Relevant bibliographies by topics / CLOUD FRAMEWORK

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

Academic literature on the topic 'CLOUD FRAMEWORK'

Author: Grafiati
Published: 11 September 2023
Last updated: 16 April 2024

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Journal articles on the topic "CLOUD FRAMEWORK"

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Ormel, Chris W., and Michiel Min. "ARCiS framework for exoplanet atmospheres." Astronomy & Astrophysics 622 (February 2019): A121. http://dx.doi.org/10.1051/0004-6361/201833678.

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Context. Understanding of clouds is instrumental in interpreting current and future spectroscopic observations of exoplanets. Modeling clouds consistently is complex, since it involves many facets of chemistry, nucleation theory, condensation physics, coagulation, and particle transport. Aims. We aim to develop a simple physical model for cloud formation and transport, efficient and versatile enough that it can be used, in modular fashion for parameter optimization searches of exoplanet atmosphere spectra. In this work we present the cloud model and investigate the dependence of key parameters as the cloud diffusivity K and the nuclei injection rate Σn. on the planet’s observational characteristics. Methods. The transport equations are formulated in 1D, accounting for sedimentation and diffusion. The grain size is obtained through a moment method. For simplicity, only one cloud species is considered and the nucleation rate is parametrized. From the resulting physical profiles we simulate transmission spectra covering the visual to mid-IR wavelength range. Results. We apply our models toward KCl clouds in the atmosphere of GJ1214 b and toward MgSiO3 clouds of a canonical hot-Jupiter. We find that larger K increases the thickness of the cloud, pushing the τ = 1 surface to a lower pressure layer higher in the atmosphere. A larger nucleation rate also increases the cloud thickness while it suppresses the grain size. Coagulation is most important at high Σn. and low K. We find that the investigated combinations of K and Σn. greatly affect the transmission spectra in terms of the slope at near-IR wavelength (a proxy for grain size), the molecular features seen at approximately μm (which disappear for thick clouds, high in the atmosphere), and the 10 μm silicate feature, which becomes prominent for small grains high in the atmosphere. Conclusions. Clouds have a major impact on the atmospheric characteristics of hot-Jupiters, and models as those presented here are necessary to reveal the underlying properties of exoplanet atmospheres. The result of our hybrid approach – aimed to provide a good balance between physical consistency and computational efficiency – is ideal toward interpreting (future) spectroscopic observations of exoplanets.
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Chang, Victor, Yen-Hung Kuo, and Muthu Ramachandran. "Cloud computing adoption framework: A security framework for business clouds." Future Generation Computer Systems 57 (April 2016): 24–41. http://dx.doi.org/10.1016/j.future.2015.09.031.

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Zhao, X., Z. Gao, W. Sun, and F. Wen. "A COARSE-TO-FINE BAND REGISTRATION FRAMEWORK FOR MULTI/HYPERSPECTRAL REMOTE SENSING IMAGES CONSIDERING CLOUD INFLUENCE." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2020 (August 3, 2020): 201–8. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-201-2020.

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Abstract. Band registration is one of the most critical steps in the production of multi/hyperspectral images and determines the accuracy of applications directly. Because of the characteristics of imaging devices in some multi/hyperspectral satellites, there may be a time difference between bands during push-broom imaging, which leads to displacements of moving clouds with respect to the ground. And a large number of feature points may gather around cloud contours due to the high contrast and rich texture, resulting in building a transformation more suitable for moving clouds and making ground objects ghosted and blurred. This brings a big challenge for registration methods based on feature extraction and matching. In this paper, we propose a novel coarse-to-fine band registration framework for multi/hyperspectral images containing moving clouds. In the coarse registration stage, a cloud mask is generated by grayscale stretching, morphology and other operations. Based on this mask, a coarse matching of cloud-free regions is performed to eliminate large misalignment between bands. In the refinement stage, low-rank analysis and RASL (Robust Alignment by Sparse and Low-rank decomposition) are used to optimize the rank of coarse results to achieve fine registration between bands. After experiments on a total of 102 images (83 cloudy images and 19 cloud-free images with all 32 bands) from Zhuhai-1 hyperspectral satellite, our method can achieve a registration accuracy of 0.6 pixels in cloudy images, 0.41 pixels in cloud-free images, which is enough for subsequent applications.
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Aazam, Mohammad, and Eui-Nam Huh. "Framework of Resource Management for Intercloud Computing." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/108286.

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There has been a very rapid increase in digital media content, due to which media cloud is gaining importance. Cloud computing paradigm provides management of resources and helps create extended portfolio of services. Through cloud computing, not only are services managed more efficiently, but also service discovery is made possible. To handle rapid increase in the content, media cloud plays a very vital role. But it is not possible for standalone clouds to handle everything with the increasing user demands. For scalability and better service provisioning, at times, clouds have to communicate with other clouds and share their resources. This scenario is called Intercloud computing or cloud federation. The study on Intercloud computing is still in its start. Resource management is one of the key concerns to be addressed in Intercloud computing. Already done studies discuss this issue only in a trivial and simplistic way. In this study, we present a resource management model, keeping in view different types of services, different customer types, customer characteristic, pricing, and refunding. The presented framework was implemented using Java and NetBeans 8.0 and evaluated using CloudSim 3.0.3 toolkit. Presented results and their discussion validate our model and its efficiency.
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Vijaya, Aparna, and Neelanarayanan V. "A Model Driven Framework for Portable Cloud Services." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 2 (April 1, 2016): 708. http://dx.doi.org/10.11591/ijece.v6i2.8270.

