806 research outputs found

    Adapting Microservices in the Cloud with FaaS

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    This project involves benchmarking, microservices and Function-as-a-service (FaaS) across the dimensions of performance and cost. In order to do a comparison this paper proposes a benchmark framework

    Privacy-Preserving and Secure Pilot Self-Assessment

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    There is a strong culture for using safety risk management tools to monitor the different parts of the operation in the aviation industry. However, most of the monitoring that is being done is done on the technical conditions of the aircraft rather than on the pilot. While there is an expectation that the pilots self-assess their health regarding how fit the pilot is to operate an aircraft, all the tools given are checklist-based. Checklists are widely used in the aviation industry to ensure all tasks are done as they should for mechanics and pilots. However, the drawback of checklist-based systems is that they do not monitor anything over time. As a pilot has responsibility for many passengers on every flight, the consequences of mistakes can be considerable. By not monitoring the health over time, some of the crucial information when considering whether the pilot is fit to fly or not may be forgotten. Fatigue and stress are two essential topics for ensuring the focus is on operating the aircraft rather than either zoning out or being concerned about something else during flight. As the EU work hour regulations exempt everyone within the aviation industry, pilots can work at any time during the day. As the pilots can work at any given point during the day, they have to self-regulate whether they can work. If they do not track topics such as sleep and nutrition, they can be fatigued and lose focus on the work to be done. This thesis presents Gearggus, a self-assessment tool that can assist the pilot in assessing their health based on the information given by a questionnaire. The user answers questions based on how important the data is monitored over time. Based on the answers, there is calculated feedback on how ready the pilot is to operate an aircraft. The data is presented on a history page, so the user can see what the score is based on and how to adjust to gain a better score. Gearggus was evaluated with a qualitative interview with experienced aviation personnel and the Department of Aviation employees at the Unversity of Tromsø. Both parties acknowledge the issues Gearggus is trying to solve, but with modifications to the system. The required changes differ between the parties

    A cost effective school management system for disadvantaged schools in the Free State province using the software as a service (SaaS) delivery model

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    Thesis (M. Tech. (Information Technology)) -- Central University of technology, Free State, 2013The aim of this study was to create a dynamic software system that captures all information related to a student and delivers it to the educators, principal, higher authorities and parents. In order to achieve this aim, an investigation was launched as to the development of a cost-effective school management system for disadvantaged schools in the Free State Province using the Software as a Service (SaaS) delivery model. Although a variety of other school management systems exist in the market, they are often expensive and difficult to maintain. Details such as previous academic performances, disciplinary actions taken against a student in the current school, ailments the student suffers from and parental details are some of the information that will help an educator to better understand a student. The software that is currently in use fails to deliver this information. Designing the software as a multitenant system, helps accommodate different schools under the same database, while the shared database, shared schema reduces back-end costs. Database design was carried out in such a way that tenant data is logically isolated and that data integrity is maintained throughout. What makes the software explained in this study cost effective is the method of delivery that was employed, which is SaaS. Here, software is not purchased, there is no upfront capital and the yearly license fee is eliminated, as schools need only pay a monthly rental fee for the services they use. Since all services are provided through the Internet, there is no need for system space; the only requirement is a high-speed Internet network

    Designing Human-Centered Collective Intelligence

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    Human-Centered Collective Intelligence (HCCI) is an emergent research area that seeks to bring together major research areas like machine learning, statistical modeling, information retrieval, market research, and software engineering to address challenges pertaining to deriving intelligent insights and solutions through the collaboration of several intelligent sensors, devices and data sources. An archetypal contextual CI scenario might be concerned with deriving affect-driven intelligence through multimodal emotion detection sources in a bid to determine the likability of one movie trailer over another. On the other hand, the key tenets to designing robust and evolutionary software and infrastructure architecture models to address cross-cutting quality concerns is of keen interest in the “Cloud” age of today. Some of the key quality concerns of interest in CI scenarios span the gamut of security and privacy, scalability, performance, fault-tolerance, and reliability. I present recent advances in CI system design with a focus on highlighting optimal solutions for the aforementioned cross-cutting concerns. I also describe a number of design challenges and a framework that I have determined to be critical to designing CI systems. With inspiration from machine learning, computational advertising, ubiquitous computing, and sociable robotics, this literature incorporates theories and concepts from various viewpoints to empower the collective intelligence engine, ZOEI, to discover affective state and emotional intent across multiple mediums. The discerned affective state is used in recommender systems among others to support content personalization. I dive into the design of optimal architectures that allow humans and intelligent systems to work collectively to solve complex problems. I present an evaluation of various studies that leverage the ZOEI framework to design collective intelligence

