Estado del arte en soluciones de virtualización : Sistemas gestores de Cloud: OpenNebula

En el presente proyecto se ha abordado la tarea de acercar las tecnologías existentes de plataformas de gestión de infraestructuras ofrecidas en la nube (Cloud Management Platform, aka CMP) al mundo empresarial. En concreto, se ha desplegado una solución de explotación de infraestructuras privadas en la nube (IaaS) enfocada a la gestión de un datacenter virtualizado, utilizando para ello soluciones completamente basadas en software libre, en concreto, OpenNebula.

Composizione dinamica di funzioni di rete virtuali in ambienti cloud

Questo documento si interroga sulle nuove possibilità offerte agli operatori del mondo delle Reti di Telecomunicazioni dai paradigmi di Network Functions Virtualization, Cloud Computing e Software Defined Networking: questi sono nuovi approcci che permettono la creazione di reti dinamiche e altamente programmabili, senza disdegnare troppo il lato prestazionale. L'intento finale è valutare se con un approccio di questo genere si possano implementare dinamicamente delle concatenazioni di servizi di rete e se le prestazioni finali rispecchiano ciò che viene teorizzato dai suddetti paradigmi. Tutto ciò viene valutato per cercare una soluzione efficace al problema dell'ossificazione di Internet: infatti le applicazioni di rete, dette middle-boxes, comportano costi elevati, situazioni di dipendenza dal vendor e staticità delle reti stesse, portando all'impossibilità per i providers di sviluppare nuovi servizi. Il caso di studio si basa proprio su una rete che implementa questi nuovi paradigmi: si farà infatti riferimento a due diverse topologie, una relativa al Livello L2 del modello OSI (cioè lo strato di collegamento) e una al Livello L3 (strato di rete). Le misure effettuate infine mostrano come le potenzialità teorizzate siano decisamente interessanti e innovative, aprendo un ventaglio di infinite possibilità per il futuro sviluppo di questo settore.

Elastic computing on Cloud resources for the CMS experiment

Nowadays, data handling and data analysis in High Energy Physics requires a vast amount of computational power and storage. In particular, the world-wide LHC Com- puting Grid (LCG), an infrastructure and pool of services developed and deployed by a ample community of physicists and computer scientists, has demonstrated to be a game changer in the efficiency of data analyses during Run-I at the LHC, playing a crucial role in the Higgs boson discovery. Recently, the Cloud computing paradigm is emerging and reaching a considerable adoption level by many different scientific organizations and not only. Cloud allows to access and utilize not-owned large computing resources shared among many scientific communities. Considering the challenging requirements of LHC physics in Run-II and beyond, the LHC computing community is interested in exploring Clouds and see whether they can provide a complementary approach - or even a valid alternative - to the existing technological solutions based on Grid. In the LHC community, several experiments have been adopting Cloud approaches, and in particular the experience of the CMS experiment is of relevance to this thesis. The LHC Run-II has just started, and Cloud-based solutions are already in production for CMS. However, other approaches of Cloud usage are being thought of and are at the prototype level, as the work done in this thesis. This effort is of paramount importance to be able to equip CMS with the capability to elastically and flexibly access and utilize the computing resources needed to face the challenges of Run-III and Run-IV. The main purpose of this thesis is to present forefront Cloud approaches that allow the CMS experiment to extend to on-demand resources dynamically allocated as needed. Moreover, a direct access to Cloud resources is presented as suitable use case to face up with the CMS experiment needs. Chapter 1 presents an overview of High Energy Physics at the LHC and of the CMS experience in Run-I, as well as preparation for Run-II. Chapter 2 describes the current CMS Computing Model, and Chapter 3 provides Cloud approaches pursued and used within the CMS Collaboration. Chapter 4 and Chapter 5 discuss the original and forefront work done in this thesis to develop and test working prototypes of elastic extensions of CMS computing resources on Clouds, and HEP Computing “as a Service”. The impact of such work on a benchmark CMS physics use-cases is also demonstrated.