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<p class="Abstract">Cloud Computing is an evolving technology as it offers significant benefits like pay only for what you use, scale the resources according to the needs and less in-house staff and resources. These benefits have resulted in tremendous increase in the number of applications and services hosted in the cloud which inturn has resulted in increase in the number of cloud providers in the market. Cloud service providers have a lot of heterogeneity in the resources they use. They have their own servers, different cloud infrastructures, API’s and methods to access the cloud resources. Despite its benefits; lack of standards among service providers has caused a high level of vendor lock-in when a software developer tries to change its cloud provider. In this paper we give an overview on the ongoing and current trends in the area of cloud service portability and we also propose a new cloud portability platform. Our new platform is based on establishing feature models which offers the desired cloud portability. Our solution DSkyL uses feature models and domain model analysis to support development, customization and deployment of application components across multiple clouds. The main goal of our approach is to reduce the effort and time needed for porting applications across different clouds. This paper aims to give an overview on DSkyL.</p>
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Vijaya, Aparna, and Neelanarayanan V. "A Model Driven Framework for Portable Cloud Services." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 2 (April 1, 2016): 708. http://dx.doi.org/10.11591/ijece.v6i2.pp708-716.

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<p class="Abstract">Cloud Computing is an evolving technology as it offers significant benefits like pay only for what you use, scale the resources according to the needs and less in-house staff and resources. These benefits have resulted in tremendous increase in the number of applications and services hosted in the cloud which inturn has resulted in increase in the number of cloud providers in the market. Cloud service providers have a lot of heterogeneity in the resources they use. They have their own servers, different cloud infrastructures, API’s and methods to access the cloud resources. Despite its benefits; lack of standards among service providers has caused a high level of vendor lock-in when a software developer tries to change its cloud provider. In this paper we give an overview on the ongoing and current trends in the area of cloud service portability and we also propose a new cloud portability platform. Our new platform is based on establishing feature models which offers the desired cloud portability. Our solution DSkyL uses feature models and domain model analysis to support development, customization and deployment of application components across multiple clouds. The main goal of our approach is to reduce the effort and time needed for porting applications across different clouds. This paper aims to give an overview on DSkyL.</p>
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Patil, Raj Kumar, and Rehaman Pasha. "A Framework for Cloud Information Accountability." International Journal of Scientific Research 2, no. 10 (June 1, 2012): 1–4. http://dx.doi.org/10.15373/22778179/oct2013/51.

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Zbořil, Martin, and Vlasta Svatá. "Cloud Adoption Framework." Procedia Computer Science 207 (2022): 483–93. http://dx.doi.org/10.1016/j.procs.2022.09.103.

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Harshvardhan, Guang Guo, Robert N. Green, Zheng Qu, and Takashi Y. Nakajima. "Remotely Sensed Microphysical and Thermodynamic Properties of Nonuniform Water Cloud Fields." Journal of the Atmospheric Sciences 61, no. 21 (November 1, 2004): 2574–87. http://dx.doi.org/10.1175/jas3301.1.

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Abstract Visible and near-infrared reflected radiances have been used to estimate the cloud optical depth and effective radius of cloud-filled global area coverage (GAC) pixels from the Advanced Very High Resolution Radiometer (AVHRR) for two cases in the North Atlantic Ocean. One is representative of clouds having low concentrations of cloud condensation nuclei (CCN), while the other is an example of maritime clouds forming in continental air, in this case, intruding from Europe around a cutoff low pressure system. It is shown that an estimate of the cloud drop concentration can be obtained from remotely sensed cloud radiative properties and standard meteorological analyses. These concentrations show very clearly the influence of enhanced CCN on cloud microphysics. However, conclusions regarding the indirect radiative effect of aerosol on cloud must wait for the development of a framework for analyzing changes in cloud liquid water path (LWP). It is shown that estimates of LWP are greatly influenced by the scheme that is used to identify cloudy pixels at the AVHRR GAC resolution. Application of a very strict thermal channel spatial coherence criterion for identifying cloud-filled pixels yields mean LWP estimates for cloudy pixels alone that are 40%–75% higher than mean LWP estimates for the much larger sample of possibly cloudy pixels identified by a reflectance threshold criterion.
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Sharma, Puneet, Peter Dalin, and Ingrid Mann. "Towards a Framework for Noctilucent Cloud Analysis." Remote Sensing 11, no. 23 (November 22, 2019): 2743. http://dx.doi.org/10.3390/rs11232743.

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In this paper, we present a framework to study the spatial structure of noctilucent clouds formed by ice particles in the upper atmosphere at mid and high latitudes during summer. We studied noctilucent cloud activity in optical images taken from three different locations and under different atmospheric conditions. In order to identify and distinguish noctilucent cloud activity from other objects in the scene, we employed linear discriminant analysis (LDA) with feature vectors ranging from simple metrics to higher-order local autocorrelation (HLAC), and histogram of oriented gradients (HOG). Finally, we propose a convolutional neural networks (CNN)-based method for the detection of noctilucent clouds. The results clearly indicate that the CNN-based approach outperforms the LDA-based methods used in this article. Furthermore, we outline suggestions for future research directions to establish a framework that can be used for synchronizing the optical observations from ground-based camera systems with echoes measured with radar systems like EISCAT in order to obtain independent additional information on the ice clouds.
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Dissertations / Theses on the topic "CLOUD FRAMEWORK"

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Falk, Matthew D. "Cryptographic cloud storage framework." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/85417.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (page 59).
The cloud prevents cheap and convenient ways to create shared remote repositories. One concern when creating systems that provide security is if the system will be able to remain secure when new attacks are developed. As tools and techniques for breaking security systems advance, new ideas are required to provide the security guarantees that may have been exploited. This project presents a framework which can handle the ever growing need for new security defenses. This thesis describes the Key Derivation Module that I have constructed, including many new Key Derivation Functions, that is used in our system.
by Matthew D. Falk.
M. Eng.
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RODRIGUES, Thiago Gomes. "Cloudacc: a cloud-based accountability framework for federated cloud." Universidade Federal de Pernambuco, 2016. https://repositorio.ufpe.br/handle/123456789/18590.

