Дисертації з теми "AWS – Amazon Web Service"

У збірнику подано тези доповідей восьмої міжнародної науково-технічної конференції “Проблеми інформатизації”. Розглянуті питання за такими напрямами: інформатизація навчального процесу; безпека функціонування, застосування та експлуатація телекомунікаційних систем та мереж; комп’ютерні методи і засоби інформаційних технологій та управління; методи швидкої та достовірної обробки даних в комп’ютерних системах та мережах; сучасні інформаційновимірювальні системи; цивільна безпека (інформаційна підтримка).

Context MongoDB is an open-source, scalable, NoSQL database that distributes the data over many commodity servers. It provides no single point of failure by copying and storing the data in different locations. MongoDB uses a master-slave design rather than the ring topology used by Cassandra. Virtualization is the technique used for accessing multiple machines in a single host and utilizing the various virtual machines. It is the fundamental technology, which allows cloud computing to provide resource sharing among the users. Objectives Studying and identifying MongoDB, Virtualization on AWS and OpenStack. Experiments were conducted to identify the CPU utilization associated when Mongo DB instances are deployed on AWS and physical server arrangement. Understanding the effect of Replication in the Mongo DB instances and its effect on MongoDB concerning throughput, CPU utilization and latency. Methods Initially, a literature review is conducted to design the experiment with the mentioned problems. A three node MongoDB cluster runs on Amazon EC2 and OpenStack Nova with Ubuntu 16.04 LTS as an operating system. Latency, throughput and CPU utilization were measured using this setup. This procedure was repeated for five nodes MongoDB cluster and three nodes production cluster with six types of workloads of YCSB. Results Virtualization overhead has been identified in terms of CPU utilization and the effects of virtualization on MongoDB are found out in terms of CPU utilization, latency and throughput. Conclusions It is concluded that there is a decrease in latency and increases throughput with the increase in nodes. Due to replication, increase in latency was observed.

During the COVID-19 pandemic the questions of the efficiency around meetings has been in the forefront of some discussion inside companies. One way to measure efficiency is to measure the interactivity between different participants. In order to measure this the participants need to be identified. With the recent spike of Machine learning advancements, is this something that can be done using facial and voice recognition? Another field that has risen to the top is cloud computing. Can machine learning and cloud computing be used to evaluate and monitor a meeting, thus handling both audio and video streams in a real time environment? The conclusion of this thesis is that Artificial Intelligence(AI) can be used to monitor a meeting. To be able to do so Amazon Web Service (AWS) can be utilized. The choice of using a DeepLens was however not best choice. A hardware like DeepLens is required, but with better integration with cloud computing, as well with more freedom regarding the usage of several models for handling both feeds. With the usage of other models to automatic annotate data the time needed for training a new model can be reduced. The data generated during a single meeting is enough with the help of transfer learning from Amazon web service to build a model for facial identification and detection.

In this thesis, a scalable, highly available and reactive RabbitMQ cluster is implemented on Amazon Web Services (AWS) and Microsoft Azure. An alternative solution was created on AWS using the CloudFormation service. These solutions are performance tested using the RabbitMQ PerfTest tool by simulating high loads with varied parameters. The test results are used to analyze the throughput and price-performance ratio for a chosen set of instances on the respective cloud platforms. How performance changes between instance family types and cloud platforms is tested and discussed. Additional conclusions are presented regarding the general performance differences in infrastructure between AWS and Microsoft Azure.

