Comparison of Architectures and Performance of Database Replication Systems

One of the most demanding needs in cloud computing and big data is that of having scalable and highly available databases. One of the ways to attend these needs is to leverage the scalable replication techniques developed in the last decade. These techniques allow increasing both the availability and scalability of databases. Many replication protocols have been proposed during the last decade. The main research challenge was how to scale under the eager replication model, the one that provides consistency across replicas. This thesis provides an in depth study of three eager database replication systems based on relational systems: Middle-R, C-JDBC and MySQL Cluster and three systems based on In-Memory Data Grids: JBoss Data Grid, Oracle Coherence and Terracotta Ehcache. Thesis explore these systems based on their architecture, replication protocols, fault tolerance and various other functionalities. It also provides experimental analysis of these systems using state-of-the art benchmarks: TPC-C and TPC-W (for relational systems) and Yahoo! Cloud Serving Benchmark (In- Memory Data Grids). Thesis also discusses three Graph Databases, Neo4j, Titan and Sparksee based on their architecture and transactional capabilities and highlights the weaker transactional consistencies provided by these systems. It discusses an implementation of snapshot isolation in Neo4j graph database to provide stronger isolation guarantees for transactions.

Arquitectura de supercomputación en la nube para la resolución de problemas de optimización combinatoria