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Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2017-04-19T15:09:08Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) tgr_thesis.pdf: 4801672 bytes, checksum: ce1d30377cfe8fad52dbfd02d55554e6 (MD5)
Made available in DSpace on 2017-04-19T15:09:08Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) tgr_thesis.pdf: 4801672 bytes, checksum: ce1d30377cfe8fad52dbfd02d55554e6 (MD5) Previous issue date: 2016-09-08
The evolution of software service delivery has changed the way accountability is performed. The complexity related to cloud computing environments increases the difficulty in properly performing accountability, since the evidences are spread through the whole infrastructure, from different servers, in physical, virtualization and application layers. This complexity increases when the cloud federation is considered because besides the inherent complexity of the virtualized environment, the federation members may not implement the same security procedures and policies. The main objective of this thesis is to propose an accountability framework named CloudAcc, that supports audit, management, planning and billing process in federated cloud environments, increasing trust and transparency. Furthermore, CloudAcc considers the legal safeguard requirements presented in Brazilian Marco Civil da Internet. We confirm the CloudAcc effectiveness when some infrastructure elements were submitted against Denial of Service (DoS) and Brute Force attacks, and our framework was able to detect them. Facing the results obtained, we can conclude that CloudAcc contributes to the state-of-the-art once it provides the holistic vision of the cloud federated environment through the evidence collection considering the three layers, supporting audit, management, planning and billing process in federated cloud environments.
A maneira de realizar accountability tem variado à medida em que o modo de entrega de serviços de Tecnologia da Informação (TI) tem evoluído. Em ambientes de nuvem a complexidade de realizar accountability apropriadamente é alta porque as evidências devem ser coletadas considerando-se as camadas física, de virtualização e de aplicações, que estão espalhadas em diferentes servidores e elementos da infraestrutura. Esta complexidade é ampliada quando ocorre a federação das infraestruturas de nuvem porque além da complexidade inerente ao ambiente virtualizado, os membros da federação podem não ter os mesmos grupos de políticas e práticas de segurança. O principal objetivo desta tese é propor um framework de accountability, denominado CloudAcc, que suporte processos de auditoria, gerenciamento, planejamento e cobrança, em nuvens federadas, aumentando a confiança e a transparência. Além disso, o CloudAcc também considera os requisitos legais para a salvaguarda dos registros, conforme descrito no Marco Civil da Internet brasileira. A efetividade do CloudAcc foi confirmada quando alguns componentes da infraestrutura da nuvem foram submetidos a ataques de negação de serviço e de força bruta, e o framework foi capaz de detectá-los. Diante dos resultados obtidos, pode-se concluir que o CloudAcc contribui para o estado-da-arte, uma vez que fornece uma visão holística do ambiente de nuvem federada através da coleta de evidências em três camadas suportando os processos de auditoria, gerenciamento, planejamento e cobrança.
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Aldakheel, Eman A. "A Cloud Computing Framework for Computer Science Education." Bowling Green State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1322873621.

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Falk, Sebastian, and Andriy Shyshka. "The Cloud Marketplace : A Capability-Based Framework for Cloud Ecosystem Governance." Thesis, Internationella Handelshögskolan, Högskolan i Jönköping, IHH, Informatik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-23968.

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Within the last five years, the market of cloud computing has shown rapid growth. However, despite the increasing popularity, researchers highlight numerous concerns regarding limited interoperability of systems hosted by different cloud providers as well as restricted customization of cloud solutions. In order to counter aforemen-tioned challenges, this study investigates the idea of introducing a marketplace for cloud services that leverage the service-oriented architecture (SOA) paradigm and of-fers software solutions, computing capabilities from cloud providers, components developed by third parties, as well as access to integration and audit services. The goal of the study lies in conceptualizing the idea and the evaluation of demand it may raise from the key cloud actors. In this regard, existing frameworks of cloud compu-ting and SOA contributed to the development of an initial model that was further improved through the interviewing process. The results of this study include a capa-bility-based framework for the cloud marketplace which not only clarifies the role and activities of the different actors but also contains the necessary features of the marketplace that are needed to ensure the proper workflow. In addition to that, the actors’ incentives and concerns regarding the marketplace were analyzed by applying SWOT-analysis. While the analysis revealed both positive interest and present de-mand among the actors, the identified weaknesses and threats highlight the need for further investigations in order to put the idea into practice.
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Jallow, Alieu. "CLOUD-METRIC: A Cost Effective Application Development Framework for Cloud Infrastructures." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-300681.