This thesis explores the creation of a scaling, containerized, advertisement server that will be used by Gold Town Games AB to better integrate ads into their application(s). The server is built as a Docker image that will be used to create server instances on AWS Elastic Container Service for automatic scaling and server resource configuration. The server was created with the intention that GTG will have full control over what advertisements are shown in their application(s) and to seamlessly integrate sponsored logos onto jerseys or sports fields. This will not only serve as a source of income with advertisers paying for ad space, but it will also make the game elements more realistic as we have come to expect teams and stadiums to be sponsored and plastered with company logos. Another important part when displaying advertisement is to track statistics for the ads, since without a way to show advertisers that their ads are shown and that they are generating engagement it is very hard to sell the ad space.
Detta examensarbete utforskar skapandet av en skalbar, containerbaserad, reklamserver som kommer användas av Gold Town Games AB för att integrera reklam i deras applikation(er). Servern är byggd som en Docker-bild som används för att skapa instanser på AWS Elastic Container Service för automatisk skalning och serverresurshantering. Servern är utvecklad med tanken att GTG ska ha full kontroll över vilken reklam som visas i deras applikation(er) och för att kunna lägga till sponsrade loggor på matchtröjor och i arenor. Detta är inte bara en extra form av inkomst, då annonsörer betalar för reklamplatser, utan hjälper även till att få delar av spelen att kännas mer realistiska då vi är vana att lag och arenor är sponsrade och fulla av företagsloggor. En annan viktig del när man visar reklam är att kunna spara statistik för den, eftersom det skulle vara väldigt svårt att sälja reklamplatser utan att kunna visa att folk faktiskt ser reklamen.

This master’s thesis evaluates automatic data annotation and machine learning predictions of iron ore pellets using tools provided by Amazon Web Services (AWS) in the cloud. The main tool in focus is Amazon SageMaker which is capable of automatic data annotation as well as building, training and deploying machine learning models quickly. Three different models was trained using SageMakers built in semantic segmentation algorithm, PSP, FCN and DeepLabV3. The dataset used for training and evaluation contains 180 images of iron ore pellets collected from LKAB’s experimental blast furnace in Luleå, Sweden. The Amazon Web Services solution for automatic annotation was shown to be of no use when annotating microscopic images of iron ore pellets. Ilastik which is an interactive learning and segmentation toolkit showed far superiority for the task at hand. Out of the three trained networks Fully-Convolutional Network (FCN) performed best looking at inference and training times, it was the quickest network to train and performed within 1% worse than the fastest in regard to inference time. The Fully-Convolutional Network had an average accuracy of 85.8% on the dataset, where both PSP & DeepLabV3 was showing similar performance. From the results in this thesis it was concluded that there are benefits of running deep neural networks as a cloud service for analysis and management ofiron ore pellets.

Мета роботи: створити систему, за допомогою якої можна перевіряти формальні граматики на граматичні помилки, а також побудувати синтаксичні діаграми. Робота складається з п’яти розділів. У першому розділі формується постановка задачі та розповідається про загальну структуру системи. У другому розділі робиться огляд предметної області та існуючих систем, проводиться їх аналіз. У третьому розділі вказується інструменти за допомогою яких була побудована система. У четвертому розділі описується програмна реалізація системи, в якій розповідається про архітектуру системи, її окремі компоненти та модулі. У п’ятому розділі демонструються приклади з використання системи, наводиться алгоритм взаємодії користувача з системою, і водночас є інструкцією з використання системи.
Purpose of the work is creating a system that can be used to check formal grammars for grammatical errors, as well as to build syntactic diagrams. The work consists of five sections. In the first section the problem statement is formed and the general structure of the system is told. The second section reviews the subject area and existing systems, analyzes them. The third section indicates the tools with which the system was built. The fourth section describes the software implementation of the system, which describes the architecture of the system, its individual components and modules. The fifth section shows examples of how to use the system, provides an algorithm for user interaction with the system, and at the same time provides instructions for using the system.
Цель работы: создать систему, с помощью которой можно проверять формальные грамматики на грамматические ошибки, а также построить синтаксические диаграммы. Работа состоит из пяти разделов. В первом разделе формируется постановка задачи и рассказывается об общей структуре системы. Во втором разделе делается обзор предметной области и существующих систем, проводится их анализ. В третьем разделе указывается инструменты с помощью которых была построена система. В четвертом разделе описывается программная реализация системы, в которой рассказывается об архитектуре системы, ее отдельные компоненты и модули. В пятом разделе демонстрируются примеры по использованию системы, приводится алгоритм взаимодействия пользователя с системой, и одновременно является инструкцией по использованию системы.

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.