La tesis está focalizada en la resolución de problemas de optimización combinatoria, haciendo uso de las opciones tecnológicas actuales que ofrecen las tecnologías de la información y las comunicaciones, y la investigación operativa. Los problemas de optimización combinatoria se resuelven en general mediante programación lineal y metaheurísticas. La aplicación de las técnicas de resolución de los problemas de optimización combinatoria requiere de una elevada carga computacional, y los algoritmos deben diseñarse, por un lado pensando en la efectividad para encontrar buenas soluciones del problema, y por otro lado, pensando en un uso adecuado de los recursos informáticos disponibles. La programación lineal y las metaheurísticas son técnicas de resolución genéricas, que se pueden aplicar a diferentes problemas, partiendo de una base común que se particulariza para cada problema concreto. En el campo del desarrollo de software, los frameworks cumplen esa función de comenzar un proyecto con el trabajo general ya disponible, con la opción de cambiar o extender ese comportamiento base o genérico, para construir el sistema concreto, lo que permite reducir el tiempo de desarrollo, y amplía las posibilidades de éxito del proyecto. En esta tesis se han desarrollado dos frameworks de desarrollo. El framework ILP permite modelar y resolver problemas de programación lineal, de forma independiente al software de resolución de programación lineal que se utilice. El framework LME permite resolver problemas de optimización combinatoria mediante metaheurísticas. Tradicionalmente, las aplicaciones de resolución de problemas de optimización combinatoria son aplicaciones de escritorio que permiten gestionar toda la información de entrada del problema y resuelven el problema en local, con los recursos hardware disponibles. Recientemente ha aparecido un nuevo paradigma de despliegue y uso de aplicaciones que permite compartir recursos informáticos especializados por Internet. Esta nueva forma de uso de recursos informáticos es la computación en la nube, que presenta el modelo de software como servicio (SaaS). En esta tesis se ha construido una plataforma SaaS, para la resolución de problemas de optimización combinatoria, que se despliega sobre arquitecturas compuestas por procesadores multi-núcleo y tarjetas gráficas, y dispone de algoritmos de resolución basados en frameworks de programación lineal y metaheurísticas. Toda la infraestructura es independiente del problema de optimización combinatoria a resolver, y se han desarrollado tres problemas que están totalmente integrados en la plataforma SaaS. Estos problemas se han seleccionado por su importancia práctica. Uno de los problemas tratados en la tesis, es el problema de rutas de vehículos (VRP), que consiste en calcular las rutas de menor coste de una flota de vehículos, que reparte mercancías a todos los clientes. Se ha partido de la versión más clásica del problema y se han hecho estudios en dos direcciones. Por un lado se ha cuantificado el aumento en la velocidad de ejecución de la resolución del problema en tarjetas gráficas. Por otro lado, se ha estudiado el impacto en la velocidad de ejecución y en la calidad de soluciones, en la resolución por la metaheurística de colonias de hormigas (ACO), cuando se introduce la programación lineal para optimizar las rutas individuales de cada vehículo. Este problema se ha desarrollado con los frameworks ILP y LME, y está disponible en la plataforma SaaS. Otro de los problemas tratados en la tesis, es el problema de asignación de flotas (FAP), que consiste en crear las rutas de menor coste para la flota de vehículos de una empresa de transporte de viajeros. Se ha definido un nuevo modelo de problema, que engloba características de problemas presentados en la literatura, y añade nuevas características, lo que permite modelar los requerimientos de las empresas de transporte de viajeros actuales. Este nuevo modelo resuelve de forma integrada el problema de definir los horarios de los trayectos, el problema de asignación del tipo de vehículo, y el problema de crear las rotaciones de los vehículos. Se ha creado un modelo de programación lineal para el problema, y se ha resuelto por programación lineal y por colonias de hormigas (ACO). Este problema se ha desarrollado con los frameworks ILP y LME, y está disponible en la plataforma SaaS. El último problema tratado en la tesis es el problema de planificación táctica de personal (TWFP), que consiste en definir la configuración de una plantilla de trabajadores de menor coste, para cubrir una demanda de carga de trabajo variable. Se ha definido un modelo de problema muy flexible en la definición de contratos, que permite el uso del modelo en diversos sectores productivos. Se ha definido un modelo matemático de programación lineal para representar el problema. Se han definido una serie de casos de uso, que muestran la versatilidad del modelo de problema, y permiten simular el proceso de toma de decisiones de la configuración de una plantilla de trabajadores, cuantificando económicamente cada decisión que se toma. Este problema se ha desarrollado con el framework ILP, y está disponible en la plataforma SaaS. ABSTRACT The thesis is focused on solving combinatorial optimization problems, using current technology options offered by information technology and communications, and operations research. Combinatorial optimization problems are solved in general by linear programming and metaheuristics. The application of these techniques for solving combinatorial optimization problems requires a high computational load, and algorithms are designed, on the one hand thinking to find good solutions to the problem, and on the other hand, thinking about proper use of the available computing resources. Linear programming and metaheuristic are generic resolution techniques, which can be applied to different problems, beginning with a common base that is particularized for each specific problem. In the field of software development, frameworks fulfill this function that allows you to start a project with the overall work already available, with the option to change or extend the behavior or generic basis, to build the concrete system, thus reducing the time development, and expanding the possibilities of success of the project. In this thesis, two development frameworks have been designed and developed. The ILP framework allows to modeling and solving linear programming problems, regardless of the linear programming solver used. The LME framework is designed for solving combinatorial optimization problems using metaheuristics. Traditionally, applications for solving combinatorial optimization problems are desktop applications that allow the user to manage all the information input of the problem and solve the problem locally, using the available hardware resources. Recently, a new deployment paradigm has appeared, that lets to share hardware and software resources by the Internet. This new use of computer resources is cloud computing, which presents the model of software as a service (SaaS). In this thesis, a SaaS platform has been built for solving combinatorial optimization problems, which is deployed on architectures, composed of multi-core processors and graphics cards, and has algorithms based on metaheuristics and linear programming frameworks. The SaaS infrastructure is independent of the combinatorial optimization problem to solve, and three problems are fully integrated into the SaaS platform. These problems have been selected for their practical importance. One of the problems discussed in the thesis, is the vehicle routing problem (VRP), which goal is to calculate the least cost of a fleet of vehicles, which distributes goods to all customers. The VRP has been studied in two directions. On one hand, it has been quantified the increase in execution speed when the problem is solved on graphics cards. On the other hand, it has been studied the impact on execution speed and quality of solutions, when the problem is solved by ant colony optimization (ACO) metaheuristic, and linear programming is introduced to optimize the individual routes of each vehicle. This problem has been developed with the ILP and LME frameworks, and is available in the SaaS platform. Another problem addressed in the thesis, is the fleet assignment problem (FAP), which goal is to create lower cost routes for a fleet of a passenger transport company. It has been defined a new model of problem, which includes features of problems presented in the literature, and adds new features, allowing modeling the business requirements of today's transport companies. This new integrated model solves the problem of defining the flights timetable, the problem of assigning the type of vehicle, and the problem of creating aircraft rotations. The problem has been solved by linear programming and ACO. This problem has been developed with the ILP and LME frameworks, and is available in the SaaS platform. The last problem discussed in the thesis is the tactical planning staff problem (TWFP), which is to define the staff of lower cost, to cover a given work load. It has been defined a very rich problem model in the definition of contracts, allowing the use of the model in various productive sectors. It has been defined a linear programming mathematical model to represent the problem. Some use cases has been defined, to show the versatility of the model problem, and to simulate the decision making process of setting up a staff, economically quantifying every decision that is made. This problem has been developed with the ILP framework, and is available in the SaaS platform.