    A Study of Very Short Intermittent DDoS Attacks on the Performance of Web Services in Clouds

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    Distributed Denial-of-Service (DDoS) attacks for web applications such as e-commerce are increasing in size, scale, and frequency. The emerging elastic cloud computing cannot defend against ever-evolving new types of DDoS attacks, since they exploit various newly discovered network or system vulnerabilities even in the cloud platform, bypassing not only the state-of-the-art defense mechanisms but also the elasticity mechanisms of cloud computing. In this dissertation, we focus on a new type of low-volume DDoS attack, Very Short Intermittent DDoS Attacks, which can hurt the performance of web applications deployed in the cloud via transiently saturating the critical bottleneck resource of the target systems by means of external attack HTTP requests outside the cloud or internal resource contention inside the cloud. We have explored external attacks by modeling the n-tier web applications with queuing network theory and implementing the attacking framework based-on feedback control theory. We have explored internal attacks by investigating and exploiting resource contention and performance interference to locate a target VM (virtual machine) and degrade its performance

    Upgrading decision support systems with Cloud-based environments and machine learning

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    Business Intelligence (BI) is a process for analyzing raw data and displaying it in order to make it easier for business users to take the right decision at the right time. Inthe market we can find several BI platforms. One commonly used BI solution is calledMicroStrategy, which allows users to build and display reports.Machine Learning (ML) is a process of using algorithms to search for patterns in data which are used to predict and/or classify other data.In recent years, these two fields have been integrated into one another in order to try to complement the prediction side of BI to enable higher quality results for the client.The consulting company (CC) where I have worked on has several solutions related to Data & Analytics built on top of Micro Strategy. Those solutions were all demonstrable in a server installed on-premises. This server was also utilized to build proofs of concept(PoC) to be used as demos for other potential clients. CC also develops new PoCs for clients from the ground up, with the objective of show casing what is possible to display to the client in order to optimize business management.CC was using a local, out of date server to demo the PoCs to clients, which suffered from stability and reliability issues. To address these issues, the server has been migrated and set up in a cloud based solution using a Microsoft Azure-based Virtual Machine,where it now performs similar functions compared to its previous iteration. This move has made the server more reliable, as well as made developing new solutions easier forthe team and enabled a new kind of service (Analytics as a Service).My work at CC was focused on one main task: Migration of the demo server for CCsolutions (which included PoCs for testing purposes, one of which is a machine learning model to predict wind turbine failures). The migration was successful as previously stated and the prediction models, albeit with mostly negative results, demonstrated successfully the development of large PoCs.Business Intelligence (BI) é um processo para analizar dados não tratados e mostrá-los para ajudar gestores a fazer a decisão correcta no momento certo. No mercado, pode-se encontrar várias plataformas de BI. Uma solução de BI comum chama-se MicroStrategy,que permite com que os utilizadores construam e mostrem relatórios.Machine Learning (ML) é um processo de usar algoritmos para procurar padrões em dados que por sua vez são usados para prever e/ou classificar outros dados.Nos últimos anos, estes campos foram integrados um no outro para tentar complementar o lado predictivo de BI para possibilitar resultados de mais alta qualidade para o cliente.A empresa de consultoria (EC) onde trabalhei tem várias soluções relacionadas com Data e Analytics construídas com base no MicroStrategy. Essas soluções eram todas demonstráveis num servidor instalado no local. Este servidor também era usado para criar provas de conceito (PoC) para serem usadas como demos para outros potenciais clientes.A EC também desenvolve novas PoCs para clientes a partir do zero, com o objectivo de demonstrar ao cliente o que é possível mostrar para optimizar a gestão do negócio.A EC estava a utilizar um servidor local desactualizado para demonstrar os PoCs aos clientes, que tinha problemas de estabilidade e fiabilidade. Para resolver estes problemas,o servidor foi migrado e configurado numa solução baseada na cloud com o uso de uma Máquina Virtual baseada no Microsoft Azure, onde executa funções semelhantes à versão anterior. Esta migração tornou o servidor mais fiável, simplificou o processo de desenvolver novas soluções para a equipa e disponibilizou um novo tipo de serviço (Analytics as a Service).O meu trabalho na EC foi focado numa tarefas principal: Migração do servidor de demonstrações de soluções CC (que inclui PoCs para propósitos de testes, uma das quais é um modelo de aprendizagem de máquina para prever falhas em turbinas eólicas). A migração foi efectuada com sucesso (como mencionado previamente) e os modelos testados,apesar de terem maioritariamente resultados negativos, demonstraram com sucesso que é possível desenvolver PoCs de grande dimensão

