Academic literature on the topic 'Google Cloud Services'

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Journal articles on the topic "Google Cloud Services"

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Pawar, Sushama, Chetashri Bhusari, and Archana Gopnarayan. "A STUDY OF GOOGLE CLOUD SERVICES." International Journal of Engineering Applied Sciences and Technology 5, no. 7 (November 1, 2020): 227–31. http://dx.doi.org/10.33564/ijeast.2020.v05i07.034.

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Sajid, Rafat Ul Aman, Sirajul Islam, Abul Bashar Khan Rakib, and Amandeep Kaur. "Interpretation on the Google Cloud Platform and Its Wide Cloud Services." International Journal of Security and Privacy in Pervasive Computing 14, no. 1 (January 1, 2022): 1–7. http://dx.doi.org/10.4018/ijsppc.313586.

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Cloud computing is growing wide and first-rate promising technology. There are three forms of cloud computing: infrastructure as a service (IaaS), software as a service (SaaS), and platform as a service (PaaS). There are many cloud service providers. Among these, AWS (Amazon Web Service), Azure (Microsoft Cloud), IBM Cloud, Oracle Cloud, and VMware are immensely popular. In this paper, the authors study the Google Cloud Platform (GCP) and a few principally used popular services. Google Cloud Platform could be a cloud provider; they supply servers and services that are used on-demand and at scale. Google Cloud Platform is the fastest growing cloud service supplier now.
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Dutta, Pranay, and Prashant Dutta. "Comparative Study of Cloud Services Offered by Amazon, Microsoft and Google." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (April 30, 2019): 981–85. http://dx.doi.org/10.31142/ijtsrd23170.

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Konovalenko, Svitlana M. "ХМАРНІ СЕРВІСИ В ОСВІТНЬОМУ ПРОЦЕСІ СТУДЕНТІВ ТЕХНОЛОГІЧНИХ КОЛЕДЖІВ." Information Technologies and Learning Tools 58, no. 2 (April 29, 2017): 88. http://dx.doi.org/10.33407/itlt.v58i2.1509.

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In the article the prospects for "cloud services" use in the educational process in higher educational institutions I-II levels of accreditation are considered. There are analyzed the literature relating to the concept of "cloud services" and their use in the educational process, as well as some advantages and disadvantages of using cloud services at distant data processing. The advantages and disadvantages of cloud service Google Cloud Platform are presented. On the basis of the research it is proposed G Suite for Education service as a Google cloud environment to work with all applications. There are considered new features of Google Docs in cloud services G Suite for Education and showed by the example the solution to problems using new features of Google Docs in Excel application.
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Nosenko, Yu G., and V. О. Bogdan. "Characteristics of Google cloud services in aspect of preschool educational institution management." CTE Workshop Proceedings 3 (March 20, 2015): 9–15. http://dx.doi.org/10.55056/cte.205.

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The implementation of cloud technologies in education began relatively recently in Ukraine. Particularly in preschool education it is still at an early stage of development and requires further study. Using cloud services releases prospects for improving the quality of preschools’ management processes. Purpose of the study: to describe the characteristics of Google cloud services in terms of preschool educational institution management. Object of study: informatization of preschool education management. Subject of study: the use of cloud services in the preschool educational institution management. Research methods: analysis, generalization and systematization of scientific and methodological sources on the use of cloud technologies in education; study of characteristics, comparison of different vendors’ cloud service to determine the most optimal ones for use in preschool educational institution management. The main results and conclusions: after a preliminary analysis of various cloud services, we have chosen Google services, as optimal for use in preschool educational institution management. Their main advantages which are principal for Ukrainian preschools, were defined: free of charge; ease of use, intuitive interface; availability of universal account that provides access to all services; the use on different platforms (Windows, Android, iOS); the available functionality, required for preschool management; the availability via any digital device connected to the Internet; no need to deploy “cloud”. The characteristics of Google services (Gmail, Google Calendar, Google Drive, Office suite, Google Sites, Google+, Blogger, Hangouts, Google Forms) in terms of preschool educational institution management were considered.
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Нелін, Є. П., О. І. Проскурня, and Я. В. Цись. "ВИКОРИСТАННЯ ХМАРНИХ СЕРВІСІВ GOOGLE НА УРОКАХ МАТЕМАТИКИ." Spiritual-intellectual upbringing and teaching of youth in the 21st century, no. 3 (2021): 261–64. http://dx.doi.org/10.34142//2708-4809.siuty.2021.62.

