Xen Worlds: Creating a virtual laboratory environment for use in education

The Xen Worlds project uses the Xen hypervisor to create a virtual lab environment, providing students with personal networks of fully functional virtual machines (VMs) called a Xen World. The Xen Worlds environment can be provided using minimal hardware, and uses open source software, making it a low-cost option for education. The current hardware, consisting of five modest servers is capable of providing 470 VMs. Since each Xen World can be isolated from each other, and from the Internet, students can be provided root access to their VMs without the security and privacy issues that would be present in a normal shared lab. In addition, to support off-campus students, Xen Worlds has several features that ensure the system is equally accessible and easy to use, even if the student has limited access to computing or network resources. To rate the usability and effectiveness of the Xen Worlds environment, student feedback was collected through the use of surveys. The results indicate students feel the environment is an enjoyable and effective teaching method, with comments indicating a desire for a greater number of assignments to be provided

Creating a Worldwide Network For the Global Environment for Network Innovations (GENI) and Related Experimental Environments

Many important societal activities are global in scope, and as these activities continually expand world-wide, they are increasingly based on a foundation of advanced communication services and underlying innovative network architecture, technology, and core infrastructure. To continue progress in these areas, research activities cannot be limited to campus labs and small local testbeds or even to national testbeds. Researchers must be able to explore concepts at scale—to conduct experiments on world-wide testbeds that approximate the attributes of the real world. Today, it is possible to take advantage of several macro information technology trends, especially virtualization and capabilities for programming technology resources at a highly granulated level, to design, implement and operate network research environments at a global scale. GENI is developing such an environment, as are research communities in a number of other countries. Recently, these communities have not only been investigating techniques for federating these research environments across multiple domains, but they have also been demonstration prototypes of such federations. This chapter provides an overview of key topics and experimental activities related to GENI international networking and to related projects throughout the world

Mitigating Interference During Virtual Machine Live Migration through Storage Offloading

Today\u27s cloud landscape has evolved computing infrastructure into a dynamic, high utilization, service-oriented paradigm. This shift has enabled the commoditization of large-scale storage and distributed computation, allowing engineers to tackle previously untenable problems without large upfront investment. A key enabler of flexibility in the cloud is the ability to transfer running virtual machines across subnets or even datacenters using live migration. However, live migration can be a costly process, one that has the potential to interfere with other applications not involved with the migration. This work investigates storage interference through experimentation with real-world systems and well-established benchmarks. In order to address migration interference in general, a buffering technique is presented that offloads the migration\u27s read, eliminating interference in the majority of scenarios

Improved self-management of datacenter systems applying machine learning

Autonomic Computing is a Computer Science and Technologies research area, originated during mid 2000's. It focuses on optimization and improvement of complex distributed computing systems through self-control and self-management. As distributed computing systems grow in complexity, like multi-datacenter systems in cloud computing, the system operators and architects need more help to understand, design and optimize manually these systems, even more when these systems are distributed along the world and belong to different entities and authorities. Self-management lets these distributed computing systems improve their resource and energy management, a very important issue when resources have a cost, by obtaining, running or maintaining them. Here we propose to improve Autonomic Computing techniques for resource management by applying modeling and prediction methods from Machine Learning and Artificial Intelligence. Machine Learning methods can find accurate models from system behaviors and often intelligible explanations to them, also predict and infer system states and values. These models obtained from automatic learning have the advantage of being easily updated to workload or configuration changes by re-taking examples and re-training the predictors. So employing automatic modeling and predictive abilities, we can find new methods for making "intelligent" decisions and discovering new information and knowledge from systems. This thesis departs from the state of the art, where management is based on administrators expertise, well known data, ad-hoc studied algorithms and models, and elements to be studied from computing machine point of view; to a novel state of the art where management is driven by models learned from the same system, providing useful feedback, making up for incomplete, missing or uncertain data, from a global network of datacenters point of view. - First of all, we cover the scenario where the decision maker works knowing all pieces of information from the system: how much will each job consume, how is and will be the desired quality of service, what are the deadlines for the workload, etc. All of this focusing on each component and policy of each element involved in executing these jobs. -Then we focus on the scenario where instead of fixed oracles that provide us information from an expert formula or set of conditions, machine learning is used to create these oracles. Here we look at components and specific details while some part of the information is not known and must be learned and predicted. - We reduce the problem of optimizing resource allocations and requirements for virtualized web-services to a mathematical problem, indicating each factor, variable and element involved, also all the constraints the scheduling process must attend to. The scheduling problem can be modeled as a Mixed Integer Linear Program. Here we face an scenario of a full datacenter, further we introduce some information prediction. - We complement the model by expanding the predicted elements, studying the main resources (this is CPU, Memory and IO) that can suffer from noise, inaccuracy or unavailability. Once learning predictors for certain components let the decision making improve, the system can become more ¿expert-knowledge independent¿ and research can focus on an scenario where all the elements provide noisy, uncertainty or private information. Also we introduce to the management optimization new factors as for each datacenter context and costs may change, turning the model as "multi-datacenter" - Finally, we review of the cost of placing datacenters depending on green energy sources, and distribute the load according to green energy availability