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Classic application development model primarily focuses on two key objectives: scalable system architecture and best possible performance. However, This model of application development works well on the private resources, but with the growing amount of public IaaS it is essential to find a balance between the cost and the performance of an application. In this thesis, we have proposed CLOUD-METRIC: A Cost Effective Application Development for Cloud Infrastructures. The framework allows users to estimate the cost of running applications on public cloud infrastructures during the development phase. We will consider two major cloud services providers, Amazon AWS and Google Cloud Platform. The provided estimates can be very useful to make improvements in the users' application architecture. In addition to cost estimation, the framework allows users to monitor resources utilized by their applications. Finally, we will provide users with recommendation of instances on AWS and GCP based on resources utilized by the applications over a period of time.
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Khan, Syeduzzaman. "A PROBABILISTIC MACHINE LEARNING FRAMEWORK FOR CLOUD RESOURCE SELECTION ON THE CLOUD." Scholarly Commons, 2020. https://scholarlycommons.pacific.edu/uop_etds/3720.

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The execution of the scientific applications on the Cloud comes with great flexibility, scalability, cost-effectiveness, and substantial computing power. Market-leading Cloud service providers such as Amazon Web service (AWS), Azure, Google Cloud Platform (GCP) offer various general purposes, memory-intensive, and compute-intensive Cloud instances for the execution of scientific applications. The scientific community, especially small research institutions and undergraduate universities, face many hurdles while conducting high-performance computing research in the absence of large dedicated clusters. The Cloud provides a lucrative alternative to dedicated clusters, however a wide range of Cloud computing choices makes the instance selection for the end-users. This thesis aims to simplify Cloud instance selection for end-users by proposing a probabilistic machine learning framework to allow to users select a suitable Cloud instance for their scientific applications. This research builds on the previously proposed A2Cloud-RF framework that recommends high-performing Cloud instances by profiling the application and the selected Cloud instances. The framework produces a set of objective scores called the A2Cloud scores, which denote the compatibility level between the application and the selected Cloud instances. When used alone, the A2Cloud scores become increasingly unwieldy with an increasing number of tested Cloud instances. Additionally, the framework only examines the raw application performance and does not consider the execution cost to guide resource selection. To improve the usability of the framework and assist with economical instance selection, this research adds two Naïve Bayes (NB) classifiers that consider both the application’s performance and execution cost. These NB classifiers include: 1) NB with a Random Forest Classifier (RFC) and 2) a standalone NB module. Naïve Bayes with a Random Forest Classifier (RFC) augments the A2Cloud-RF framework's final instance ratings with the execution cost metric. In the training phase, the classifier builds the frequency and probability tables. The classifier recommends a Cloud instance based on the highest posterior probability for the selected application. The standalone NB classifier uses the generated A2Cloud score (an intermediate result from the A2Cloud-RF framework) and execution cost metric to construct an NB classifier. The NB classifier forms a frequency table and probability (prior and likelihood) tables. For recommending a Cloud instance for a test application, the classifier calculates the highest posterior probability for all of the Cloud instances. The classifier recommends a Cloud instance with the highest posterior probability. This study performs the execution of eight real-world applications on 20 Cloud instances from AWS, Azure, GCP, and Linode. We train the NB classifiers using 80% of this dataset and employ the remaining 20% for testing. The testing yields more than 90% recommendation accuracy for the chosen applications and Cloud instances. Because of the imbalanced nature of the dataset and multi-class nature of classification, we consider the confusion matrix (true positive, false positive, true negative, and false negative) and F1 score with above 0.9 scores to describe the model performance. The final goal of this research is to make Cloud computing an accessible resource for conducting high-performance scientific executions by enabling users to select an effective Cloud instance from across multiple providers.
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Mengistu, Tessema Mindaye. "RESOURCE MANAGEMENT FRAMEWORK FOR VOLUNTEER CLOUD COMPUTING." OpenSIUC, 2018. https://opensiuc.lib.siu.edu/dissertations/1613.