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The authors identify the prospects for the introduction into the educational process of general secondary education in the study of mathematics in Google’s cloud services. The positive influence on mathematics lessons from the involvement of Google cloud services — Google-form, which contributes to the intensification of the educational process.
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KIPORENKO, Svitlana. "FEATURES OF USING CLOUD TECHNOLOGIES IN EDUCATION." "EСONOMY. FINANСES. MANAGEMENT: Topical issues of science and practical activity", no. 4 (44) (April 2019): 181–89. http://dx.doi.org/10.37128/2411-4413-2019-4-19.

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The article investigates cloud technologies and features of their use in educational institutions. The essence of cloud technologies in education is determined. It is noted that the modern institution of higher education provides usage of information and communication technologies, and in particular, cloud technologies. The main characteristics (features) which characterize cloud services are described here. It is noted that usage of cloud services has benefits and problems as well. There are such mandatory characteristics of cloud computing as self-service on demand, universal access to the network, resource pooling, elasticity, consumption accounting. Four models of deployment of cloud technologies are revealed in the article. They are corporate, public, group and hybrid. It is admitted that the most appropriate model of deployment of cloud technologies in the infrastructure of higher education institutions is hybrid. Cloud technologies support such activities as: communication, collaboration and co-operation, which determines certain areas of their usage. The architecture of cloud technologies is presented in three levels: software as a service (SaaS), platform as a service (PaaS), infrastructure as a service (IaaS). The most popular cloud services which are used in various spheres, including educational institutions, are analyzed here. Office 365, Microsoft Planner, Microsoft SkyDrive, Google Drive, Google Talk, Google Docs, these are the common cloud services which are selected in SaaS. Popular software platforms which are presented in the PaaS segment are Windows Azure, Google App Engine, Cloud9 IDE, Ideone IDE. Each of the cloud platforms that is described in the article has its own peculiarities. Possible areas of application of cloud technologies in the educational process are identified. They are free access to the programs and services used in the educational process, planning activities, implementation of the working regime of teachers and employees, the organization and conduct of scientific online conferences, seminars, forums, round tables, trainings, joint project work of students, individual independent work of students, electronic interaction with entrants.
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Franchuk, V. M. "The use of cloud technologies in universities. Google services." CTE Workshop Proceedings 1 (March 21, 2013): 99–100. http://dx.doi.org/10.55056/cte.146.

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Google is a mathematical term for 1 with 100 zeros. The term was introduced by Milton Sirota, nephew of the American mathematician Edward Kasner, and described in Kasner and James Newman's book Mathematics and Imagination. Google's play on words reflects the company's mission, which is to organize the vast amount of data available on the global Internet.Google's work is based on a single goal: to organize the data accumulated around the world and make it as accessible and useful as possible. An important aspect in accomplishing this challenging task is to organize the knowledge base held by academic institutions. In an academic environment, data sharing is essential for both teaching and research. In this regard, Google provides tools to improve knowledge sharing and collaboration that students, faculty, and other community members can use anytime, anywhere.
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Spirin, Oleg M., and Oksana A. Odud. "МОДЕЛЬ ФОРМУВАННЯ ІНФОРМАЦІЙНО-КОМУНІКАЦІЙНОЇ КОМПЕТЕНТНОСТІ ДОКТОРА ФІЛОСОФІЇ НА ОСНОВІ ВИКОРИСТАННЯ ХМАРНИХ СЕРВІСІВ GOOGLE SCHOLAR." Information Technologies and Learning Tools 56, no. 6 (December 30, 2016): 204. http://dx.doi.org/10.33407/itlt.v56i6.1489.

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This article deals with the problem of the formation of IC-competence of Ph.D. based on the using of international scientometric systems cloud services, including system Google Scholar. It is justified and specified the concept of "informative-communicative competence of Ph.D." It is defined the model of the formation of informative-communicative competence of Ph.D. through the using of cloud informative-analytical services of the system Google Scholar in training PhD which based on the main scientific approaches in teaching adults. The model consists of five components: target, organizational technological, effective- diagnostic and contents. The groups of cloud services of Google Scholar are chosen: information and search services, information and analytic services, additional services. It is determined the using of cloud informative and analytical services of Google Scholar for informative and analytical support of scientific and educational research.
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Zulpukarova, D. "Google Applications in Student’s Self-learning." Bulletin of Science and Practice 5, no. 12 (December 15, 2019): 420–30. http://dx.doi.org/10.33619/2414-2948/49/52.