Serverless Computing il caso Google Cloud Functions

Il serverless é un paradigma del cloud computing al giorno d’oggi sempre più diffuso; si basa sulla scrittura di funzioni stateless in quanto le attività relative alla loro manutenzione e scalabilità fanno capo al provider dei servizi cloud. Lo sviluppatore deve quindi solamente concentrarsi sulla creazione del prodotto. Questo lavoro si apre con un’analisi del cloud computing introducendo i principali modelli di applicazione, passando dal parlare di servizi cloud, con le varie sottocategorie e i relativi utilizzi per poi arrivare a parlare di serverless. Si é scelto di focalizzarsi sulla piattaforma Google con la suite: Google Cloud Platform. In particolare, si parlerà di Google Cloud Functions, una nuova offerta serverless della compagnia, di recente sviluppo e in continuo aggiornamento. Partiremo dalle prime release, analizzeremo l’ambiente di sviluppo, i casi d’uso, vantaggi, svantaggi, parleremo poi di portabilità e verranno mostrati alcuni esempi del loro utilizzo.

Dynamic power- and failure-aware cloud resources allocation for Sets of independent tasks

Cloud computing is increasingly being adopted in different scenarios, like social networking, business applications, scientific experiments, etc. Relying in virtualization technology, the construction of these computing environments targets improvements in the infrastructure, such as power-efficiency and fulfillment of users’ SLA specifications. The methodology usually applied is packing all the virtual machines on the proper physical servers. However, failure occurrences in these networked computing systems can induce substantial negative impact on system performance, deviating the system from ours initial objectives. In this work, we propose adapted algorithms to dynamically map virtual machines to physical hosts, in order to improve cloud infrastructure power-efficiency, with low impact on users’ required performance. Our decision making algorithms leverage proactive fault-tolerance techniques to deal with systems failures, allied with virtual machine technology to share nodes resources in an accurately and controlled manner. The results indicate that our algorithms perform better targeting power-efficiency and SLA fulfillment, in face of cloud infrastructure failures.

Android as a cloud ticket validator

Trabalho Final de Mestrado para a obtenção do grau de Mestre em Engenharia Informática e de Computadores

Explorador integrado de serviços de armazenamento na cloud: cloud cockpit

Projeto para obtenção do grau de Mestre em Engenharia Informática e de Computadores

Data analytics in the cloud with flexible mapreduced workflows

Data analytic applications are characterized by large data sets that are subject to a series of processing phases. Some of these phases are executed sequentially but others can be executed concurrently or in parallel on clusters, grids or clouds. The MapReduce programming model has been applied to process large data sets in cluster and cloud environments. For developing an application using MapReduce there is a need to install/configure/access specific frameworks such as Apache Hadoop or Elastic MapReduce in Amazon Cloud. It would be desirable to provide more flexibility in adjusting such configurations according to the application characteristics. Furthermore the composition of the multiple phases of a data analytic application requires the specification of all the phases and their orchestration. The original MapReduce model and environment lacks flexible support for such configuration and composition. Recognizing that scientific workflows have been successfully applied to modeling complex applications, this paper describes our experiments on implementing MapReduce as subworkflows in the AWARD framework (Autonomic Workflow Activities Reconfigurable and Dynamic). A text mining data analytic application is modeled as a complex workflow with multiple phases, where individual workflow nodes support MapReduce computations. As in typical MapReduce environments, the end user only needs to define the application algorithms for input data processing and for the map and reduce functions. In the paper we present experimental results when using the AWARD framework to execute MapReduce workflows deployed over multiple Amazon EC2 (Elastic Compute Cloud) instances.

Integrated Management of IaaS Resources

This paper proposes and reports the development of an open source solution for the integrated management of Infrastructure as a Service (IaaS) cloud computing resources, through the use of a common API taxonomy, to incorporate open source and proprietary platforms. This research included two surveys on open source IaaS platforms (OpenNebula, OpenStack and CloudStack) and a proprietary platform (Parallels Automation for Cloud Infrastructure - PACI) as well as on IaaS abstraction solutions (jClouds, Libcloud and Deltacloud), followed by a thorough comparison to determine the best approach. The adopted implementation reuses the Apache Deltacloud open source abstraction framework, which relies on the development of software driver modules to interface with different IaaS platforms, and involved the development of a new Deltacloud driver for PACI. The resulting interoperable solution successfully incorporates OpenNebula, OpenStack (reuses pre-existing drivers) and PACI (includes the developed Deltacloud PACI driver) nodes and provides a Web dashboard and a Representational State Transfer (REST) interface library. The results of the exchanged data payload and time response tests performed are presented and discussed. The conclusions show that open source abstraction tools like Deltacloud allow the modular and integrated management of IaaS platforms (open source and proprietary), introduce relevant time and negligible data overheads and, as a result, can be adopted by Small and Medium-sized Enterprise (SME) cloud providers to circumvent the vendor lock-in problem whenever service response time is not critical.

