Cloud computing enables the delivery of remote computing, software and storage services through web browsers following pay-as-you-go model. In addition to successful commercial applications, many research efforts including DOE Magellan Cloud project focus on discovering the opportunities and challenges arising from the computing and data-intensive scientific applications that are not well addressed by the current supercomputers, Linux clusters and Grid technologies. The elastic resource provision, noninterfering resource sharing and flexible customized configuration provided by the Cloud infrastructure has shed light on efficient execution of many scientific applications modeled as Directed Acyclic Graph (DAG) structured workflows to enforce the intricate dependency among a large number of different processing tasks. Meanwhile, the Cloud environment poses various challenges. Cloud providers and Cloud users pursue different goals. Providers aim to maximize profit by achieving higher resource utilization and users want to minimize expenses while meeting their performance requirements. Moreover, due to the expanding Cloud services and emerging newer technologies, the ever-increasing heterogeneity of the Cloud environment complicates the challenges for both parties. In this thesis, we address the workflow scheduling problem from different applications and various objectives. For batch applications, due to the increasing deployment of many data centers and computer servers around the globe escalated by the higher electricity price, the energy cost on running the computing, communication and cooling together with the amount of CO2 emissions have skyrocketed. In order to maintain sustainable Cloud computing facing with ever-increasing problem complexity and big data size in the next decades, we design and develop energy-aware scientific workflow scheduling algorithm to minimize energy consumption and CO2 emission while still satisfying certain Quality of Service (QoS) such as response time specified in Service Level Agreement (SLA). Furthermore, the underlying Cloud hardware/Virtual Machine (VM) resource availability is time-dependent because of the dual operation modes namely on-demand and reservation instances at various Cloud data centers. We also apply techniques such as Dynamic Voltage and Frequency Scaling (DVFS) and DNS scheme to further reduce energy consumption within acceptable performance bounds. Our multiple-step resource provision and allocation algorithm achieves the response time requirement in the step of forward task scheduling and minimizes the VM overhead for reduced energy consumption and higher resource utilization rate in the backward task scheduling step. We also evaluate the candidacy of multiple data centers from the energy and performance efficiency perspectives as different data centers have various energy and cost related parameters. For streaming applications, we formulate scheduling problems with two different objectives, namely one is to maximize the throughput under a budget constraint while another is to minimize execution cost under a minimum throughput constraint. Two different algorithms named as Budget constrained RATE (B-RATE) and Budget constrained SWAP (B-SWAP) are designed under the first objective; Another two algorithms, namely Throughput constrained RATE (TP-RATE) and Throughput constrained SWAP (TP-SWAP) are developed under the second objective.

This research investigated the potential benefits, risks and challenges, innovation properties and viability of cloud computing for records management on an Australian organisation within the mining software development sector. This research involved the use of a case study results analysis as well as a literature analysis. The literature analysis identified the ten potential benefits of cloud computing, as well as the ten risks and challenges associated with cloud computing. It further identified aspects, which needed to be addressed when adopting cloud computing in order to promote innovation within an organisation. The case study analysis was compared against a literature review of ten potential benefits of cloud computing, as well as the ten risks and challenges associated with cloud computing. This was done in order to determine cloud computing’s viability for records management for Company X (The company in the case study). Cloud computing was found to be viable for Company X. However, there were certain aspects, which need to be discussed and clarified with the cloud service provider beforehand in order to mitigate possible risks and compliance issues. It is also recommended that a cloud service provider who complies with international standards, such as ISO 15489, be selected. The viability of cloud computing for organisations similar to Company X (mining software development) followed a related path. These organisations need to ensure that the service provider is compliant with laws in their local jurisdiction, such as Electronic Transactions Act 1999 (Australia, 2011:14-15), as well as laws where their data (in the cloud) may be hosted. The benefits, risks and challenges of records management and cloud computing are applicable to these similar organisations. However, mitigation of these risks needs to be discussed with a cloud service provider beforehand. From an innovation perspective, cloud computing is able to promote innovation within an organisation, if certain antecedents are dealt with. Furthermore, if cloud computing is successfully adopted then it should promote innovation within organisations. / Information Science / M. Inf.

