A Virtualized Infrastructure for IVR Applications as Services

Interactive Voice Response (IVR) applications are ubiquitous nowadays. Automated attendant, bank teller and automated surveys are a few of many applications requiring IVR capabilities. Cloud computing is a paradigm gaining a lot of momentum. It has three major service models: Infrastructure as a service – IaaS, Platform as a service – PaaS, and Software as a Service – SaaS. It offers also several inherent benefits such as scalability, resource efficiency and easy introduction of new functionality. However, very few, if any, IVR applications are offered today in cloud-based settings despite of all its potential benefits. This thesis deals with IaaS. Accordingly, we propose a novel architecture for a virtualized IVR infrastructure that relies on RESTFul Web services. The architecture proposes IVR substrates that are virtualized, composed, and assembled on the fly to build IVR applications. As a proof of concept, we have implemented an IaaS prototype on which performance measurements have been done to evaluate our architecture concept. In addition, a simple proof of concept PaaS consisting of a graphical user interface (GUI)has been built to enable the development and management of simple IVR services in the SaaS layer

Algorithmes de gestion de ressources dans une infrastructure de virtualisation de services de jeux vidéo

Depuis sa création, l’industrie du jeu vidéo a connu une effervescence sans précédent en dépassant celle du cinéma. Bien que centrée autour d’un objectif ludique, elle s’étend également à l’apprentissage. D’autre part, depuis quelques années, un nouveau paradigme nommé cloud computing (infonuage) révolutionne la façon dont les entreprises et particuliers accèdent à des ressources informatiques telles que de la puissance de calcul ou de la capacité de stockage. Adoptant une approche élastique d’utilisation à la demande, le cloud computing est une solution très intéressante pour l’externalisation et la simplification des services informatiques. Le cloud computing a d’ailleurs été adapté au jeu vidéo sous le nom de cloud gaming. Son objectif est de permettre à des clients ayant un accès à Internet de jouer à des jeux vidéo exigeants en ressources sur des terminaux à faible consommation, comme les téléphones intelligents ou encore les tablettes. L’idée est que les actions des joueurs sont transmises à des serveurs de traitement distants situés dans le cloud, qui transmettent en retour un flux vidéo et audio. De nos jours, la plupart des études visent à améliorer la qualité de service du cloud gaming, encore pénalisé par la latence des réseaux. Malheureusement, très peu de travaux visent à proposer des innovations au niveau de l’architecture du cloud gaming. Ce mémoire présente deux contributions majeures. La première est une architecture distribuée innovante, où les modules d’un jeu sont séparés en substrates, petites briques logicielles spécialisées, dotées d’interfaces et capables d’accomplir un ensemble de tâches. Cette architecture introduit un nouveau modèle d’affaires puisque les substrates sont hébergés et fournis par des substrates providers. La seconde contribution est la conception d’un algorithme de gestion de ressources pour les infrastructures de virtualisation de jeux vidéo orientés substrates, afin de conserver une certaine qualité de service

Adaptive Failure-Aware Scheduling for Hadoop

Given the dynamic nature of cloud environments, failures are the norm rather than the exception in data centers powering cloud frameworks. Despite the diversity of integrated recovery mechanisms in cloud frameworks, their schedulers still generate poor scheduling decisions leading to tasks' failures due to unforeseen events such as unpredicted demands of services or hardware outages. Traditionally, simulation and analytical modeling have been widely used to analyze the impact of the scheduling decisions on the failures rates. However, they cannot provide accurate results and exhaustive coverage of the cloud systems especially when failures occur. In this thesis, we present new approaches for modeling and verifying an adaptive failure-aware scheduling algorithm for Hadoop to early detect these failures and to reschedule tasks according to changes in the cloud. Hadoop is the framework of choice on many off-the-shelf clusters in the cloud to process data-intensive applications by efficiently running them across distributed multiple machines. The proposed scheduling algorithm for Hadoop relies on predictions made by machine learning algorithms trained on previously executed tasks and data collected from the Hadoop environment. To further improve Hadoop scheduling decisions on the fly, we use reinforcement learning techniques to select an appropriate scheduling action for a scheduled task. Furthermore, we propose an adaptive algorithm to dynamically detect failures of nodes in Hadoop. We implement the above approaches in ATLAS: an AdapTive Failure-Aware Scheduling algorithm that can be built on top of existing Hadoop schedulers. To illustrate the usefulness and benefits of ATLAS, we conduct a large empirical study on a Hadoop cluster deployed on Amazon Elastic MapReduce (EMR) to compare the performance of ATLAS to those of three Hadoop scheduling algorithms (FIFO, Fair, and Capacity). Results show that ATLAS outperforms these scheduling algorithms in terms of failures' rates, execution times, and resources utilization. Finally, we propose a new methodology to formally identify the impact of the scheduling decisions of Hadoop on the failures rates. We use model checking to verify some of the most important scheduling properties in Hadoop (schedulability, resources-deadlock freeness, and fairness) and provide possible strategies to avoid their occurrences in ATLAS. The formal verification of the Hadoop scheduler allows to identify more tasks failures and hence reduce the number of failures in ATLAS