Cloud-assisted Wireless Body Area Networks

Wireless Body Area Networks (WBANs) have emerged as a promising technology for medical and non-medical applications. WBANs consist of a number of miniaturized, portable, and autonomous sensor nodes that are used for long-term health monitoring of patients. These sensor nodes continuously collect information of patients, which are used for ubiquitous health monitoring. In addition, WBANs may be used for managing catastrophic events and increasing the effectiveness and performance of rescue forces. The huge amount of data collected by WBAN nodes demands scalable, on-demand, powerful, and secure storage and processing infrastructure. Cloud computing is expected to play a significant role in achieving the aforementioned objectives. The cloud computing environment links different devices ranging from miniaturized sensor nodes to high-performance supercomputers for delivering people-centric and context-centric services to the individuals and industries. The possible integration of WBANs with cloud computing (WBAN-cloud) will introduce viable and hybrid platform that must be able to process the huge amount of data collected from multiple WBANs. This WBAN-cloud will enable users (including physicians and nurses) to globally access the processing and storage infrastructure at competitive costs. Because WBANs forward useful and life-critical information to the cloud – which may operate in distributed and hostile environments, novel security mechanisms are required to prevent malicious interactions to the storage infrastructure. Both the cloud providers and the users must take strong security measures to protect the storage infrastructure.

Ownserver: sistema doméstico de prestação de serviços cloud

Neste trabalho foi considerada a possibilidade de incorporar serviços remotos, normalmente associados a serviços web e cloud computing, numa solução local que centralizasse os vários serviços num único sistema e permitisse aos seus utilizadores consumir e configurar os mesmos, quer a partir da rede local, quer remotamente a partir da Internet. Desta forma seria possível conciliar o acesso a partir de qualquer local com internet, característico nas clouds, com a simplicidade de concentrar num só sistema vários serviços que são por norma oferecidos por entidades distintas e ainda permitir aos seus utilizadores o controlo e configuração sobre os mesmos. De forma a validar que este conceito é viável, prático e funcional, foram implementadas duas componentes. Um cliente que corre nos dispositivos dos utilizadores e que proporciona a interface para consumir os serviços disponíveis e um servidor que irá conter e prestar esses serviços aos clientes. Estes serviços incluem lista de contactos, mensagens instantâneas, salas de conversação, transferência de ficheiros, chamadas e conferências de voz e vídeo, pastas remotas, pastas sincronizadas, backups, pastas partilhadas, VoD (Video-on Demand) e AoD (Audio-on Demand). Para o desenvolvimento do cliente e do servidor foi utilizada a framework Qt que recorre à linguagem de programação C++ e ao conjunto de bibliotecas que possui, para o desenvolvimento de aplicações multiplataforma. Para as comunicações entre clientes e servidor, foi utilizado o protocolo XMPP (Extensible Messaging and Presence Protocol), pela forma da biblioteca qxmpp e do servidor XMPP ejabberd. Pelo facto de conter um conjunto de centenas de extensões atualmente ativas que auferem funcionalidades como salas de conversação, transferências de ficheiros e até estabelecer sessões multimédia, graças à sua flexibilidade permitiu ainda a criação de extensões personalizada necessárias para algumas funcionalidades que se pretendeu implementar. Foi ainda utilizado no servidor a framework ffmpeg para suportar algumas funcionalidades multimédia. Após a implementação do cliente para Windows e Linux, e de implementar o servidor em Linux foi realizado um conjunto de testes funcionais para perceber se as funcionalidades e seus mecanismos funcionam corretamente. No caso onde a análise da performance e do consumo de recursos era importante, foram realizados testes de performance e testes de carga.

On the cloud deployment of a session abstraction for service/data aggregation

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Personalization platform for multimodal ubiquitous computing applications

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Communications with QoS in IoT&Cloud environment

Dissertação de mestrado integrado em Engenharia de Telecomunicações e Informática

Phylogenetic Code in the Cloud - Can it Meet the Expectations?

Cloud computing has recently become very popular, and several bioinformatics applications exist already in that domain. The aim of this article is to analyse a current cloud system with respect to usability, benchmark its performance and compare its user friendliness with a conventional cluster job submission system. Given the current hype on the theme, user expectations are rather high, but current results show that neither the price/performance ratio nor the usage model is very satisfactory for large-scale embarrassingly parallel applications. However, for small to medium scale applications that require CPU time at certain peak times the cloud is a suitable alternative.