The objective of this thesis was to explore the topic of scalable web development, and it answered the question, “How do you scale a website to handle more traffic at peak times without wasting resources?” This is important research to any web company that has issues with rising costs as demand for their website increases. It would be wise for every online business to be prepared for more web traffic, before it occurs, without spending the budget of a multi-million user web company in low traffic periods. The last thing you want is an error as your customer base starts to arrive, giving them a bad experience for their first impressions, which would result in lost revenue.

Scalable software development architectures, including microservices, big data, and Kubernetes were studied, in addition to similar web service companies including Facebook, Twitter, and Match.com. A scalable architecture was designed for a social media web service, MeAndYou, using the big data configuration with a shared Aurora database, which was configured using an auto-scaling group attached to a load balancer in Amazon Web Services (AWS). It was tested using a custom threaded Selenium-based Python script that applied simulated user load to the servers. As the load was applied, AWS added more Elastic Compute Cloud (EC2) instances running a virtual disk image of the web server. After the load was removed, the instances were terminated automatically by AWS to save costs.

Countless steps were taken to make the web service bigger and more scalable than it originally was, before testing, including adding more fields to user profiles, adding more search types, and separating the layers of code into different Hypertext Preprocessor (PHP) files in the front-end. A version control system was configured on the servers using GitHub and rsync. The systems architecture designed suggests the Match Engine should use a stream processing message queue, which would allow the system to factor searches one at a time as they are created, with horizontal scaling capabilities, rather than grabbing the entire database and storing it in memory. The backend Match Engine was also tested for accuracy using Structured Query Language (SQL) injection, which determined how the match algorithm should be improved in the future.

Data is one of the most contributing factors for modern business operations. Having bad data could therefore lead to tremendous losses, both financially and for customer experience. This thesis seeks to find anomalies in real-world, complex, structured data, causing an international enterprise to miss out on income and the potential loss of customers. By using graph theory and similarity analysis, the findings suggest that certain countries contribute to the discrepancies more than other countries. This is believed to be an effect of countries customizing their products to match the market’s needs. This thesis is just scratching the surface of the analysis of the data, and the number of opportunities for future work are therefore many.

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.