Edge Computing for Extreme Reliability and Scalability

The massive number of Internet of Things (IoT) devices and their continuous data collection will lead to a rapid increase in the scale of collected data. Processing all these collected data at the central cloud server is inefficient, and even is unfeasible or unnecessary. Hence, the task of processing the data is pushed to the network edges introducing the concept of Edge Computing. Processing the information closer to the source of data (e.g., on gateways and on edge micro-servers) not only reduces the huge workload of central cloud, also decreases the latency for real-time applications by avoiding the unreliable and unpredictable network latency to communicate with the central cloud

Live-Migration in Cloud Computing Environment

O tráfego global de IP aumentou cinco vezes nos últimos cinco anos, e prevê-se que crescerá três vezes nos próximos cinco. Já para o período de 2013 a 2018, anteviu-se que o total do tráfego de IP iria aumentar a sua taxa composta de crescimento anual (CAGR) em, aproximadamente, 3.9 vezes. Assim, os Prestadores de Serviços estão a sofrer com este acréscimo exponencial, que é proveniente do número abismal de dispositivos e utilizadores que estão ligados à Internet, bem como das suas exigências por vários recursos e serviços de rede (como por exemplo, distribuição de conteúdo multimédia, segurança, mobilidade, etc.). Mais especificamente, estes estão com dificuldades em: introduzir novos serviços geradores de receitas; e otimizar e adaptar as suas infraestruturas mais caras, centros de processamento de dados, e redes empresariais e de longa distância (COMpuTIN, 2015). Estas redes continuam a ter sérios problemas (no que toca a agilidade, gestão, mobilidade e no tempo despendido para se adaptarem), que não foram corrigidos até ao momento. Portanto, foram propostos novos modelos de Virtualização de Funções da Rede (NFV) e tecnologias de Redes de Software Definidos (SDN) para solucionar gastos operacionais e de capital não otimizado, e limitações das redes (Lopez, 2014, Hakiri and Berthou, 2015). Para se ultrapassar tais adversidades, o Instituto Europeu de Normas de Telecomunicações (ETSI) e outras organizações propuseram novas arquiteturas de rede. De acordo com o ETSI, a NFV é uma técnica emergente e poderosa, com grande aplicabilidade, e com o objetivo de transformar a maneira como os operadores desenham as redes. Isto é alcançado pela evolução da tecnologia padrão de virtualização TI, de forma a consolidar vários tipos de equipamentos de redes como: servidores de grande volume, routers, switches e armazenamento (Xilouris et al., 2014). Nesta dissertação, foram usadas as soluções mais atuais de SDN e NFV, de forma a produzir um caso de uso que possa solucionar o crescimento do tráfego de rede e a excedência da sua capacidade máxima. Para o desenvolvimento e avalização da solução, foi instalada a plataforma de computação na nuvem OpenStack, de modo a implementar, gerir e testar um caso de uso de Live Migration.Global IP traffic has increased fivefold over the past five years, and will continue increasing threefold over the next five years. The overall IP traffic will grow at a compound annual growth rate (CAGR) nearly 3.9-fold from 2013 to 2018. Service Providers are experiencing the exponential growth of IP traffic that comes from the incredible increased number of devices and users who are connected to the internet along with their demands for various resources and network services like multimedia content distribution, security, mobility and else. Therefore, Service Providers are finding difficult to introduce new revenue generating services, optimize and adapt their expensive infrastructures, data centers, wide-area networks and enterprise networks (COMpuTIN, 2015). The networks continue to have serious known problems, such as, agility, manageability, mobility and time-to-application that have not been successfully addressed so far. Thus, novel Network Function Virtualization (NFV) models and Software-defined Networking (SDN) technologies have been proposed to solve the non-optimal capital and operational expenditures and network’s limitations (Lopez, 2014, Hakiri and Berthou, 2015). In order to solve these issues, the European Telecommunications Standards Institute (ETSI) and other standard organizations are proposing new network architecture approaches. According to ETSI, The Network Functions Virtualization is a powerful emerging technique with widespread applicability, aiming to transform the way that network operators design networks by evolving standard IT virtualization technology to consolidate many network equipment types: high volume servers, routers, switches and storage (Xilouris et al., 2014). In this thesis, the current Software-Defined Networking (SDN) and Network Function Virtualization (NFV) solutions were used in order to make a use case that can address the increasing of network traffic and exceeding its maximum capacity. To develop and evaluate the solution, OpenStack cloud computing platform was installed in order to deploy, manage and test a Live-Migration use-case