The modern world has opened the gates to a lot of advancements in cloud computing, particularly in the field of Cloud Gaming. The most recent development made in this area is the open-source cloud gaming system called GamingAnywhere. The relationship between the CPU and GPU is what is the main object of our concentration in this thesis paper. The Graphical Processing Unit (GPU) performance plays a vital role in analyzing the playing experience and enhancement of GamingAnywhere. In this paper, the virtualization of the GPU has been concentrated on and is suggested that the acceleration of this unit using NVIDIA CUDA, is the key for better performance while using GamingAnywhere. After vast research, the technique employed for NVIDIA CUDA has been chosen as gVirtuS. There is an experimental study conducted to evaluate the feasibility and performance of GPU solutions by VMware in cloud gaming scenarios given by GamingAnywhere. Performance is measured in terms of bitrate, packet loss, jitter and frame rate. Different resolutions of the game are considered in our empirical research and our results show that the frame rate and bitrate have increased with different resolutions, and the usage of NVIDIA CUDA enhanced GPU.

FARIAS, Victor Aguiar Evangelista de. Uma Abordagem para a Modelagem de Desempenho e de Elasticidade para Bancos de Dados em Nuvem. 2016. 73 f. Dissertação (mestrado em computação)- Universidade Federal do Ceará, Fortaleza-CE, 2016. / Submitted by Elineudson Ribeiro (elineudsonr@gmail.com) on 2016-03-31T18:48:05Z No. of bitstreams: 1 2016_dis_vaefarias.pdf: 2901674 bytes, checksum: 2defc02493d2e15c69317aca46126bb3 (MD5) / Approved for entry into archive by Rocilda Sales (rocilda@ufc.br) on 2016-04-25T12:33:53Z (GMT) No. of bitstreams: 1 2016_dis_vaefarias.pdf: 2901674 bytes, checksum: 2defc02493d2e15c69317aca46126bb3 (MD5) / Made available in DSpace on 2016-04-25T12:33:53Z (GMT). No. of bitstreams: 1 2016_dis_vaefarias.pdf: 2901674 bytes, checksum: 2defc02493d2e15c69317aca46126bb3 (MD5) Previous issue date: 2016 / Cloud computing is a successful, emerging paradigm that supports on-demand services. With the exponential growth of data generated by present applications, NoSQL databases which are inherently distributed systems have been used to manage data in the cloud. In this scenario, it is fundamental for cloud providers to guarantee Quality of Service (QoS) by satisfying tho Service Level Agreement (SLA) contract while reducing the operational costs related to both overprovisioning and underprovisioning. Thus QoS mechanisms can greatly benefit from a predictive model that estimates SLA-based performance metrics for a given cluster and workload configuration. Therewith, elastic provisioning strategies can benefit from these predictive models to provide a reliable mechanism to add and remove resources reliably. In this work, we present a generic performance modeling for NoSQL databases in terms of SLA-based metrics capable of capturing non-linear effects caused by concurrency and distribution aspects. Moreover we present a elastic provisioning mechanism based on performance models. Results of experimental evaluation confirm that our performance modeling can accurately estimate the performance under a wide range of workload configurations and also that our elastic provisioning approach can ensure QoS while using resources efficiently. / A computação em nuvem é um paradigma de computação emergente e bem sucedido que oferece serviços por demanda. Com o crescimento exponencial da quantidade de dados utilizados pelas aplicações atuais, os bancos de dados NoSQL, que são sistemas inerentemente distribuídos, têm sido usados para gerenciar dados na Nuvem. Nesse cenário, é fundamental que os provedores de serviços em nuvem garantam a Qualidade de Serviço (QoS) por meio do cumprimento do contrato Service Level Agreement (SLA) enquanto reduz os custos operacionais relacionados a overprovisioning e underprovisioning. Mecanismos de QoS podem se beneficiar fortemente de modelos de desempenho preditivos que estimam o desempenho para uma dada configuração do sistema NoSQL e da carga de trabalho. Com isso, estratégias de elasticidade podem aproveitar esses modelos preditivos para fornecer meios de adicionar e remover recursos computacionais de forma mais confiável. Este trabalho apresenta uma abordagem para modelagem de desempenho genérica para banco de dados NoSQL em termos de métricas de desempenho baseadas no SLA capaz de capturar o efeitos não-lineares causados pelo aspectos de concorrência e distribuição. Adicionalmente, é apresentado um mecanismo de elasticidade para adicionar e remover nós sistema NoSQL baseado em modelos de desempenho. Resultados de avaliação experimental confirmam que a modelagem de desempenho estima as métricas de forma acurada para vários cenários de carga de trabalho e configurações do sistema. Por fim, a nossa estratégia de elasticidade é capaz de garantir a QoS enquanto utiliza os recursos de forma eficiente.