Säkerhetskopiering är ett sätt att kunna skydda data genom att kopiera och spara data på till exempel hårddiskar, externa servrar eller i molnet. Att spara på egna servrar kan dock vara en kostsam lösning för företag och organisationer, både inköp av servrar, installationer samt underhåll kan göra att kostnaderna drar iväg. Med säkerhetskopiering i molnet försvinner stora delar av den kostnaden, inga servrar behöver köpas in och därmed behövs inte heller något underhåll som installationer, byte av hårddiskar med mera, skötas eftersom molnleverantören sköter all hårdvara. Det finns många aspekter att tänka på innan en flytt görs till en molntjänst. Saker som kostnad, säkerhet, topologi och att automatisera säkerhetskopieringen kan påverkas av valet av molntjänst. Det är därför av extra stor vikt att ha säkerheten i åtanke då den data som sparas och kopieras kan innehålla känslig information som inte bör komma ut till andra då detta kan skada företaget eller organisationen. Därför bör dessa aspekter ses över innan valet av molntjänst görs. Denna rapport syftar att lyfta fram information om säkerhetskopiering och molntjänster för företag som står vid ett vägskäl och ska till att välja molntjänst. Fokus för rapporten ligger på att kostnad, säkerhet och automatisering av säkerhetskopiering mot molntjänster. Rapporten passar därmed åt företag som är intresserade av molntjänster och söker information på vad som är viktigt att tänka på innan en molntjänst väljs. Säkerhetsanalysen kommer bestå av en riskanalys som utgår från Cloud Security Alliance (CSA) forskning där de tagit fram de vanligaste hoten kring molntjänster och en utvärdering kring hur stor dessa risker är kommer att göras med hjälp av en matris. Genom att dra paralleller mot existerande molnföretag ska läsaren kunna få en känsla av hur säkerhetsriskerna kan inträffa och vad som behövs för att undvika riskerna. Vidare tas kriterierna för molntjänster upp och jämförs genom ett stapeldiagram där de viktigaste kriterierna presenteras och sedan diskuteras. Kriterierna tas fram genom sökningar via sökmotorer där en fras används för att se vad som olika skribenter och företag anser vara de viktigaste kriterierna. För kostnaden görs en jämförelse mellan olika molnleverantörer och deras priser kring molntjänsterna. Ett scenario för användning av olika resurser som till exempel GB per nedladdning eller anrop kommer att tas fram baserat på de praktiska delarna i studien. Detta scenario används sedan som bas för att ta fram en ungefärlig kostnad per år för de olika molntjänsterna. Till sist redovisas också ett skript för säkerhetskopieringen för att demonstrera hur en säkerhetskopieringslösning i molnet skulle kunna se ut. Detta genom att jobba med verktyget Amazon Web Service (AWS) där en virtuell miljö sats upp och sedan säkerhetskopierats mot AWS. Slutsatsen för rapporten visar på att säkerhet ofta är något som förbises av användare, medvetet eller ej. Både kriterier och risker-analysen i rapporten lyfter fram säkerhet som en av de viktigaste delarna i molntjänster. För kriterier togs topologi fram som den viktigaste aspekten kring molntjänster, där säkerhet och support kom delad tvåa. Vidare tog riskanalysen upp avsaknaden av säkerhet inom molntjänster och lyfte fram riktiga händelser som påverkat stora företag. Det behövs mer kunskap inom säkerhet för många företag och organisationer eftersom de kan förbise detta till förmån för enklare lösningar. Detta trots att säkerhet rankas som en av de viktigaste aspekterna vid val av molntjänst, tillsammans med topologi och support. Trots att säkerhetsbristen tas upp i rapporten verkar det inte vara saknaden av verktyg som är felet, det finns mycket att jobba med inom molntjänster och det går att implementera säkerhetslösningar bara viljan finns. Vidare är kostnaden mellan olika molntjänster oftast inte är alltför stor. De allra flesta molntjänster som jämförts i rapporten ser till att hålla en rimlig kostnad som liknar dess konkurrent. De etiska aspekter som tagits upp i rapporten handlar om ansvar och frågan om vem ansvaret ligger på tas upp. Här handlar argumentet om huruvida det är molnleverantörens ansvar att se till att förenkla och verkligen se till att kunder förstår och använder sig av säkerhetslösningarna de tagit fram, eller om det är företagens egna ansvar att se till att känslig information som kunduppgifter inte kommer ut.
Backup is a way of protecting data through copying and saving data on for an example hard drives, external servers or in the cloud. To save the backup on local servers can be a costly solution for business and organisations since the cost of buying servers, installation and maintenance may cause the cost to increase. With backups in the cloud some of that cost disappears as no servers needs to be bought and without the need for servers there is no need for maintenance like installation, change of hardware etcetera. This is because all of that is taken care of by the cloud service provider. But there are a lot of things to consider before moving to the cloud. Things such as cost, security, topology and the option to automatizate the backup could all affect the choice of cloud service. This is why it is of a great importance to have security in mind since the data that backups could contain sensitive information that could harm the business or organisation, if leaked. This report has the purpose to produce information regarding backups and cloud services for business and organisations that are at a crossroad trying to pick a cloud service provider. The focus of the report lies on cost, security and to automate the process of backups to the cloud. Therefore the report is suited for business that are interested in cloud services and that are on the lookout for information on what is important to think about before they choose a cloud service provider. The security analysis will consist of a risk analysis that is using the study done by Cloud Security Alliance as a base. They have through a study produced a paper on the twelve most common risk with cloud services, there will be evaluated in the report and placed on a matrix. By comparing the risk and drawing parallels to existing cloud service providers it looks to give the reader more knowledge around risks in cloud services and how to avoid them. Further the criterias for the cloud services will be brought up and compared through a staple diagram which will then be discussed. The criterias was made through searches via a search engine using a phrase to see what writers and companies thought were the most important criterias. The cost of the cloud services was done through a comparison between different cloud service provider and their cloud services. A user-case scenario was made based on the practical parts of the study where costs such as GB per download and request were part of the final yearly cost for each of the cloud service. Lastly there’s a script for the backup to demonstrate how a backup solution in a cloud could look like. This is done through working with the tool Amazon Web Service (AWS) in a virtual environment that would backup to AWS. Bigger parts of the inquiry shows that security is often overlooked which is part of the conclusion in the report. Both the criteria and the risk-analysis in the report lifts the security aspects as one of the most important part of cloud services. For criterias it shows topology as the most important aspect in choosing a cloud service, security and support being second. Furthermore the risk analysis brought up the lack of security in cloud services and gives examples from real life events where big companies have had their security breach. More knowledge is needed when it comes to security, for a lot of companies and organisation since they can often overlook security in favor of a simpler solution. Even though security is one of the most important aspects when choosing a cloud service and should be treated as such. Despite the lack of security being brought up in the report it seems there are a lot of tools to work with within the cloud services, as long as the will exist to work with these tools. It is not the lack of tools but rather the ignorance or lack of understanding of security that is the cause of the problem. As for the cost it turns out to not be such a big difference, it is only ranked as the third most important thing when choosing a cloud service and during the comparison it shows that most cloud services sticks to similar prices as their competition. The ethical aspects in the report is about responsibility and the question on who is responsible is brought up. Here the argument is about whether or not it is the cloud service providers responsibility to make sure their users understand the security tools they provide or to make sure they are enforced. Or if it is the users responsibility to make use of the tools they are given and to not think too lightly of security.