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The need for high computing resources is on the rise, despite the exponential increase of the computing capacity of workstations, the proliferation of mobile devices, and the omnipresence of data centers with massive server farms that housed tens (if not hundreds) of thousands of powerful servers. This is mainly due to the unprecedented increase in the number of Internet users worldwide and the Internet of Things (IoTs). So far, Cloud Computing has been providing the necessary computing infrastructures for applications, including IoT applications. However, the current cloud infrastructures that are based on dedicated datacenters are expensive to set-up; running the infrastructure needs expertise, a lot of electrical power for cooling the facilities, and redundant supply of everything in a data center to provide the desired resilience. Moreover, the current centralized cloud infrastructures will not suffice for IoT's network intensive applications with very fast response requirements. Alternative cloud computing models that depend on spare resources of volunteer computers are emerging, including volunteer cloud computing, in addition to the conventional data center based clouds. These alternative cloud models have one characteristic in common -- they do not rely on dedicated data centers to provide the cloud services. Volunteer clouds are opportunistic cloud systems that run over donated spare resources of volunteer computers. On the one hand, volunteer clouds claim numerous outstanding advantages: affordability, on-premise, self-provision, greener computing (owing to consolidate use of existent computers), etc. On the other hand, full-fledged implementation of volunteer cloud computing raises unique technical and research challenges: management of highly dynamic and heterogeneous compute resources, Quality of Service (QoS) assurance, meeting Service Level Agreement (SLA), reliability, security/trust, which are all made more difficult due to the high dynamics and heterogeneity of the non-dedicated cloud hosts. This dissertation investigates the resource management aspect of volunteer cloud computing. Due to the intermittent availability and heterogeneity of computing resource involved, resource management is one of the challenging tasks in volunteer cloud computing. The dissertation, specifically, focuses on the Resource Discovery and VM Placement tasks of resource management. The resource base over which volunteer cloud computing depends on is a scavenged, sporadically available, aggregate computing power of individual volunteer computers. Delivering reliable cloud services over these unreliable nodes is a big challenge in volunteer cloud computing. The fault tolerance of the whole system rests on the reliability and availability of the infrastructure base. This dissertation discusses the modelling of a fault tolerant prediction based resource discovery in volunteer cloud computing. It presents a multi-state semi-Markov process based model to predict the future availability and reliability of nodes in volunteer cloud systems. A volunteer node is modelled as a semi-Markov process, whose future state depends only on its current state. This exactly matches with a key observation made in analyzing the traces of personal computers in enterprises that the daily patterns of resource availability are comparable to those in the most recent days. The dissertation illustrates how prediction based resource discovery enables volunteer cloud systems to provide reliable cloud services over the unreliable and non-dedicated volunteer hosts with empirical evidences. VM placement algorithms play crucial role in Cloud Computing in fulfilling its characteristics and achieving its objectives. In general, VM placement is a challenging problem that has been extensively studied in conventional Cloud Computing context. Due to its divergent characteristics, volunteer cloud computing needs a novel and unique way of solving the existing Cloud Computing problems, including VM placement. Intermittent availability of nodes, unreliable infrastructure, and resource constrained nodes are some of the characteristics of volunteer cloud computing that make VM placement problem more complicated. In this dissertation, we model the VM placement problem as a \textit{Bounded 0-1 Multi-Dimensional Knapsack Problem}. As a known NP-hard problem, the dissertation discusses heuristic based algorithms that takes the typical characteristics of volunteer cloud computing into consideration, to solve the VM placement problem formulated as a knapsack problem. Three algorithms are developed to meet the objectives and constraints specific to volunteer cloud computing. The algorithms are tested on a real volunteer cloud computing test-bed and showed a good performance results based on their optimization objectives. The dissertation also presents the design and implementation of a real volunteer cloud computing system, cuCloud, that bases its resource infrastructure on donated computing resource of computers. The need for the development of cuCloud stems from the lack of experimentation platform, real or simulation, that specifically works for volunteer cloud computing. The cuCloud is a system that can be called a genuine volunteer cloud computing system, which manifests the concept of ``Volunteer Computing as a Service'' (VCaaS), with a particular significance in edge computing and related applications. In the course of this dissertation, empirical evaluations show that volunteer clouds can be used to execute range of applications reliably and efficiently. Moreover, the physical proximity of volunteer nodes to where applications originate, edge of the network, helps them in reducing the round trip time latency of applications. However, the overall computing capability of volunteer clouds will not suffice to handle highly resource intensive applications by itself. Based on these observations, the dissertation also proposes the use of volunteer clouds as a resource fabric in the emerging Edge Computing paradigm as a future work.
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Zhang, Amy (Amy X. ). "A functional flow framework for cloud computing." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/77453.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 53).
This thesis covers a basic framework to calculate the maximum computation rate of a set of functions over a network. These functions are broken down into a series of computations, which are distributed among nodes of the network, with the output sent to the terminal node. We analyze two models with different types of computation costs, a linear computation cost model and a maximum computation cost model. We show how computation distribution through the given network changes with different types of computation and communication limitations. This framework can also be used in cloud design, where a network of given complexity is designed to maximize computation rate for a given set of functions. We provide a greedy algorithm that provides one solution to this problem, and create simulations for each framework, and analyze the results.
by Amy Zhang.
M.Eng.
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Chaudhry, Nauman Riaz. "Workflow framework for cloud-based distributed simulation." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/14778.

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Although distributed simulation (DS) using parallel computing has received considerable research and development in a number of compute-intensive fields, it has still to be significantly adopted by the wider simulation community. According to scientific literature, major reasons for low adoption of cloud-based services for DS execution are the perceived complexities of understanding and managing the underlying architecture and software for deploying DS models, as well as the remaining challenges in performance and interoperability of cloud-based DS. The focus of this study, therefore, has been to design and test the feasibility of a well-integrated, generic, workflow structured framework that is universal in character and transparent in implementation. The choice of a workflow framework for implementing cloud-based DS was influenced by the ability of scientific workflow management systems to define, execute, and actively manage computing workflows. As a result of this study, a hybrid workflow framework, combined with four cloud-based implementation services, has been used to develop an integrated potential standard for workflow implementation of cloud-based DS, which has been named the WORLDS framework (Workflow Framework for Cloud-based Distributed Simulation). The main contribution of this research study is the WORLDS framework itself, which identifies five services (including a Parametric Study Service) that can potentially be provided through the use of workflow technologies to deliver effective cloud-based distributed simulation that is transparently provisioned for the user. This takes DS a significant step closer to its provision as a viable cloud-based service (DSaaS). In addition, the study introduces a simple workflow solution to applying parametric studies to distributed simulations. Further research to confirm the generic nature of the workflow framework, to apply and test modified HLA standards, and to introduce a simulation analytics function by modifying the workflow is anticipated.
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Li, Min. "A resource management framework for cloud computing." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/47804.