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Problem and rationale. Despite the spread of information technology in various areas of the modern society, the possibilities of cloud technology are not used much in the practice of university education. The article substantiates the effectiveness of the use of Google applications in the process of organizing student’s self-learning. Methodology. While researching the problem, we have used the analysis of theoretical and methodological literature, study of best practices in teaching mathematics and computer science using information technology, analysis of the advantages and disadvantages of using cloud technology in training, conduct of computer science courses using Google applications, as well as the management of independent learning of first-year students of Osh State University using cloud technology. Results. The capabilities of the specialized services of Google.docs, Google.tables, Google.presentation and Google.forms for creating a document, presentation, spreadsheet, online questionnaires and online tests have been defined. The use of Google services in the practice of training leads to the formation of student’s training in the skills of working with cloud technologies, meanwhile students demonstrate three levels of knowledge of cloud technologies: high, medium and low. Conclusions. Working with cloud technologies, it is possible for students to build ICT skills, skills of self-organization. Using Google applications, it is potential to monitor the implementation of self-learning, student projects, both in the process of completing assignments and achieving final results, the outcome of which is the improvement of teacher’s work process and saving time.
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Dissertations / Theses on the topic "Google Cloud Services"

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Quadri, S. (Samsideen). "Cloud computing:migrating to the cloud, Amazon Web Services and Google Cloud Platform." Master's thesis, University of Oulu, 2017. http://urn.fi/URN:NBN:fi:oulu-201703151365.

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Cloud computing provides great benefits and challenges for small, medium, and large organisations. Whether it is a financial, technology, or engineering sector any company may find a useful cloud component for the organisation’s needs. Though with its benefits come many challenges, experts believe that cloud computing advantages outweigh the disadvantages. With more research in the area of cloud computing, the challenges will be dealt with. Amazon and Google are the prominent cloud services providers with different components and services that are evolving. However, there are many other cloud vendors and this makes it difficult for potential cloud users to migrate or choose a suitable vendor from the numerous available ones. This thesis, using design science approach describes the implementation of cloud computing by different cloud providers, i.e., examines and demonstrates how Amazon and Google implemented and structured their cloud infrastructure. Also, it provides the criteria for accessing the suitability of a business or organisation for choosing a cloud provider. In addition, a web application hosted on both AWS and GCP platforms is developed in order to demonstrate the workability of the framework and guideline for selecting a cloud provider. The application is called KlaudCelet. KlaudCelet is a recommender system that is developed based on previous research on cloud deployment frameworks and proposed model (CPSM; Cloud Provider Selection Model). The recommendation by the system is reliable because it is based on research results. KlaudCelet recommends a deployment model, a cloud service provider, and a decision either to migrate or not. CPSM was not used in KlaudCelet but the model by Keung & Kwok and Misra & Mondal were used. The guideline developed was used to create a recommender framework. The need for solving this problem was shown by conducting a review of previous research and a small-scale survey among IT experts. The survey found that IT experts are optimistic about cloud computing adoption despite its challenges such as security, privacy and leaning curve. The decision to migrate to the cloud must take into consideration the organisation’s state and culture, cloud deployment model, and choosing a suitable cloud service provider.
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Bandini, Alessandro. "Programming and Deployment of Cloud-based Data Analysis Applications." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/13803/.

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Cloud Computing constitutes a model capable of enabling the network access in a shared, practical and on demand of different computational resources like networks, memory, application or services. This work has as goal the explanation of the project made within Cloud Computing. After an introduction of the theory that lies behind Cloud computing's technologies, there is the practical part of the the work, starting from a more specific platform, Hadoop, which allows storage and data analysis and then moving to more general purpose platforms, Amazon Web Services and Google App Engine, where different types of services have been tried. The major part of the project is based on Google App Engine, where storage and computational services have been used to run MapReduce jobs. MapReduce is a different programming approach for solving data analysis problems, that is suited for big data. The first jobs are written in python, an imperative programming language. Later on, a functional approach on the same problems has been tried, with the Scala language and Spark platform, to compare the code. As Cloud computing is mainly used to host websites, a simple site was developed as integral part of the work. The development of the site is not explained as it goes beyond this thesis' main focus, only the relevant aspects will be treated.
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Englund, Carl. "Evaluation of cloud-based infrastructures for scalable applications." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139935.