As the IoT has grown in popularity, modern Cloud Providers have begun offering IoT-specific features. These features could provide IoT developers with a lot of aid, but they are new and relatively unexplored. This study aims to explore these IoT-specific features in order to build a better foundation for developers looking to leverage them to improve their IoT systems or development process. To do this, a literature study combined with a case study was carried out. The literature study gives insight into the current research around IoT and IoT cloud providers, for example with common issues and possible applications. The case study provides a reference of how a simple proof-of-concept system can be developed using two of the available providers: AWS IoT and Azure IoT.

Usage of cloud-based storage systems gained a lot of prominence in fast few years. Every day millions of files are uploaded and downloaded from cloud storage. This data that cannot be handled by traditional databases and this is considered to be Big Data. New powerful platforms have been developed to store and organize big and unstructured data. These platforms are called Big Data systems. Some of the most popular big data platform are Mongo, Hadoop, and Cassandra. In this, we used Cassandra database management system because it is an open source platform that is developed in java. Cassandra has a masterless ring architecture. The data is replicated among all the nodes for fault tolerance. Unlike MySQL, Cassandra uses per-column basis technique to store data. Cassandra is a NoSQL database system, which can handle unstructured data. Most of Cassandra parameters are scalable and are easy to configure. Amazon provides cloud computing platform that helps a user to perform heavy computing tasks over remote hardware systems. This cloud computing platform is known as Amazon Web Services. AWS services also include database deployment and network management services, that have a non-complex user experience. In this document, a detailed explanation on Cassandra database deployment on AWS platform is explained followed by Cassandra performance tuning.    In this study impact on read and write performance with change Cassandra parameters when deployed on Elastic Cloud Computing platform are investigated. The performance evaluation of a three node Cassandra cluster is done. With the knowledge of configuration parameters a three node, Cassandra database is performance tuned and a draft model is proposed.             A cloud environment suitable for the experiment is created on AWS. A three node Cassandra database management system is deployed in cloud environment created. The performance of this three node architecture is evaluated and is tested with different configuration parameters. The configuration parameters are selected based on the Cassandra metrics behavior with the change in parameters. Selected parameters are changed and the performance difference is observed and analyzed. Using this analysis, a draft model is developed after performance tuning selected parameters. This draft model is tested with different workloads and compared with default Cassandra model. The change in the key cache memory and memTable parameters showed improvement in performance metrics. With increases of key cache size and save time period, read performance improved. This also showed effect on system metrics like increasing CPU load and disk through put, decreasing operation time and The change in memTable parameters showed the effect on write performance and disk space utilization. With increase in threshold value of memTable flush writer, disk through put increased and operation time decreased. The draft derived from performance evaluation has better write and read performance.