Gestão e engenharia de CAP na nuvem híbrida

Doutoramento em InformáticaThe evolution and maturation of Cloud Computing created an opportunity for the emergence of new Cloud applications. High-performance Computing, a complex problem solving class, arises as a new business consumer by taking advantage of the Cloud premises and leaving the expensive datacenter management and difficult grid development. Standing on an advanced maturing phase, today’s Cloud discarded many of its drawbacks, becoming more and more efficient and widespread. Performance enhancements, prices drops due to massification and customizable services on demand triggered an emphasized attention from other markets. HPC, regardless of being a very well established field, traditionally has a narrow frontier concerning its deployment and runs on dedicated datacenters or large grid computing. The problem with common placement is mainly the initial cost and the inability to fully use resources which not all research labs can afford. The main objective of this work was to investigate new technical solutions to allow the deployment of HPC applications on the Cloud, with particular emphasis on the private on-premise resources – the lower end of the chain which reduces costs. The work includes many experiments and analysis to identify obstacles and technology limitations. The feasibility of the objective was tested with new modeling, architecture and several applications migration. The final application integrates a simplified incorporation of both public and private Cloud resources, as well as HPC applications scheduling, deployment and management. It uses a well-defined user role strategy, based on federated authentication and a seamless procedure to daily usage with balanced low cost and performance.O desenvolvimento e maturação da Computação em Nuvem abriu a janela de oportunidade para o surgimento de novas aplicações na Nuvem. A Computação de Alta Performance, uma classe dedicada à resolução de problemas complexos, surge como um novo consumidor no Mercado ao aproveitar as vantagens inerentes à Nuvem e deixando o dispendioso centro de computação tradicional e o difícil desenvolvimento em grelha. Situando-se num avançado estado de maturação, a Nuvem de hoje deixou para trás muitas das suas limitações, tornando-se cada vez mais eficiente e disseminada. Melhoramentos de performance, baixa de preços devido à massificação e serviços personalizados a pedido despoletaram uma atenção inusitada de outros mercados. A CAP, independentemente de ser uma área extremamente bem estabelecida, tradicionalmente tem uma fronteira estreita em relação à sua implementação. É executada em centros de computação dedicados ou computação em grelha de larga escala. O maior problema com o tipo de instalação habitual é o custo inicial e o não aproveitamento dos recursos a tempo inteiro, fator que nem todos os laboratórios de investigação conseguem suportar. O objetivo principal deste trabalho foi investigar novas soluções técnicas para permitir o lançamento de aplicações CAP na Nuvem, com particular ênfase nos recursos privados existentes, a parte peculiar e final da cadeia onde se pode reduzir custos. O trabalho inclui várias experiências e análises para identificar obstáculos e limitações tecnológicas. A viabilidade e praticabilidade do objetivo foi testada com inovação em modelos, arquitetura e migração de várias aplicações. A aplicação final integra uma agregação de recursos de Nuvens, públicas e privadas, assim como escalonamento, lançamento e gestão de aplicações CAP. É usada uma estratégia de perfil de utilizador baseada em autenticação federada, assim como procedimentos transparentes para a utilização diária com um equilibrado custo e performance

Network monitoring in public clouds: issues, methodologies, and applications

Cloud computing adoption is rapidly growing thanks to the carried large technical and economical advantages. Its effects can be observed also looking at the fast increase of cloud traffic: in accordance with recent forecasts, more than 75\% of the overall datacenter traffic will be cloud traffic by 2018. Accordingly, huge investments have been made by providers in network infrastructures. Networks of geographically distributed datacenters have been built, which require efficient and accurate monitoring activities to be operated. However, providers rarely expose information about the state of cloud networks or their design, and seldom make promises about their performance. In this scenario, cloud customers therefore have to cope with performance unpredictability in spite of the primary role played by the network. Indeed, according to the deployment practices adopted and the functional separation of the application layers often implemented, the network heavily influences the performance of the cloud services, also impacting costs and revenues. In this thesis cloud networks are investigated enforcing non-cooperative approaches, i.e.~that do not require access to any information restricted to entities involved in the cloud service provision. A platform to monitor cloud networks from the point of view of the customer is presented. Such a platform enables general customers---even those with limited expertise in the configuration and the management of cloud resources---to obtain valuable information about the state of the cloud network, according to a set of factors under their control. A detailed characterization of the cloud network and of its performance is provided, thanks to extensive experimentations performed during the last years on the infrastructures of the two leading cloud providers (Amazon Web Services and Microsoft Azure). The information base gathered by enforcing the proposed approaches allows customers to better understand the characteristics of these complex network infrastructures. Moreover, experimental results are also useful to the provider for understanding the quality of service perceived by customers. By properly interpreting the obtained results, usage guidelines can be devised which allow to enhance the achievable performance and reduce costs. As a particular case study, the thesis also shows how monitoring information can be leveraged by the customer to implement convenient mechanisms to scale cloud resources without any a priori knowledge. More in general, we believe that this thesis provides a better-defined picture of the characteristics of the complex cloud network infrastructures, also providing the scientific community with useful tools for characterizing them in the future