The modern world has opened the gates to a lot of advancements in cloud computing, particularly in the field of Cloud Gaming. The most recent development made in this area is the open-source cloud gaming system called GamingAnywhere. The relationship between the CPU and GPU is what is the main object of our concentration in this thesis paper. The Graphical Processing Unit (GPU) performance plays a vital role in analyzing the playing experience and enhancement of GamingAnywhere. In this paper, the virtualization of the GPU has been concentrated on and is suggested that the acceleration of this unit using NVIDIA CUDA, is the key for better performance while using GamingAnywhere. After vast research, the technique employed for NVIDIA CUDA has been chosen as gVirtuS. There is an experimental study conducted to evaluate the feasibility and performance of GPU solutions by VMware in cloud gaming scenarios given by GamingAnywhere. Performance is measured in terms of bitrate, packet loss, jitter and frame rate. Different resolutions of the game are considered in our empirical research and our results show that the frame rate and bitrate have increased with different resolutions, and the usage of NVIDIA CUDA enhanced GPU.

The modern world has opened the gates to a lot of advancements in cloud computing, particularly in the field of Cloud Gaming. The most recent development made in this area is the open-source cloud gaming system called GamingAnywhere. The relationship between the CPU and GPU is what is the main object of our concentration in this thesis paper. The Graphical Processing Unit (GPU) performance plays a vital role in analyzing the playing experience and enhancement of GamingAnywhere. In this paper, the virtualization of the GPU has been concentrated on and is suggested that the acceleration of this unit using NVIDIA CUDA, is the key for better performance while using GamingAnywhere. After vast research, the technique employed for NVIDIA CUDA has been chosen as gVirtuS. There is an experimental study conducted to evaluate the feasibility and performance of GPU solutions by VMware in cloud gaming scenarios given by GamingAnywhere. Performance is measured in terms of bitrate, packet loss, jitter and frame rate. Different resolutions of the game are considered in our empirical research and our results show that the frame rate and bitrate have increased with different resolutions, and the usage of NVIDIA CUDA enhanced GPU.