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The usage of cloud computing in order to move away from local servers and infrastructure have grown enormously the last decade. The ability to quickly scale capacity of servers and their resources at once when needed is something that can both be a price saver for companies and help them deliver high end products that will function correctly at all times even under heavy load to their customers. To meet todays challenges, one of the strategic directions of Attentec, a software company located in Linköping, is to examine the world of cloud computing in order to deliver robust and scalable applications to their customers. This thesis investigates the usage of cloud services in order to deploy scalable applications which can adapt to usage peaks within minutes.
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Talevi, Iacopo. "Big Data Analytics and Application Deployment on Cloud Infrastructure." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14408/.

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This dissertation describes a project began in October 2016. It was born from the collaboration between Mr.Alessandro Bandini and me, and has been developed under the supervision of professor Gianluigi Zavattaro. The main objective was to study, and in particular to experiment with, the cloud computing in general and its potentiality in the data elaboration field. Cloud computing is a utility-oriented and Internet-centric way of delivering IT services on demand. The first chapter is a theoretical introduction on cloud computing, analyzing the main aspects, the keywords, and the technologies behind clouds, as well as the reasons for the success of this technology and its problems. After the introduction section, I will briefly describe the three main cloud platforms in the market. During this project we developed a simple Social Network. Consequently in the third chapter I will analyze the social network development, with the initial solution realized through Amazon Web Services and the steps we took to obtain the final version using Google Cloud Platform with its charateristics. To conclude, the last section is specific for the data elaboration and contains a initial theoretical part that describes MapReduce and Hadoop followed by a description of our analysis. We used Google App Engine to execute these elaborations on a large dataset. I will explain the basic idea, the code and the problems encountered.
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Величко, Георгій Вячеславович. "Методи та засоби прогнозування індексу акцій у реальному часі на базі хмарних обчислювальних сервісів." Master's thesis, КПІ ім. Ігоря Сікорського, 2021. https://ela.kpi.ua/handle/123456789/46687.

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Магістерська дисертація: 92 с., 28 рис., 21 табл., 1 дод., 30 джерел. Тема роботи — Методи та засоби прогнозування індексу акцій у реальному часі на базі хмарних обчислювальних сервісів. Об’єкт дослідження — методи прогнозування акцій на базі хмарних обчислювальних сервісів. Предмет досліджень — багатошарова нейронна мережа, згорткова нейронна мережа та мережа довгої короткочасної пам’яті, хмарні обчислювальні сервіси, Amazon Web Services. Мета роботи — підвищити ефективність прогнозування індексу акцій на базі хмарних обчислювальних сервісів та розробити модель реалізації системи на базі хмарних сервісів. Актуальність — використання апарату штучних нейронних мереж дає змогу робити більш точні передбачення ціни акцій порівнюючи з іншими технічними методами. Хмарні сервіси забезпечують швидке та гнучке розгортання інфраструктури, забезпечує їх повсякчасну доступність. В ході виконання роботи було реалізовано декілька ахітектур нейронних мереж та проведено аналіз результатів їх роботи. Також було cпроектовано систему прогнозування індексу акцій в реальному часі на базі хмарних сервісів. Для покращення результатів у майбутньому можна застосувати Keras Tuner, який автоматично підбирає гіперпараметри нейронної мережі. Також можна використовувати додаткову інформацію про компанію і додавати ці дані до моделей.
Master`s thesis contains: 92 p., 21 tables, 28 fig., 1 add. and 30 references. The theme: The mechanisms tools for real-time stock market index prediction based on Cloud Compute Services. The objects of this research are mechanisms for stock market index prediction based on Cloud Compute Services. The subjects of this research are neural network architectures: multilayer neural network, convolutional neural network, long short-term memory, cloud compute services, Amazon Web Services. The purpose of this work is to improve efficiency of stock market index prediction based on Cloud Compute Services and design model of system for stock market index forecasting based on Cloud Compute Services. The relevance of this topic is that using neural networks could drastically improve stock market values forecasting comparing to other technical methods. Cloud compute services provides fast and flexible deployment of infrastructure as well as constant availability. Also designed a model of system for real-time stock index forecasting based on cloud compute services. During research I built three architectures of neural networks and analyzed which of them work better with the given case. For further research, it is possible to use Keras Tuner which allows to automatically tune hyperparameters on our neural network and improve output result. Also, additional data about companies provided real-time can improve models.
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Göthesson, Richard, and Gustav Hedman. "Autentiseringsprocesser i molnbaserade datortjänster." Thesis, Malmö högskola, Fakulteten för teknik och samhälle (TS), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20452.