Design and implementation of an adaptive dashboard and visualization interface for the dynamic optimization and control of Data Centers

Over the last few years, the Data Center market has increased exponentially and this tendency continues today. As a direct consequence of this trend, the industry is pushing the development and implementation of different new technologies that would improve the energy consumption efficiency of data centers. An adaptive dashboard would allow the user to monitor the most important parameters of a data center in real time. For that reason, monitoring companies work with IoT big data filtering tools and cloud computing systems to handle the amounts of data obtained from the sensors placed in a data center.Analyzing the market trends in this field we can affirm that the study of predictive algorithms has become an essential area for competitive IT companies. Complex algorithms are used to forecast risk situations based on historical data and warn the user in case of danger. Considering that several different users will interact with this dashboard from IT experts or maintenance staff to accounting managers, it is vital to personalize it automatically. Following that line of though, the dashboard should only show relevant metrics to the user in different formats like overlapped maps or representative graphs among others. These maps will show all the information needed in a visual and easy-to-evaluate way. To sum up, this dashboard will allow the user to visualize and control a wide range of variables. Monitoring essential factors such as average temperature, gradients or hotspots as well as energy and power consumption and savings by rack or building would allow the client to understand how his equipment is behaving, helping him to optimize the energy consumption and efficiency of the racks. It also would help him to prevent possible damages in the equipment with predictive high-tech algorithms.

Informações de suporte ao escalonamento de workflows científicos para a execução em plataformas de computação em nuvem

A ciência tem feito uso frequente de recursos computacionais para execução de experimentos e processos científicos, que podem ser modelados como workflows que manipulam grandes volumes de dados e executam ações como seleção, análise e visualização desses dados segundo um procedimento determinado. Workflows científicos têm sido usados por cientistas de várias áreas, como astronomia e bioinformática, e tendem a ser computacionalmente intensivos e fortemente voltados à manipulação de grandes volumes de dados, o que requer o uso de plataformas de execução de alto desempenho como grades ou nuvens de computadores. Para execução dos workflows nesse tipo de plataforma é necessário o mapeamento dos recursos computacionais disponíveis para as atividades do workflow, processo conhecido como escalonamento. Plataformas de computação em nuvem têm se mostrado um alternativa viável para a execução de workflows científicos, mas o escalonamento nesse tipo de plataforma geralmente deve considerar restrições específicas como orçamento limitado ou o tipo de recurso computacional a ser utilizado na execução. Nesse contexto, informações como a duração estimada da execução ou limites de tempo e de custo (chamadas aqui de informações de suporte ao escalonamento) são importantes para garantir que o escalonamento seja eficiente e a execução ocorra de forma a atingir os resultados esperados. Este trabalho identifica as informações de suporte que podem ser adicionadas aos modelos de workflows científicos para amparar o escalonamento e a execução eficiente em plataformas de computação em nuvem. É proposta uma classificação dessas informações, e seu uso nos principais Sistemas Gerenciadores de Workflows Científicos (SGWC) é analisado. Para avaliar o impacto do uso das informações no escalonamento foram realizados experimentos utilizando modelos de workflows científicos com diferentes informações de suporte, escalonados com algoritmos que foram adaptados para considerar as informações inseridas. Nos experimentos realizados, observou-se uma redução no custo financeiro de execução do workflow em nuvem de até 59% e redução no makespan chegando a 8,6% se comparados à execução dos mesmos workflows sendo escalonados sem nenhuma informação de suporte disponível.

Orquestração de migração massiva de máquinas virtuais baseada em análise cíclica para ambientes de computação na nuvem.

Um das principais características da tecnologia de virtualização é a Live Migration, que permite que máquinas virtuais sejam movimentadas entre máquinas físicas sem a interrupção da execução. Esta característica habilita a implementação de políticas mais sofisticadas dentro de um ambiente de computação na nuvem, como a otimização de uso de energia elétrica e recursos computacionais. Entretanto, a Live Migration pode impor severa degradação de desempenho nas aplicações das máquinas virtuais e causar diversos impactos na infraestrutura dos provedores de serviço, como congestionamento de rede e máquinas virtuais co-existentes nas máquinas físicas. Diferente de diversos estudos, este estudo considera a carga de trabalho da máquina virtual um importante fator e argumenta que escolhendo o momento adequado para a migração da máquina virtual pode-se reduzir as penalidades impostas pela Live Migration. Este trabalho introduz a Application-aware Live Migration (ALMA), que intercepta as submissões de Live Migration e, baseado na carga de trabalho da aplicação, adia a migração para um momento mais favorável. Os experimentos conduzidos neste trabalho mostraram que a arquitetura reduziu em até 74% o tempo das migrações para os experimentos com benchmarks e em até 67% os experimentos com carga de trabalho real. A transferência de dados causada pela Live Migration foi reduzida em até 62%. Além disso, o presente introduz um modelo que faz a predição do custo da Live Migration para a carga de trabalho e também um algoritmo de migração que não é sensível à utilização de memória da máquina virtual.