Les systèmes distribués modernes à large-échelle évoluent dans des contextes variables soumis à de nombreux aléas auxquels ils doivent s'adapter dynamiquement. Dans ce cadre, l'informatique autonome se propose de réduire les interventions humaines lentes et coûteuses, en leur préférant l'auto-gestion. Elle repose avant tout sur une description adéquate de ses composants, de leurs interactions et des différents aspects ou topologies qu'il peut adopter. Diverses approches de modélisation ont étés proposées dans la littérature, se concentrant en général sur certains du système dynamique et ne permettent ainsi pas de répondre à chacune des problématiques inhérentes à l'auto-gestion. Cette thèse traite de la modélisation basée graphes des systèmes dynamiques et de son adéquation pour la mise en œuvre des quatre propriétés fondamentales de l'informatique. Elle propose quatre principales contributions théoriques et appliquées. La première est une méthodologie pour la construction et la caractérisation générative de transformations correctes par construction dont l'application préserve nécessairement la correction du système. La seconde contribution consiste en une extension des systèmes de réécriture de graphe permettant de représenter, mettre à jour, évaluer et paramétrer les caractéristiques d'un système aisément et efficacement. Une étude expérimentale extensive révèle un net gain d'efficacité vis à vis de méthodes classiques. Les deux dernières contributions s'articulent autour de l'élaboration de deux modules de gestions visant : (1) des requêtes de traitement d'événements complexes et (2) tout système Machine-à-Machine se conformant au standard ETSI M2M. / Modern, large-scale systems are deployed in changing environments. They must dynamically adapt to context changes. In this scope, autonomic computing aims at reducing slow and costly human interventions, by building self-managed systems. Self-adaptability of a system is primarily based on a suitable description of its components, their interactions and the various states it can adopt. Various modeling approaches have been elaborated. They usually focus on some aspects or properties of dynamic systems and do not tackle each of self-management's requirements. This manuscript deals with graph-based representations of dynamic systems and their suitability for the implementation of autonomic computing's four fundamental properties : self-optimization, self-protection, self-healing and self-configuring. This thesis offers four principal theoretical and applied contributions. The first one is a methodology for the construction and generative characterization of transformations correct by construction whose application necessarily preserves a system's correctness. The second one consists in an extension of graph rewriting systems allowing to easily and efficiently represent, update, evaluate and configure a system's characteristics. An experimental study reveals a significant efficiency gain with regard to classical methods. The two lasts contribution are articulated around the design of two autonomic managers driving: (1) complex events processing requests and (2) any Machine-to-Machine system complying to the ETSI M2M2 standard.

Computação em nuvem é uma solução adequada para profissionais, empresas, centros de pesquisa e instituições que necessitam de acesso a recursos computacionais sob demanda. Atualmente, nuvens computacionais confiam no gerenciamento de sua estrutura para fornecer recursos computacionais com qualidade de serviço adequada as expectativas de seus clientes, tal qualidade de serviço é estabelecida através de acordos de nível de serviço. Nesse contexto, o monitoramento é uma função crítica de gerenciamento para se prover tal qualidade de serviço. Requisitos de monitoramento em nuvens computacionais são propriedades que um sistema de monitoramento de nuvem precisa reunir para executar suas funções de modo adequado e atualmente existem diversos requisitos definidos pela literatura, tais como: oportunidade, elasticidade e escalabilidade. Entretanto, tais requisitos geralmente possuem influência mútua entre eles, que pode ser positiva ou negativa, e isso impossibilita o desenvolvimento de soluções de monitoramento completas. Dado o cenario descrito acima, essa tese tem como objetivo investigar a influência mútua entre escalabilidade e oportunidade. Especificamente, essa tese propõe um modelo matemático para estimar a influência mútua entre tais requisitos de monitoramento. A metodologia utilizada por essa tese para construir tal modelo matemático baseia-se em parâmetros de monitoramento tais como: topologia de monitoramento, quantidade de dados de monitoramento e frequencia de amostragem. Além destes, a largura de banda de rede e o tempo de resposta também são importantes métricas do modelo matemático. A avaliação dos resultados obtidos foi realizada através da comparação entre os resultados do modelo matemático e de uma simulação. As maiores contribuições dessa tese são divididas em dois eixos, estes são denominados: Básico e Chave. As contribuições do eixo básico são: (i) a discussão a respeito da estrutura de monitoramento de nuvem e introdução do conceito de foco de monitoramento (ii) o exame