Cette thèse a pour but l'enrichissement des méthodes supervisées d'analyse de données symboliques et l'extension de ce domaine aux données volumineuses, dites "Big Data". Nous proposons à cette fin une méthode supervisée nommée HistSyr. HistSyr convertit automatiquement les variables continues en histogrammes les plus discriminants pour les classes d'individus. Nous proposons également une nouvelle méthode d'arbres de décision symbolique, dite SyrTree. SyrTree accepte tous plusieurs types de variables explicatives et à expliquer pour construire l'arbre de décision symbolique. Enfin, nous étendons HistSyr aux Big Data, en définissant une méthode distribuée nommée CloudHistSyr. CloudHistSyr utilise Map/Reduce pour créer les histogrammes les plus discriminants pour des données trop volumineuses pour HistSyr. Nous avons testé CloudHistSyr sur Amazon Web Services (AWS). Nous démontrons la scalabilité et l’efficacité de notre méthode sur des données simulées et sur les données expérimentales. Nous concluons sur l’utilité de CloudHistSyr qui , grâce à ses résultats, permet l'étude de données massives en utilisant les méthodes d'analyse symboliques existantes
This Thesis proposes new supervised methods for Symbolic Data Analysis (SDA) and extends this domain to Big Data. We start by creating a supervised method called HistSyr that converts automatically continuous variables to the most discriminant histograms for classes of individuals. We also propose a new method of symbolic decision trees that we call SyrTree. SyrTree accepts many types of inputs and target variables and can use all symbolic variables describing the target to construct the decision tree. Finally, we extend HistSyr to Big Data, by creating a distributed method called CloudHistSyr. Using the Map/Reduce framework, CloudHistSyr creates of the most discriminant histograms for data too big for HistSyr. We tested CloudHistSyr on Amazon Web Services. We show the efficiency of our method on simulated data and on actual car traffic data in Nantes. We conclude on overall utility of CloudHistSyr which, through its results, allows the study of massive data using existing symbolic analysis methods

Кулініч, Р. В. Використання систем штучного інтелекту для фізичного захисту підприємства : дипломна робота : 125 – Кібербезпека / Р. В. Кулініч ; керівник роботи В. І. Гур’єв ; НУ "Чернігівська політехніка", кафедра кібербезпеки та математичного моделювання. – Чернігів, 2021. – 56 с.
Актуальність роботи. В період пандемії, як ніколи раніше, стають все актуальнішими освітні онлайн ресурси, які роблять вивчення будь-якої предметної області більш ефективнішим для безпечним для людського здоров'я. Метою роботи розробити веб ресурс для проведення та відвідування онлайн курсів, який забезпечує повну захищеність даних користувачів. Завдання дослідження: − проаналізувати предметну область та веб-продукти цього цієї доменної області; − розробити та візуально зобразити за допомогою діаграм, структуру створюваної платформи; − зібрати та оформити функціональні та нефункціональні вимоги до розроблюваної платформи; − створити та зобразити архітектуру розроблюваної платформи; − створити мокапи розроблюваної платформи; − розробити програмний продукт, що буде відповідати поставленим вимогам. Об’єктом дослідження є процес розробки веб-платформи, для проведення та вивчення дисциплін онлайн. Предметом дослідження є популярні освітні веб-сервіси. У роботі було використано методи дослідження: порівняльний, науковий та аналітичний. Наукова новизна одержаних результатів полягає у створенні цілком захищеної веб-платформи для розміщення, проведення, вивчення дисциплін онлайн. Практичне значення отриманих результатів. Розробка цілком захищеної освітньої веб-платформи, на базі якої можна проводити курси, складати іспити, отримувати сертифікати.
Relevance of work. During the pandemic, more than ever, online educational resources are becoming increasingly relevant, making the study of any subject area more effective for safe for human health. The aim of the work is to develop a web resource for conducting and attending online courses that provides complete protection of user data. Objectives of the study: − analyze the subject area and web products of this domain area; − develop and visually depict with the help of diagrams, the structure of the created platform; − collect and design functional and non-functional requirements for the developed platform; − create and depict the architecture of the developed platform; − to create mockups of the developed platform; − develop a software product that will meet the requirements. The object of research is a process of developing a web platform for conducting and studying disciplines online. The subjects of the study are popular educational web services. The research methods were used in the work: comparative, scientific and analytical. The scientific novelty of the obtained research results is to create a fully secure web platform for hosting, conducting, studying disciplines online. The practical significance of the results. Development of a fully secure educational web platform, on the basis of which you can conduct courses, take exams, receive certificates.