Transferencia tecnológica de networking datacenter a infraestructura virtual cloud computing (iaas) en laboratorio, limitada a saturación de tráfico

Cloud Computing se enfoca en tres categorías como son IaaS, PaaS y SaaS, mediante el uso de recursos virtuales que permiten la transición entre arquitecturas de red tradicional a arquitecturas de virtualización cumpliendo caracteristicas de flexibilidad, disponibilidad, confiabilidad, escalabilidad y portabilidad. Este estudio se centra en la categoría IaaS mediante el análisis de disponibilidad de varios modelos de equipos de red que hacen parte de la Infraestructura de datacenter tradicional de un carrier de comunicaciones, el análisis de disponibilidad se realizó en cinco modelos de equipos con mayor índice de fallas, igualmente se implementó un escenario de laboratorio de Cloud Computing IaaS donde se configuró un componente de virtualización de Servers y un Componente de virtualización de hardware de networking para virtualizar cada uno de los modelos de equipos considerados en la muestra de estudio. De acuerdo con los resultados en los equipos físicos, el modelo 3 es el equipo que presentó menor carga de CPU cuando se dio la saturación de tráfico, el Modelo 1 presentó una indisponibilidad del 32% y el Modelo 4 del 56%, los Modelos 2 ,3 y 5 tuvieron una disponibilidad del 100%. Los resultados en los equipos virtualizados, muestran que el estado de salud de los dispositivos virtualizados tiende a reducirse a medida que hay mayor saturación, esto debido a la falta de un hypervisor en la configuración del ambiente virtualizado, el modelo 2 presenta el mejor comportamiento en CPU, mientras que en el modelo 5 a mayor saturación de tráfico la CPU se consume por completo viéndose afectado el estado de salud del dispositivo, con una indisponibilidad del 100%.Abstract. Cloud Computing focuses on three categories such as IaaS, PaaS and SaaS, by using virtual resources that allow the transition between traditional network architectures to virtualization architectures fulfilling characteristics of flexibility, availability, reliability, scalability and portability. Cloud Computing focuses on three categories such as IaaS, PaaS and SaaS, by using virtual resources that allow the transition from traditional network architectures to virtualization architectures. This study focuses on the IaaS category by analyzing availability of several models of network equipment that are part of the traditional datacenter infrastructure of a communications carrier, the availability analysis was conducted in five equipment models with the highest faults record, also was implemented a Cloud Computing laboratory where was configured a server virtualization component and a networking hardware virtualization component to virtualize each device models considered in the study sample. According to the results in the physical equipments, the equipment model 3 had the most lowest CPU load when the traffic saturation occured, the equipment model 1 had an unavailability of 32% and the equipment model 4 had an unavailability of 56%, the equipment models 2,3 and 4 had a availability of 100%. The results in the virtualized equipment models show that the health of devices tends to decrease as no greater traffic saturation, this due to lack of a hypervisor in the configuration of virtualized environment, the equipment model 2 presents the best performance in CPU, while the equipment model 5 to highest traffic saturation affect the CPU performance and it is completely consumed being affected the health of the device, with an unavailability of 100%.Maestrí