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The cloud computing paradigm is realized through large scale distributed resource management and computation platforms such as MapReduce, Hadoop, Dryad, and Pregel. These platforms enable quick and efficient development of a large range of applications that can be sustained at scale in a fault-tolerant fashion. Two key technologies, namely resource virtualization and feature-rich enterprise storage, are further driving the wide-spread adoption of virtualized cloud environments. Many challenges arise when designing resource management techniques for both native and virtualized data centers. First, parameter tuning of MapReduce jobs for efficient resource utilization is a daunting and time consuming task. Second, while the MapReduce model is designed for and leverages information from native clusters to operate efficiently, the emergence of virtual cluster topology results in overlaying or hiding the actual network information. This leads to two resource selection and placement anomalies: (i) loss of data locality, and (ii) loss of job locality. Consequently, jobs may be placed physically far from their associated data or related jobs, which adversely affect the overall performance. Finally, the extant resource provisioning approach leads to significant wastage as enterprise cloud providers have to consider and provision for peak loads instead of average load (that is many times lower). In this dissertation, we design and develop a resource management framework to address the above challenges. We first design an innovative resource scheduler, CAM, aimed at MapReduce applications running in virtualized cloud environments. CAM reconciles both data and VM resource allocation with a variety of competing constraints, such as storage utilization, changing CPU load and network link capacities based on a flow-network algorithm. Additionally, our platform exposes the typically hidden lower-level topology information to the MapReduce job scheduler, which enables it to make optimal task assignments. Second, we design an online performance tuning system, mrOnline, which monitors the MapReduce job execution, tunes the parameters based on collected statistics and provides fine-grained control over parameter configuration changes to the user. To this end, we employ a gray-box based smart hill-climbing algorithm that leverages MapReduce runtime statistics and effectively converge to a desirable configuration within a single iteration. Finally, we target enterprise applications in virtualized environment where typically a network attached centralized storage system is deployed. We design a new protocol to share primary data de-duplication information available at the storage server with the client. This enables better client-side cache utilization and reduces server-client network traffic, which leads to overall high performance. Based on the protocol, a workload aware VM management strategy is further introduced to decrease the load to the storage server and enhance the I/O efficiency for clients.
Ph. D.
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Books on the topic "CLOUD FRAMEWORK"

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SOA with .NET and Windows Azure: Realizing service-orientation with the Microsoft platform. Upper Saddle River, NJ: Prentice Hall, 2010.

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Mahmood, Zaigham, and Saqib Saeed, eds. Software Engineering Frameworks for the Cloud Computing Paradigm. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5031-2.

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Mahmood, Zaigham. Software Engineering Frameworks for the Cloud Computing Paradigm. London: Springer London, 2013.

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Kaufman, Stephen. Pro Windows Server AppFabric. [New York?]: Apress, 2010.

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Gerald, Schimak, Denzer Ralf, and SpringerLink (Online service), eds. Environmental Software Systems. Frameworks of eEnvironment: 9th IFIP WG 5.11 International Symposium, ISESS 2011, Brno, Czech Republic, June 27-29, 2011. Proceedings. Berlin, Heidelberg: IFIP International Federation for Information Processing, 2011.

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Data Classification Matrix and Cloud Assessment Framework: Cloud Assessment Framework and Evaluation Methodology. Washington, DC, 2023. http://dx.doi.org/10.1596/39550.

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Chang, Victor. Proposed Cloud Computing Business Framework. Nova Science Publishers, Incorporated, 2015.

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Cloud Network Management: An IoT Based Framework. Taylor & Francis Group, 2020.

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Biswash, Sanjay Kumar, and Sourav Kanti Addya. Cloud Network Management: An IoT Based Framework. Taylor & Francis Group, 2020.

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Biswash, Sanjay Kumar, and Sourav Kanti Addya. Cloud Network Management: An IoT Based Framework. Taylor & Francis Group, 2020.

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Book chapters on the topic "CLOUD FRAMEWORK"

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Farooqui, Shamayel M. "Mastering the Cloud." In Enterprise DevOps Framework, 57–72. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3612-3_6.

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Kohne, Andreas. "Das FCS-Framework." In Cloud-Föderationen, 81–150. Wiesbaden: Springer Fachmedien Wiesbaden, 2018. http://dx.doi.org/10.1007/978-3-658-20973-5_7.

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Farooqui, Shamayel M. "Hopping on the Cloud." In Enterprise DevOps Framework, 45–56. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3612-3_5.

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Das, Ravindra. "The Cloud." In Deploying the Zero Trust Framework in MSFT Azure, 1–19. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003442578-1.

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Dong, Bo, Qinghua Zheng, Mu Qiao, Jian Shu, and Jie Yang. "BlueSky Cloud Framework: An E-Learning Framework Embracing Cloud Computing." In Lecture Notes in Computer Science, 577–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10665-1_55.

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Stanton, Brian, Mary Theofanos, and Karuna P. Joshi. "Framework for Cloud Usability." In Lecture Notes in Computer Science, 664–71. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20376-8_59.

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Zhao, Liang, Sherif Sakr, Anna Liu, and Athman Bouguettaya. "Performance Evaluation Framework of Cloud Platforms." In Cloud Data Management, 47–65. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04765-2_4.

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Sasmitha and A. Suresh. "Trusted Cloud Service Framework for Cloud Computing Security." In International Conference on Innovative Computing and Communications, 157–69. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3315-0_13.

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Nguyen, Binh Minh, Viet Tran, and Ladislav Hluchy. "Development Framework for Cloud Services." In Communications in Computer and Information Science, 137–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35211-9_18.

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Kundu, Anirban, Chunlin Ji, and Ruopeng Liu. "Cloud Based Heterogeneous Distributed Framework." In Advances in Intelligent Systems and Computing, 471–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32063-7_50.