do conceito de requisito de monitoramento e a proposição do conceito de abilidade de monitoramento (iii) a análise dos desafios e tendências a respeito de monitoramento de nuvens computacionais. As contribuições do eixo chave são: (i) a discussão a respeito de oportunidade e escalabilidade incluindo métodos para lidar com a mútua influência entre tais requisitos e a relação desses requisitos com parâmetros de monitoramento (ii) a identificação dos parâmetros de monitoramento que são essenciais na relação entre oportunidade e escalabilidade (iii) a proposição de um modelo matemático baseado em parâmetros de monitoramento que visa estimar a relação mútua entre oportunidade e escalabilidade. / Cloud computing is a suitable solution for professionals, companies, research centres, and institutions that need to have access to computational resources on demand. Nowadays, clouds have to rely on proper management of its structure to provide such computational resources with adequate quality of service, which is established by Service Level Agreements (SLAs), to customers. In this context, cloud monitoring is a critical management function to achieve it. Cloud monitoring requirements are properties that a cloud monitoring system need to meet to perform its functions properly, and currently there are several of them such as timeliness, elasticity and scalability. However, such requirements usually have mutual influence, which is either positive or negative, among themselves, and it has prevented the development of complete cloud monitoring solutions. From the above, this thesis investigates the mutual influence between timeliness and scalability. This thesis proposes a mathematical model to estimate such mutual influence to enhance cloud monitoring systems. The methodology used in this thesis is based on monitoring parameters such as monitoring topologies, the amount of monitoring data, and frequency sampling. Besides, it considers as important metrics network bandwidth and response time. Finally, the evaluation is based on a comparison of the mathematical model results and outcomes obtained via simulation. The main contributions of this thesis are divided into two axes, namely, basic and key. Basic contributions of this thesis are: (i) it discusses the cloud monitoring structure and introduced the concept of cloud monitoring focus (ii) it examines the concept of cloud monitoring requirement and proposed to divide them into two groups defined as cloud monitoring requirements and cloud monitoring abilities (iii) it analysed challenges and trends in cloud monitoring pointing research gaps that include the mutual influence between cloud monitoring requirements which is core to the key contributions. The key contributions of this thesis are: (i) it presents a discussion of timeliness and scalability that include: the methods currently used to cope with the mutual influence between them, and the relation between such requirements and monitoring parameters (ii) it identifies the monitoring parameters that are essential in the relation between timeliness and scalability (iii) it proposes a mathematical model based on monitoring parameters to estimate the mutual influence between timeliness and scalability.

Ingeniero Civil en Computación / Reservo.cl es una aplicación Web creada por la empresa SC3 SpA para reservar horas en consultorios médicos. Aunque esta aplicación está en producción y es exitosa, tiene muchas limitaciones para poder aumentar la tasa de atención de usuarios, lo cual limita su expansión en el mercado chileno, y eventualmente en el Latinoamericano. Por lo tanto, el objetivo de este trabajo de memoria es identificar los problemas que limitan su expansión, proponer soluciones para abordarlos, e implementar algunas de ellas. Particularmente se realizó: (1) un análisis de la infraestructura de soporte actual y de los puntos donde sería necesario intervenir el software, (2) un listado detallado de necesidades de mejoras a la aplicación y a la empresa, y (3) un diseño de la solución a cada una de las necesidades identificadas. Algunos de los principales problemas identificados en el análisis fueron los siguientes: hay funcionalidades ocupan muchos recursos, los datos se encuentran almacenados en una única base de datos, la aplicación no tiene capacidad de escalar, no se puede garantizar un cierto nivel de uptime, y se desconoce el nivel de vulnerabilidad de la aplicación ante ataques externos. La gran mayoría de estos problemas son el resultado de la arquitectura monolítica que tiene actualmente la aplicación. Por lo tanto, para ayudar a paliar esta situación se definió una arquitectura basada en microservicios, que desacopla los componentes de software, dándole mayor flexibilidad, capacidad de evolución y de atención de transacciones a la solución. Los servicios de la nueva solución son implementados con servicios de Amazon AWS, lo cual permite obtener mayor escalabilidad y alta disponibilidad. Respecto a seguridad de la plataforma, la solución propuesta cuenta un nivel de seguridad bueno, pues está basado en componentes ya probados, los cuales pueden además ser configurados para implementar posibles mejoras. Debido al alcance del problema abordado y al limitado tiempo disponible para realizar el trabajo de memoria, algunas de las soluciones propuestas quedaron implementadas, otras en desarrollo y otras están sólo diseñadas. Sin embargo, todas ellas fueron evaluadas por expertos del área de software para asegurarse que son pertinentes para abordar los problemas planteados. / 05/04/2021