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Tidigare forskning har påvisat brister i olika former av autentiseringsprocesser som leder till autentiseringsattacker. Målet med vår studie är att presentera ett antal riktlinjer som företag och privatpersoner kan följa för att minimera risken för autentiseringsattacker. Metoderna som användes för att komma fram till dessa riktlinjer var kvalitativa där en praktisk observationsstudie, en litteraturstudie samt en enkätundersökning låg till grund för vår insamlade data. Resultatet av studien pekar på att Google Cloud Platform, Amazon Web Services och Microsoft Azure alla har en stark autentiseringsprocess i jämförelse med kritik från tidigare forskning. Enkätundersökningen visade dessutom att olika former av alternativ autentisering, såsom Two Factor Authentication (2FA) och Multi Factor Authentication (MFA), rekommenderas för ett starkt försvar mot autentiseringsattacker.Uppsatsens resultat pekar även på att användarens egenansvar i autentiseringsprocessen är av stor vikt för att minimera risken för autentiseringsattacker. Säkra lösenord bör konstrueras och frekvent bytas ut. Även alternativ autentisering och begränsning av användarens tillgång till känslig information bör tillämpas.
Previous research has shown deficiencies in various forms of authentication processes that lead to authentication attacks. The goal of our study is to present a number of guidelines that businesses and individuals can follow to minimize the risk of authentication attacks. The methods used to reach these guidelines were qualitative. They consisted of a practical observational study, a literature review and a survey, which formed the basis of our collected data. The results of the study indicate that Google Cloud Platform, Amazon Web Services and Microsoft Azure all have a strong authentication process in comparison with the criticism of previous research. The survey also showed that different forms of authentication methods, such as the Two Factor Authentication (2FA) and Multi Factor Authentication (MFA), are recommended for a strong defense against authentication attacks.The thesis’ results also points to the user’s own responsibility in the authentication process are essential to minimize the risk of authentication attacks. Secure passwords should be designed and frequently replaced. Alternative authentication and restricted access to sensitive information for the user should also be applied.
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Torelli, Mattia. "Notifiche push in applicativi Android: uso di gcm ed una soluzione alternativa basata su long-polling http." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amslaurea.unibo.it/7574/.

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Vengono presentate due soluzioni per le notifiche push in ambiente Android: le soluzioni trattate riguardano l'uso di GCM (Google Cloud Messaging) ed una implementazione che impiega il Long-Polling HTTP come alternativa al servizio GCM.
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Cavallin, Riccardo. "Approccio blockchain per la gestione dei dati personali." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21604/.

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L'elaborato presenta la tecnologia blockchain nelle sue funzionalità e nei suoi limiti. In particolare sono presentate le piattaforme Ethereum, RadixDLT e IOTA. Vengono discusse le implicazioni del regolamento GDPR nei confronti di questa tecnologia per la costruzione di Data Marketplace basato su Ethereum. Dopo aver presentato l'architettura di un marketplace si analizzano le prestazioni di diversi servizi di storage online per l'archiviazione di dati personali.
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Galavotti, Serena. "Contacts Sharing: integrazione tra CRM e il cloud Google." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amslaurea.unibo.it/6747/.

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Kutil, Ivan. "Google jako enterprise řešení firmy." Master's thesis, Vysoká škola ekonomická v Praze, 2011. http://www.nusl.cz/ntk/nusl-85156.