Hacia la seguridad criptográfica en sistemas DaaS

El nuevo paradigma de computación en la nube posibilita la prestación de servicios por terceros. Entre ellos, se encuentra el de las bases de datos como servicio (DaaS) que permite externalizar la gestión y alojamiento del sistema de gestión de base de datos. Si bien esto puede resultar muy beneficioso (reducción de costes, gestión simplificada, etc.), plantea algunas dificultades respecto a la funcionalidad, el rendimiento y, en especial, la seguridad de dichos servicios. En este trabajo se describen algunas de las propuestas de seguridad en sistemas DaaS existentes y se realiza un análisis de sus características principales, introduciendo un nuevo enfoque basado en tecnologías no exclusivamente relacionales (NoSQL) que presenta ventajas respecto a la escalabilidad y el rendimiento.

Flexible processing architecture for maintaining QoS in embedded systems applications

Comunicación presentada en las V Jornadas de Computación Empotrada, Valladolid, 17-19 Septiembre 2014

Tecnologie per l’analisi in tempo reale di Big Data: prestazioni a confronto

Lo scopo di questo l'elaborato è l'analisi,lo studio e il confronto delle tecnologie per l'analisi in tempo reale di Big Data: Apache Spark Streaming, Apache Storm e Apache Flink. Per eseguire un adeguato confronto si è deciso di realizzare un sistema di rilevamento e riconoscimento facciale all’interno di un video, in maniera da poter parallelizzare le elaborazioni necessarie sfruttando le potenzialità di ogni architettura. Dopo aver realizzato dei prototipi realistici, uno per ogni architettura, si è passati alla fase di testing per misurarne le prestazioni. Attraverso l’impiego di cluster appositamente realizzati in ambiente locale e cloud, sono state misurare le caratteristiche che rappresentavano, meglio di altre, le differenze tra le architetture, cercando di dimostrarne quantitativamente l’efficacia degli algoritmi utilizzati e l’efficienza delle stesse. Si è scelto quindi il massimo input rate sostenibile e la latenza misurate al variare del numero di nodi. In questo modo era possibile osservare la scalabilità di architettura, per analizzarne l’andamento e verificare fino a che limite si potesse giungere per mantenere un compromesso accettabile tra il numero di nodi e l’input rate sostenibile. Gli esperimenti effettuati hanno mostrato che, all’aumentare del numero di worker le prestazioni del sistema migliorano, rendendo i sistemi studiati adatti all’utilizzo su larga scala. Inoltre sono state rilevate sostanziali differenze tra i vari framework, riportando pro e contro di ognuno, cercando di evidenziarne i più idonei al caso di studio.

L'implementazione delle piattaforme di E-Procurement: il caso FAAC Group

Questa tesi concerne quella che è una generalizzata tendenza verso la trasformazione digitale dei processi di business. Questa evoluzione, che implica l’utilizzo delle moderne tecnologie informatiche tra cui il Cloud Computing, le Big Data Analytics e gli strumenti Mobile, non è priva di insidie che vanno di volta in volta individuate ed affrontate opportunamente. In particolare si farà riferimento ad un caso aziendale, quello della nota azienda bolognese FAAC spa, ed alla funzione acquisti. Nell'ambito degli approvvigionamenti l'azienda sente la necessità di ristrutturare e digitalizzare il processo di richiesta di offerta (RdO) ai propri fornitori, al fine di consentire alla funzione di acquisti di concentrarsi sull'implementazione della strategia aziendale più che sull'operatività quotidiana. Si procede quindi in questo elaborato all'implementazione di un progetto di implementazione di una piattaforma specifica di e-procurement per la gestione delle RdO. Preliminarmente vengono analizzati alcuni esempi di project management presenti in letteratura e quindi viene definito un modello per la gestione del progetto specifico. Lo svolgimento comprende quindi: una fase di definizione degli obiettivi di continuità dell'azienda, un'analisi As-Is dei processi, la definizione degli obiettivi specifici di progetto e dei KPI di valutazione delle performance, la progettazione della piattaforma software ed infine alcune valutazioni relative ai rischi ed alle alternative dell'implementazione.