Nos últimos anos, Cloud Computing tem se tornado uma tecnologia importante que possibilitou executar aplicações sem a necessidade de implementar uma infraestrutura física com a vantagem de reduzir os custos ao usuário cobrando somente pelos recursos computacionais utilizados pela aplicação. O desafio com a implementação de aplicações distribuídas em ambientes de Cloud Computing é o planejamento da infraestrutura de máquinas virtuais visando otimizar o tempo de execução e o custo da implementação. Assim mesmo, nos últimos anos temos visto como a quantidade de dados produzida pelas aplicações cresceu mais que nunca. Estes dados contêm informação valiosa que deve ser obtida utilizando ferramentas como MapReduce. MapReduce é um importante framework para análise de grandes quantidades de dados desde que foi proposto pela Google, e disponibilizado Open Source pela Apache com a sua implementação Hadoop. O objetivo deste trabalho é apresentar que é possível predizer o tempo de execução de uma aplicação distribuída, a saber, uma aplicação MapReduce, na infraestrutura de Cloud Computing, utilizando um modelo matemático baseado em especificações teóricas. Após medir o tempo levado na execução da aplicação e variando os parámetros indicados no modelo matemático, e, após utilizar uma técnica de regressão linear, o objetivo é atingido encontrando um modelo do tempo de execução que foi posteriormente aplicado para predizer o tempo de execução de aplicações MapReduce com resultados satisfatórios. Os experimentos foram realizados em diferentes configurações: a saber, executando diferentes aplicações MapReduce em clusters privados e públicos, bem como em infraestruturas de Cloud comercial, e variando o número de nós que compõem o cluster, e o tamanho do workload dado à aplicação. Os experimentos mostraram uma clara relação com o modelo teórico, indicando que o modelo é, de fato, capaz de predizer o tempo de execução de aplicações MapReduce. O modelo desenvolvido é genérico, o que quer dizer que utiliza abstrações teóricas para a capacidade computacional do ambiente e o custo computacional da aplicação MapReduce. Motiva-se a desenvolver trabalhos futuros para estender esta abordagem para atingir outro tipo de aplicações distribuídas, e também incluir o modelo matemático deste trabalho dentro de serviços na núvem que ofereçam plataformas MapReduce, a fim de ajudar os usuários a planejar suas implementações. / In the last years, Cloud Computing has become a key technology that made possible running applications without needing to deploy a physical infrastructure with the advantage of lowering costs to the user by charging only for the computational resources used by the application. The challenge with deploying distributed applications in Cloud Computing environments is that the virtual machine infrastructure should be planned in a way that is time and cost-effective. Also, in the last years we have seen how the amount of data produced by applications has grown bigger than ever. This data contains valuable information that has to be extracted using tools like MapReduce. MapReduce is an important framework to analyze large amounts of data since it was proposed by Google, and made open source by Apache with its Hadoop implementation. The goal of this work is to show that the execution time of a distributed application, namely, a MapReduce application, in a Cloud computing environment, can be predicted using a mathematical model based on theoretical specifications. This prediction is made to help the users of the Cloud Computing environment to plan their deployments, i.e., quantify the number of virtual machines and its characteristics in order to have a lesser cost and/or time. After measuring the application execution time and varying parameters stated in the mathematical model, and after that, using a linear regression technique, the goal is achieved finding a model of the execution time which was then applied to predict the execution time of MapReduce applications with satisfying results. The experiments were conducted in several configurations: namely, private and public clusters, as well as commercial cloud infrastructures, running different MapReduce applications, and varying the number of nodes composing the cluster, as well as the amount of workload given to the application. Experiments showed a clear relation with the theoretical model, revealing that the model is in fact able to predict the execution time of MapReduce applications. The developed model is generic, meaning that it uses theoretical abstractions for the computing capacity of the environment and the computing cost of the MapReduce application. Further work in extending this approach to fit other types of distributed applications is encouraged, as well as including this mathematical model into Cloud services offering MapReduce platforms, in order to aid users plan their deployments.