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The diploma thesis deals with the topic of cloud computing and its appropriate deployment in small and medium business. The objective of this thesis is to analyze the offer of Google products and to demonstrate measurable benefits of this solution. Introductory part of the thesis summarizes the history of IT and it is looking for the connection between history of IT and the term cloud computing. This term is described in detail with selected cloud computing characteristics. The main part of the thesis deals with analysis of Google services, technologies and platforms, which could be used in enterprise. The matters are viewed not only economically, but also from the technological and security point of view. In practical implementations are demonstrated measurable benefits of deployment to the Google cloud platform. Finally, the thesis provides the summary of platform's advantages and disadvantages with a description of practical advices for small and medium businesses
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Books on the topic "Google Cloud Services"

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1975-, Arbon Jason, and Carollo Jeff 1982-, eds. How Google tests software. Upper Saddle River, NJ: Addison-Wesley, 2012.

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Wang lu yu yun duan ying yong: Google + O365 + Evernote yun duan huo yong jiao xue. Taibei Shi: Qi feng zi xun gu fen you xian gong si, 2016.

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Lay. Dropbox, Evernote, Google zui qiang yun duan huo yong shu. Taibei Shi: Qi biao chu ban gu fen you xian gong si, 2016.

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Yun duan wang ye cheng shi she ji: Google App Engine ying yong shi zuo. Xinbei Shi Xizhi Qu: Bo shuo wen hua gu fen you xian gong si, 2012.

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Ravulavaru, Arvind. Google Cloud AI Services Quick Start Guide: Build intelligent applications with Google Cloud AI services. Packt Publishing, 2018.

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Costa, Rui, and Drew Hodun. Google Cloud Cookbook: Practical Solutions for Building and Deploying Cloud Services. O'Reilly Media, Incorporated, 2022.

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Lanham, Micheal. Practical AI on the Google Cloud Platform: Utilizing Google's State-Of-the-Art AI Cloud Services. O'Reilly Media, Incorporated, 2020.

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Moreno, Sebastian. Google Cloud Certified Professional Cloud Developer Exam Guide: Modernize your applications using cloud-native services and best practices. Packt Publishing, 2021.

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Beginning Serverless Computing: Developing with Amazon Web Services, Microsoft Azure, and Google Cloud. Apress, 2017.

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Aggarwal, Vikas, and Aditya Patawari. Ansible 2 Cloud Automation Cookbook: Write Ansible playbooks for AWS, Google Cloud, Microsoft Azure, and OpenStack. Packt Publishing - ebooks Account, 2018.

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Book chapters on the topic "Google Cloud Services"

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Lloyd, Jeremy. "Other Google Services." In Infrastructure Leader’s Guide to Google Cloud, 331–37. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8820-7_30.

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Sabharwal, Navin, and Piyush Pandey. "Automation with Jenkins and GCP-Native CI/CD Services." In Pro Google Cloud Automation, 221–99. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6573-4_5.

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Shashi, Ashutosh. "Advanced GCP Services." In Designing Applications for Google Cloud Platform, 155–80. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9511-3_6.

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Cabianca, Dario. "Configuring Advanced Network Services." In Google Cloud Platform (GCP) Professional Cloud Network Engineer Certification Companion, 291–333. Berkeley, CA: Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9354-6_6.

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Lloyd, Jeremy. "Other Key Day 2 Products and Services." In Infrastructure Leader’s Guide to Google Cloud, 315–24. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8820-7_28.

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Bisong, Ekaba. "An Overview of Google Cloud Platform Services." In Building Machine Learning and Deep Learning Models on Google Cloud Platform, 7–10. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4470-8_2.

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Yuan, David Yu, and Tony Wildish. "Bioinformatics Application with Kubeflow for Batch Processing in Clouds." In Lecture Notes in Computer Science, 355–67. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59851-8_24.

