decentralized AI security, secure multi-party computation (SMPC).
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