La tesi si occupa di documentare il deployment di un ambiente di sviluppo software su cloud. In partcolare si tratta dell'ambiente CAS, un progetto di un gruppo di ricerca del Dipartimento di Informatica dell'Università di Bologna. Per portare CAS su cloud viene utilizzata la tecnologia Docker e i servizi cloud offerti da Amazon Web Service.

In a heavy vehicle, internet connection is not reliable, primarily because the truck often travels to a remote location where network might not be available. The data generated from the sensors in a vehicle might not be sent to the internet when the connection is poor and hence it would be appropriate to store and do some basic computation on those data in the heavy vehicle itself and send it to the cloud when there is a good network connection. The process of doing computation near the place where data is generated is called Edge computing. Scania has its own Edge computation solution, which it uses for doing computations like preprocessing of sensor data, storing data etc. Scania’s solution is compared with a commercial edge computing platform called as AWS (Amazon Web Service’s) Greengrass. The comparison was in terms of Data efficiency, CPU load, and memory footprint. In the conclusion it is shown that Greengrass solution works better than the current Scania solution in terms of CPU load and memory footprint, while in data efficiency even though Scania solution is more efficient compared to Greengrass solution, it was shown that as the truck advances in terms of increasing data size the Greengrass solution might prove competitive to the Scania solution.One more topic that is explored in this thesis is Digital twin. Digital twin is the virtual form of any physical entity, it can be formed by obtaining real-time sensor values that are attached to the physical device. With the help of sensor values, a system with an approximate state of the device can be framed and which can then act as the digital twin. Digital twin can be considered as an important use case of edge computing. The digital twin is realized with the help of AWS Device shadow.
I ett tungt fordonsscenario är internetanslutningen inte tillförlitlig, främst eftersom lastbilen ofta reser på avlägsna platser nätverket kanske inte är tillgängligt. Data som genereras av sensorer kan inte skickas till internet när anslutningen är dålig och det är därför bra att ackumulera och göra en viss grundläggande beräkning av data i det tunga fordonet och skicka det till molnet när det finns en bra nätverksanslutning. Processen att göra beräkning nära den plats där data genereras kallas Edge computing. Scania har sin egen Edge Computing-lösning, som den använder för att göra beräkningar som förbehandling av sensordata, lagring av data etc. Jämförelsen skulle vara vad gäller data efficiency, CPU load och memory consumption. I slutsatsen visar det sig att Greengrass-lösningen fungerar bättre än den nuvarande Scania-lösningen när det gäller CPU-belastning och minnesfotavtryck, medan det i data-effektivitet trots att Scania-lösningen är effektivare jämfört med Greengrass-lösningen visades att när lastbilen går vidare i Villkor för att öka datastorleken kan Greengrass-lösningen vara konkurrenskraftig för Scania-lösningen. För att realisera Edge computing används en mjukvara som heter Amazon Web Service (AWS) Greengrass.Ett annat ämne som utforskas i denna avhandling är digital twin. Digital twin är den virtuella formen av någon fysisk enhet, den kan bildas genom att erhålla realtidssensorvärden som är anslutna till den fysiska enheten. Med hjälp av sensorns värden kan ett system med ungefärligt tillstånd av enheten inramas och som sedan kan fungera som digital twin. Digital twin kan betraktas som ett viktigt användningsfall vid kantkalkylering. Den digital twin realiseras med hjälp av AWS Device Shadow.