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Abstract Bioinformatics pipelines make extensive use of HPC batch processing. The rapid growth of data volumes and computational complexity, especially for modern applications such as machine learning algorithms, imposes significant challenges to local HPC facilities. Many attempts have been made to burst HPC batch processing into clouds with virtual machines. They all suffer from some common issues, for example: very high overhead, slow to scale up and slow to scale down, and nearly impossible to be cloud-agnostic. We have successfully deployed and run several pipelines on Kubernetes in OpenStack, Google Cloud Platform and Amazon Web Services. In particular, we use Kubeflow on top of Kubernetes for more sophisticated job scheduling, workflow management, and first class support for machine learning. We choose Kubeflow/Kubernetes to avoid the overhead of provisioning of virtual machines, to achieve rapid scaling with containers, and to be truly cloud-agnostic in all cloud environments. Kubeflow on Kubernetes also creates some new challenges in deployment, data access, performance monitoring, etc. We will discuss the details of these challenges and provide our solutions. We will demonstrate how our solutions work across all three very different clouds for both classical pipelines and new ones for machine learning.
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Abraham, Anoop, and Jeong Yang. "A Comparative Analysis of Performance and Usability on Serverless and Server-Based Google Cloud Services." In Proceedings of the 2023 International Conference on Advances in Computing Research (ACR’23), 408–22. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33743-7_33.

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Reis, Arsénio, Dennis Paulino, Vitor Filipe, and João Barroso. "Using Online Artificial Vision Services to Assist the Blind - an Assessment of Microsoft Cognitive Services and Google Cloud Vision." In Advances in Intelligent Systems and Computing, 174–84. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77712-2_17.

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Garbarino, Ernesto. "Service Discovery." In Beginning Kubernetes on the Google Cloud Platform, 129–54. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5491-2_4.

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Conference papers on the topic "Google Cloud Services"

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Abrahao, Silvia, and Emilio Insfran. "Models@runtime for Monitoring Cloud Services in Google App Engine." In 2017 IEEE World Congress on Services (SERVICES). IEEE, 2017. http://dx.doi.org/10.1109/services.2017.14.

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Koh, John S., Jason Nieh, and Steven M. Bellovin. "Encrypted cloud photo storage using Google photos." In MobiSys '21: The 19th Annual International Conference on Mobile Systems, Applications, and Services. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3458864.3468220.

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"AN EFFICIENT GOOGLE DATASTORE ADAPTER FOR RICH INTERNET APPLICATIONS." In International Conference on Cloud Computing and Services Science. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003389104100417.

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Albertengo, Guido, Fikru Getachew Debele, Waqar Hassan, and Dario Stramandino. "On the Performance of Web Services and Google Cloud Messaging." In 2017 IEEE International Conference on Computer and Information Technology (CIT). IEEE, 2017. http://dx.doi.org/10.1109/cit.2017.66.

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Yue, Peng, Chongxin Guo, and Liangcun Jiang. "Using Google Fusion Tables for Cloud-based sensor observation services." In 2013 Second International Conference on Agro-Geoinformatics. IEEE, 2013. http://dx.doi.org/10.1109/argo-geoinformatics.2013.6621949.

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"SHARING SECURE DOCUMENTS IN THE CLOUD - A Secure Layer for Google Docs." In International Conference on Cloud Computing and Services Science. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003391904390444.

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Yang, Ching-Nung, Fu-Heng Wu, Sin-Yen Tsai, and Wen-Chun Kuo. "E-Health Services for Elderly Care Based on Google Cloud Messaging." In 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity). IEEE, 2015. http://dx.doi.org/10.1109/smartcity.2015.39.

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Gupta, Bulbul, Pooja Mittal, and Tabish Mufti. "A Review on Amazon Web Service (AWS), Microsoft Azure & Google Cloud Platform (GCP) Services." In Proceedings of the 2nd International Conference on ICT for Digital, Smart, and Sustainable Development, ICIDSSD 2020, 27-28 February 2020, Jamia Hamdard, New Delhi, India. EAI, 2021. http://dx.doi.org/10.4108/eai.27-2-2020.2303255.

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Alajmi, Qasim, Louay A. Al-Nuaimy, G. Jai Arul Jose, Mohammed Mastan, and Mohammed A. Al-Sharafi. "Cloud computing services and its Effect on tertiary education: Using Google Classroom." In 2019 7th International conference on ICT & Accessibility (ICTA). IEEE, 2019. http://dx.doi.org/10.1109/icta49490.2019.9144797.

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Bucur, Vlad, and Liviu Miclea. "Optimizing Towards a Multi-Cloud Environment through Benchmarking Data Transfer Speeds in Amazon Web Services and Google Cloud." In 2021 IEEE 17th International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 2021. http://dx.doi.org/10.1109/iccp53602.2021.9733705.