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Conference papers on the topic "CLOUD FRAMEWORK"

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Munhoz, Vanderlei, and Márcio Castro. "HPC@Cloud: A Provider-Agnostic Software Framework for Enabling HPC in Public Cloud Platforms." In Simpósio em Sistemas Computacionais de Alto Desempenho. Sociedade Brasileira de Computação, 2022. http://dx.doi.org/10.5753/wscad.2022.226528.

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The cloud computing paradigm democratized compute infrastructure access to millions of resource-strained organizations, applying economics of scale to massively reduce infrastructure costs. In the High Performance Computing (HPC) context, the benefits of using public cloud resources make it an attractive alternative to expensive on-premises clusters, however there are several challenges and limitations. In this paper, we present HPC@Cloud: a provideragnostic software framework that comprises a set of key software tools to assist in the migration, test and execution of HPC applications in public clouds. HPC@Cloud allows the HPC community to benefit from readily available public cloud resources with minimum efforts and features an empirical approach for estimating cloud infrastructure costs for HPC workloads. We also provide an experimental analysis of HPC@Cloud on two public clouds: Amazon AWS and Vultr Cloud.
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Neto, C. R. Lima, and V. C. Garcia. "Cloud testing framework." In the 17th International Conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2460999.2461037.

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Galante, Guilherme, and Luis Carlos E. de Bona. "Nebulous: A Framework for Scientific Applications Execution on Cloud Environments." In Simpósio em Sistemas Computacionais de Alto Desempenho. Sociedade Brasileira de Computação, 2011. http://dx.doi.org/10.5753/wscad.2011.17266.

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This paper presents a framework, called Nebulous, designed to simplify the execution of MPI and OpenMP parallel applications in computational clouds. The framework aims to automate the process of deployment and execution, avoiding the direct user interaction with the cloud. The framework is built on a cloud middleware layer (e.g. OpenNebula, Eucalyptus or Nimbus) and consists of three components: the Resource Description Block, an Application Programming Interface and the Executor. According to the results, Nebulous allows users to deploy applications over varying numbers of nodes in a simple way, uniformly, scalably and with minimum effort.
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Wu, Curt, and Steve Marotta. "Framework for Assessing Cloud Trustworthiness." In 2013 IEEE 6th International Conference on Cloud Computing (CLOUD). IEEE, 2013. http://dx.doi.org/10.1109/cloud.2013.76.

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Peng, Tao, and Satyandra K. Gupta. "A Computational Framework for Point Cloud Construction Using Digital Projection Patterns." In ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/detc2006-99553.

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Many reverse engineering and inspection applications require generation of point clouds representing faces of physical objects. This paper describes a computational framework for constructing point clouds using digital projection patterns. The basic principle behind the approach is to project known patterns on the object using a digital projector. A digital camera is then used to take images of the object with the known projection patterns imposed on it. Due to the presence of 3-D faces of the object, the projection patterns appear distorted in the images. The images are analyzed to construct the 3-D point cloud that is capable of introducing the observed distortions in the images. The approach described in this paper presents three advances over the previously developed approaches. First, it is capable of working with the projection patterns that have variable fringe widths and curved fringes and hence can provide improved accuracy. Second, our algorithm minimizes the number of images needed for creating the 3-D point cloud. Finally, we use a hybrid approach that uses a combination of reference plane images and estimated system parameters to construct the point cloud. This approach provides good run-time computational performance and simplifies the system calibration.
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Hosono, Shigeru, He Huang, Tatsunori Hara, Yoshiki Shimomura, and Tamio Arai. "A Lifetime Supporting Framework for Cloud Applications." In 2010 IEEE International Conference on Cloud Computing (CLOUD). IEEE, 2010. http://dx.doi.org/10.1109/cloud.2010.63.

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Tunc, Cihan, Salim Hariri, Mheni Merzouki, Charif Mahmoudi, Frederic J. De Vaulx, Jaafar Chbili, Robert Bohn, and Abdella Battou. "Cloud Security Automation Framework." In 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems (FAS*W). IEEE, 2017. http://dx.doi.org/10.1109/fas-w.2017.164.

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Reixa, Miguel, Carlos Costa, and Manuela Aparicio. "Cloud services evaluation framework." In the Workshop. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2316936.2316948.

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Shankar, Laxman. "Bigonet Mobile Cloud Framework." In 2014 Sixth International Conference on Communication Systems and Networks (COMSNETS). IEEE, 2014. http://dx.doi.org/10.1109/comsnets.2014.6734915.

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Wang, Yazhe, Shunan Ma, and Lei Ren. "A Security Framework for Cloud Manufacturing." In ASME 2014 International Manufacturing Science and Engineering Conference collocated with the JSME 2014 International Conference on Materials and Processing and the 42nd North American Manufacturing Research Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/msec2014-4082.

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Cloud manufacturing has been considered as a promising new service-oriented manufacturing paradigm that can transform traditional industry. However security is one of the major issues which hamper the growth of cloud manufacturing industry. In this paper, we analyze the cloud manufacturing security issues and challenges, and propose a security framework for cloud manufacturing, which includes four levels: infrastructure security, identity and access management, data protection and security, and cloud security as a service. The Infrastructure security level can ensure an organization’s core IT infrastructure security at the network, host, and application levels; Identity and access management level can improve operational efficiency and to comply with privacy and data protection requirements; Data protection and security level can help users evaluate their data security scenarios and make informed judgments regarding risk for their organizations; security as a service level, which includes web security service, storage security service and IAM(Identity and Access Management) service, would extract security functions and capacities to assemble software as a service. The cloud manufacturing security framework we proposed can resolve the security issues and improve the security performance of cloud manufacturing industry.
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Reports on the topic "CLOUD FRAMEWORK"

1

Stanton, Brian C., Mary F. Theofanos, and Karun P. Joshi. Framework for Cloud Usability. National Institute of Standards and Technology, December 2015. http://dx.doi.org/10.6028/nist.sp.500-316.