    Future proofing Lovelace system development environment

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    Abstract. Software development methods and tools improve continuously to improve the development process. Modern software architecture has paved the way for microservice based architecture. The main point of microservice architecture is to split a system to small independent parts that can be deployed separately without affecting the other parts of the system. With microservices and tools, a system can achieve fault tolerance, scalability and faster release cycle with automation. The use of container technologies has increased and popularized with microservices, because containers simplify the deployment process. In this project, a modern development environment was introduced to help future development of a Virtual Learning Environment. The development environment included a public repository, containers, a container registry, container orchestration, server confguration and automated deployment. After successful implementation the simple mock up system was tested by smoke, load and spike testing methods. Overall the implementation and confguration was successful, however for implementing it for the Lovelace system in University of Oulu’s environment, some confguration and tool choices may need to be changed.Lovelace järjestelmän modernisointi tulevaisuuden kehitykseen. Tiivistelmä. Ohjelmistokehityksen tavat ja työkalut kehittyvät jatkuvasti helpottamaan, sekä parantamaan ohjelmiston kehitysprosesseja. Moderni ohjelmistoarkkitehtuuri on luonut tietä mikropalveluarkkitehtuurille, jonka päätarkoituksena on pilkkoa järjestelmä pieniin lähes itsenäisiin osiin, joita voidaan erikseen kehittää vaikuttamatta järjestelmän muihin osiin. Mikropalveluiden ja muiden työkalujen avulla järjestelmä saavuttaa vikasietoisuutta, skaalautuvuutta sekä nopeamman julkaisusyklin automaation ansiosta. Konttiteknologioiden käyttö on myös yleistynyt mikropalveluiden myötä, jotka helpottaa ohjelmiston toimittamista servereille. Tämän projektin aikana implementoitiin moderni kehitysympäristö helpottamaan jatkokehitystä Lovelace systeemille. Kehitysympäristö sisälsi julkisen säilön, kontin, kontti rekisterin, konttien orkesterointi työkalun, serveri confguroinnin ja automaattisen sijoituksen. Onnistuneen implementaation jälkeen, yksinkertainen järjestelmä testattiin savu, kuorma ja piikki testi metodeilla. Kokonaisuudessaan implementaatio ja confgurointi onnistuivat, mutta Lovelace implementaatio Oulun Yliopiston ympäristöön vaatii confgurointi muutoksia ja mahdollisesti muutamien työkalujen vaihtamista

    Plataforma de serviços para monitorização da cadeia de valor do pescado

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    Traceability in the food value chain is a topic of interest due to the advantages it brings to both the consumers, producers and regulatory authorities. This thesis describes my contributions during the design and implementation of a microservice based middleware for the Portuguese fish value chain considering current practices in the industry and the requirements of the stakeholders involved in the project, with the goal of integrating all the traceability information available from each operator to provide customers with the full story of the products they purchase. During this project I assumed many roles such as development, operations and even some security allowing me to improve my skills in all these fields and experimenting with the latest cloud native technologies such as containers and with DevOps practices.A rastreabilidade na cadeia de valor alimentar é um tema de interesse pelas vantagens que traz aos consumidores, produtores e autoridades reguladoras. Esta dissertação descreve as minhas contribuições durante a conceção e implementação de um middleware baseado em micro-serviços para a cadeia de valor do pescado portuguesa considerando as práticas atuais da indústria e os requisitos das partes interessadas envolvidas no projeto, com o objetivo de integrar toda a informação de rastreabilidade disponível de cada um dos operadores para fornecer aos clientes a história completa dos produtos que adquirem. Durante este projeto, assumi muitas funções, como desenvolvimento, operações e até mesmo alguma segurança, o que me permitiu melhorar as minhas capacidades em todos essas disciplinas e experimentar as mais recentes tecnologias nativas da nuvem, como contentores e práticas de DevOps.Mestrado em Engenharia Informátic
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