Task of this master thesis is to create system for support of automated testing and deploying of server applications according to requirements defined by company AVG. The main target is create system for deploying and testing that automatically or manually test the server application in the cloud environment with ability of final deployment to the production environment with overall progress monitoring. Automated testing and deploying system has been split to the two parts. The first part is user interface that allows adding new applications, testing applications and deploying applications to its production environment. The second part represents the REST service which process testing and deploying tasks and  store progress to database. System was tested with sample server's applications in Amazon Web Services cloud environment. Thesis describes general testing principles cross multiple areas. Also it contains general architecture withing diagrams, which shows use cases. At the end of thesis is described testing of samples application together with results.

碩士
國立交通大學
資訊科學與工程研究所
102
Cloud computing has been a commonplace and widely used in both technological and business world. The number of cloud providers has been constantly growing and so has the set of options for end users. There have also been some outstanding applications such as the invention of Dropbox, a file hosting service that offers cloud storage and file synchronization. With a great deal of knowledge on cloud computing from some courses that I took in the master program and as a keen observer, I realized that such systems as the course selection system (for example the one that operates at NCTU) or the railway ticket system back in my country, Vietnam, do not work well sometimes. There are times that websites are overloaded to a point when their services are degraded or disrupted entirely. This web traffic overload happens when there is a large surge in traffic to a particular website causing a dramatic increase in server load and putting severe strain on the network links leading to the server. For example many students access the course selection system at the time the school lets the students select their courses on it. Also, many people want to buy railway tickets on railway ticket system at the time closing to New Year vacation when they want to go home. For those systems, is there any practical way to improve them with low cost but high degree of effectiveness? Amazon Web Services (AWS), a cloud computing provider, provides a number of services with low cost and rapid elasticity. Therefore, this should be considered as a choice for us. In this research, I am going to conduct several experiments with an aim to migrate a sample system like a course selection system to AWS and evaluate the effects of the new system. Furthermore, I expect to propose a model for the new system on the cloud which includes one domain name system web services, three load balancers, three to eighteen web servers, and one database server. The number of web servers might be changed depending on how many users access the new system at the same time. It’s assumed that the original system has the capacity bandwidth of 1Gbps. As well as that if we compare the new system with the original one, the capacity of the new system is approximately 3 times as big as that of the original system. Furthermore, the cost of new system is relatively low estimated at $600 for every semester. After doing this research, I have reached the conclusion that the overload system may be improved by applying cloud computing technology.

Este trabalho visa estudar e documentar a análise de serviços de Data Warehouse para a sua integração num sistema de Big Data em desenvolvimento pela Critical TechWorks (CTW), com o objetivo de ser implementado no sistema financeiro da Bayerische Motoren Werke (BMW) no Reino Unido, dando suporte às decisões de negócio, personalizando e melhorando assim o serviço prestado pela BMW.
This work aims to study and report an analysis of Data Warehouse services to be intgrated on a Big Data system in development by CTW. This system has the main objective of being integrated on the BMW financial system on United Kingdom, giving support to business decisions, giving better and custom service to BMW clients.

Neste documento descreve-se o estágio curricular desenvolvido no âmbito da Unidade Curricular de Estágio do Mestrado de Engenharia Informática - Computação Móvel, pertencente ao Instituto Politécnico de Leiria, e realizado no Grupo Lusiaves SGPS,SA, no período compreendido entre Outubro de 2019 e Junho de 2020. Durante o período de estágio o objetivo foi de desenvolver mecanismos de Continuous Integration e Continuous Development, assim como o desenho e migração de aplicações monolíticas para containers . Adicionalmente, foi também essencial desenvolver aptidões relativas à Plataforma de Integrações utilizada na Empresa (Boomi), assim como aptidões ao nível de infraestrutura de empresa (AWS). Durante a duração do estágio e com recurso a uma metodologia ágil, foi possível implementar ferramentas, tecnologias e métodos que permitiram concretizar o objetivo inicial de se ter processos de Continuous Integration, Continuous Development e de contentores escaláveis. Foi possível implementar algumas soluções que ajudaram não só na qualidade dos dados de logging, como a solução de contentorização pretendida, assim como mecanismos que simplificaram o Continuous Integration e o Continuous Development.