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Arellano, Lawrence, Steven Arroyo, Gerald Giese, Philip Cox, and G. Rogers. Cloud computing strategic framework (FY13 - FY15). Office of Scientific and Technical Information (OSTI), November 2012. http://dx.doi.org/10.2172/1097208.

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Ackerman, Thomas P. Evaluating and Improving Cloud Processes in the Multi-Scale Modeling Framework. Office of Scientific and Technical Information (OSTI), March 2015. http://dx.doi.org/10.2172/1171707.

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Xu, Kuan-Man, and Anning Cheng. Evaluation and improvement of the cloud resolving model component of the multi-scale modeling framework. Office of Scientific and Technical Information (OSTI), October 2009. http://dx.doi.org/10.2172/1136726.

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Pincus, Robert. Final report for grant DE-FG02-06ER64182: "Evaluation and Improvement of the Cloud Resolving Model Component of the Multi-Scale Modeling Framework". Office of Scientific and Technical Information (OSTI), October 2009. http://dx.doi.org/10.2172/1108698.

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Ma, Po Lun, and Panagiotis Stinis. Developing a simulator-based satellite dataset for using machine learning techniques to derive aerosol-cloud-precipitation interactions in models and observations in a consistent framework. Office of Scientific and Technical Information (OSTI), September 2020. http://dx.doi.org/10.2172/1984697.

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Lytvynova, Svitlana H. Хмаро орієнтоване навчальне середовище загальноосвітнього навчального закладу. [б. в.], August 2018. http://dx.doi.org/10.31812/0564/2451.

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Research goals: to outline the state of secondary education to implement a cloud-oriented learning environment (COLE), establishing research objectives: to determine the readiness of students to the introduction of COLE, to develop a conceptual framework of cooperation in COLE at the level of the institution, identify the actors interact COLE. The object of research supports the learning environment secondary schools; subject – a cloud-oriented learning environment of secondary schools. Research methods used: analysis of statistics and publications. Experimental research and conducted in secondary schools Obolon district of Kyiv. Intermediate results: the architecture of COLE 44 secondary schools of the district, introduced more than 10 thousand accounts established electronic interaction between teachers and students by e-mail Outlook. Currently under development cloud storage (SkyDrive) training materials teachers practiced the skills of teamwork and planning tools calendars. The main conclusions and recommendations. Implementation of COLE at secondary schools provides endless opportunities both teacher and student, in fact created conditions for innovation and learning. Without a doubt we can say that for the future of Honshu, for full use must have a quality Internet, motivated teachers. For subjects of the educational process, the conditions of access to learning materials anywhere, anytime, and it activates the cognitive and creative activity of students that will improve key indicators of learning.
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Solovyanenko, Nina I. ЮРИДИЧЕСКИЕ СТРАТЕГИИ ЦИФРОВОЙ ТРАНСФОРМАЦИИ АГРАРНОГО БИЗНЕСА. DOI CODE, 2021. http://dx.doi.org/10.18411/0131-5226-2021-70004.

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t.The development of global agricultural production and food trade in recent decades implies a digital transformation and the transition to a new technological order, which is an essential factor for sustainable development. Digitalization of agriculture and the food sector is carried out on the basis of IT 2 platforms, the Internet of Things, cloud computing, big data, artificial intelligence, and blockchain technology. Fragmented and unclear legal mechanisms, slow updating of legal regulation hinder the introduction of digital solutions. A modern regulatory framework based on digital strategies should strengthen the confidence of farmers in "smart agriculture". In Russia, the legal mechanism of strategic planning covers the development of the national platform "Digital Agriculture". Digital strategies also include updating basic legislation.
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Armstrong, Dave. Interactive Dashboards and Web Apps with Shiny in R. Instats Inc., 2023. http://dx.doi.org/10.61700/3g41zs4gcnd3e469.

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This seminar introduces Shiny, the R package that serves as a framework for building interactive web applications. One of the benefits of Shiny is that it allows dynamic cloud calculations that you can integrate into your research project (e.g., running statistical models with a custom specification, filtering and summarizing data, producing custom graphics). This seminar discusses how to build Shiny apps, how to customize their appearance, and how to host them so other users can view and interact with them -- readers, reviewers, or other research team members. An official Instats certificate of completion is provided at the conclusion of the seminar. The seminar offers 2 ECTS Equivalent points for European PhD students.
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Armstrong, Dave. Interactive Dashboards and Web Apps with Shiny in R. Instats Inc., 2023. http://dx.doi.org/10.61700/b3vv6dox5eqdl469.

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This seminar introduces Shiny, the R package that serves as a framework for building interactive web applications. One of the benefits of Shiny is that it allows dynamic cloud calculations that you can integrate into your research project (e.g., running statistical models with a custom specification, filtering and summarizing data, producing custom graphics). This seminar discusses how to build Shiny apps, how to customize their appearance, and how to host them so other users can view and interact with them -- readers, reviewers, or other research team members. An official Instats certificate of completion is provided at the conclusion of the seminar. The seminar offers 2 ECTS Equivalent points for European PhD students.
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