Edge caching with mobility prediction in virtualized LTE mobile networks

Abstract Mobile Edge Computing enables the deployment of services, applications, content storage and processing in close proximity to mobile end users. This highly distributed computing environment can be used to provide ultra-low latency, precise positional awareness and agile applications, which could significantly improve user experience. In order to achieve this, it is necessary to consider next-generation paradigms such as Information-Centric Networking and Cloud Computing, integrated with the upcoming 5th Generation networking access. A cohesive end-to-end architecture is proposed, fully exploiting Information-Centric Networking together with the Mobile Follow-Me Cloud approach, for enhancing the migration of content-caches located at the edge of cloudified mobile networks. The chosen content-relocation algorithm attains content-availability improvements of up to 500 when a mobile user performs a request and compared against other existing solutions. The performed evaluation considers a realistic core-network, with functional and non-functional measurements, including the deployment of the entire system, computation and allocation/migration of resources. The achieved results reveal that the proposed architecture is beneficial not only from the users’ perspective but also from the providers point-of-view, which may be able to optimize their resources and reach significant bandwidth savings.

Expert survey on priorities for the information society in the Caribbean

An on-line survey of experts was conducted to solicit their views on policy priorities in the area of information and communication technologies (ICT) in the Caribbean. The experts considered the goal to “promote teacher training in the use of ICTs in the classroom” to be the highest priority, followed by goals to “reduce the cost of broadband services” and “promote the use of ICT in emergency and disaster prevention, preparedness and response.” Goals in the areas of cybercrime, e-commerce, egovernment, universal service funds, consumer protection, and on-line privacy rounded out the top 10. Some of the lowest ranked goals were those related to coordinating the management of infrastructure changes. These included the switchover for digital terrestrial television (DTT) and digital FM radio, cloud computing for government ICT, the introduction of satellite-based internet services, and the installation of content distribution networks (CDNs). Initiatives aimed at using ICT to promote specific industries, or specific means of promoting the digital economy, tended toward the centre of the rankings. Thus, a general pattern emerged which elevated the importance of focusing on how ICT is integrated into the broader society, with economic issues a lower priority, and concerns about coordination on infrastructure issues lower still.

Dynamic Models under Uncertainty for Large-Scale Enterprise Systems

Thesis (Ph.D.)--University of Washington, 2016-06

New capabilities:can IT service providers leverage crowdsourcing?

Technological advancements enable new sourcing models in software development such as cloud computing, software-as-a-service, and crowdsourcing. While the first two are perceived as a re-emergence of older models (e.g., ASP), crowdsourcing is a new model that creates an opportunity for a global workforce to compete with established service providers. Organizations engaging in crowdsourcing need to develop the capabilities to successfully utilize this sourcing model in delivering services to their clients. To explore these capabilities we collected qualitative data from focus groups with crowdsourcing leaders at a large technology organization. New capabilities we identified stem from the need of the traditional service provider to assume a "client" role in the crowdsourcing context, while still acting as a "vendor" in providing services to the end client. This paper expands the research on vendor capabilities and IS outsourcing as well as offers important insights to organizations that are experimenting with, or considering, crowdsourcing.

Digital fiber nonlinearity compensation:toward 1-Tb/s transport

The world is connected by a core network of long-haul optical communication systems that link countries and continents, enabling long-distance phone calls, data-center communications, and the Internet. The demands on information rates have been constantly driven up by applications such as online gaming, high-definition video, and cloud computing. All over the world, end-user connection speeds are being increased by replacing conventional digital subscriber line (DSL) and asymmetric DSL (ADSL) with fiber to the home. Clearly, the capacity of the core network must also increase proportionally. © 1991-2012 IEEE.

A utility model for volunteered service composition

Volunteered Service Composition (VSC) refers to the process of composing volunteered services and resources. These services are typically published to a pool of voluntary resources. The composition aims at satisfying some objectives (e.g. Utilizing storage and eliminating waste, sharing space and optimizing for energy, reducing computational cost etc.). In cases when a single volunteered service does not satisfy a request, VSC will be required. In this paper, we contribute to three approaches for composing volunteered services: these are exhaustive, naïve and utility-based search approach to VSC. The proposed new utility-based approach, for instance, is based on measuring the utility that each volunteered service can provide to each request and systematically selects the one with the highest utility. We found that the utility-based approach tend to be more effective and efficient when selecting services, while minimizing resource waste when compared to the other two approaches.