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Reports on the topic "Google Cloud Services"

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Korobeinikova, Tetiana I., Nataliia P. Volkova, Svitlana P. Kozhushko, Daryna O. Holub, Nataliia V. Zinukova, Tetyana L. Kozhushkina, and Sergei B. Vakarchuk. Google cloud services as a way to enhance learning and teaching at university. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3854.

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The article is devoted to the issue of a cloud-based learning system implementation as a powerful strategy for future specialists’ training at higher educational establishments. Using cloud computing in self-work management of the university courses is essential to equip students with a workload of appropriate educational materials and variable activities for professional training. Theoretical and empirical research methods were applied to select the appropriate services and tools for organizing students’ self-work at university. Critical analysis of scientific literature, synthesis of the data, didactic observation of the educational process, designing of the skeleton for university courses, questionnaires enabled to facilitate the study of the issue. G Suite has been chosen to enhance the quality of training of prospective specialists at a higher educational establishment. This paper introduces the outcomes of the project on applying Google Classroom in the management of students’ self-work while studying university courses. The focus of the first stage of the project was on testing pilot versions of the courses with the aim to work out the requirements and recommendations for incorporation general blended learning model of university courses. Particular attention is drawn to the designed model of the university course based on the curriculum with the necessary components of blended learning in the G Suite virtual environment. Cloud-based higher education is considered as a prospective tool for design of university courses with the need for further research and implementation.
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Oleksiuk, Vasyl P., Olesia R. Oleksiuk, Oleg M. Spirin, Nadiia R. Balyk, and Yaroslav P. Vasylenko. Some experience in maintenance of an academic cloud. [б. в.], June 2021. http://dx.doi.org/10.31812/123456789/4436.

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The article is devoted to the systematization of experience in the deployment, maintenance and servicing of the private academic cloud. The article contains model of the authors’ cloud infrastructure. It was developed at Ternopil Volodymyr Hnatiuk National Pedagogical University (Ukraine) on the basis of the Apache CloudStack platform. The authors identify the main tasks for maintaining a private academic cloud. Here they are making changes to the cloud infrastructure; maintenance of virtual machines (VM) to determine the performance and migration of VM instances; work with VMs; backup of all cloud infrastructure. The analysis of productivity and providing students with computing resources is carried out. The main types of VM used in training are given. The number and characteristics of VM that can be served by a private academic cloud are calculated. Approaches and schemes for performing backup are analysed. Some theoretical and practical experience of using cloud services to perform backup has been studied. Several scripts have been developed for archiving the platform database and its repositories. They allow you to upload backups to the Google Drive cloud service. The performance of these scripts for the author’s deployment of private cloud infrastructure was evaluated.
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Osadcha, Kateryna P., and Viacheslav V. Osadchyi. The use of cloud computing technology in professional training of future programmers. [б. в.], June 2021. http://dx.doi.org/10.31812/123456789/4435.

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The article provides a brief analysis of the current state of the study of cloud technologies by future software engineers at foreign and Ukrainian universities. The author experience in the application of cloud technologies in the training of future software engineers in Ukraine is presented. The application of cloud business automation systems, online services to monitor the implementation of the software projects, Google services for collaboration, planning and productivity while studying professional disciplines and carrying out diploma projects is described. Based on the survey conducted at Stackoverflow, the state of application of cloud technologies by software engineers around the world has been analyzed. The cloud technologies that are not studied at the analyzed universities of Ukraine and those that are not popular with software developers in the world, but studied at Ukrainian universities by future software engineers are outlined. Conclusions are made on the modernization of training programs for future software engineers. Topics for the study of cloud technologies by future software engineers in the content of professional disciplines are proposed.
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Shkodin, Andrey. Sets of exercises for education workers in the Far North. Science and Innovation Center Publishing House, April 2021. http://dx.doi.org/10.12731/shkodin.0418.15042021.

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Complexes of exercises for educational workers of the Far North is an electronic resource developed specifically for pedagogical workers living in the Far North. The selection and description of exercise complexes was developed on the basis of a study of the peculiarities of living in the Far North, common diseases characteristic of the inhabitants of the Far North and the peculiarities of the profession of a teacher. Access to the electronic resource is free, hosted on the google cloud service and youtube video hosting, contains video resources and comments on use. Available through a browser, no additional software required.
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