Yes / Failure Prediction has long known to be a challenging problem. With the evolving trend of technology and growing complexity of high-performance cloud data centre infrastructure, focusing on failure becomes very vital particularly when designing systems for the next generation. The traditional runtime fault-tolerance (FT) techniques such as data replication and periodic check-pointing are not very effective to handle the current state of the art emerging computing systems. This has necessitated the urgent need for a robust system with an in-depth understanding of system and component failures as well as the ability to predict accurate potential future system failures. In this paper, we studied data in-production-faults recorded within a five years period from the National Energy Research Scientific computing centre (NERSC). Using the data collected from the Computer Failure Data Repository (CFDR), we developed an effective failure prediction model focusing on high-performance cloud data centre infrastructure. Using the Auto-Regressive Moving Average (ARMA), our model was able to predict potential future failures in the system. Our results also show a failure prediction accuracy of 95%, which is good.

Made available in DSpace on 2016-06-02T19:06:15Z (GMT). No. of bitstreams: 1 6215.pdf: 2705931 bytes, checksum: f13134b07bf961a0e166ae8f6fdc0bf0 (MD5) Previous issue date: 2014-02-27 / Financiadora de Estudos e Projetos / During the last decade, the advent of large scale processing and the need for rapid modification of computational structures have increased the popularity of Cloud Computing, particularly the Infrastructure as a Service model. Several companies have invested in infrastructure to become providers of this kind of service, whether for general public or only to supply their own business needs. This has increased the number of virtualized datacenters across the world and created a growing interest in interoperability between different providers. However, due to the lack of technology standardization, and to limitations in the current network s architecture, this interoperability is still an issue. Based on this, this research project presents an OpenFlow based network flow redirection architecture to support service continuity during the migration of applications between different IaaS providers. The tests performed show the applicability of the proposed architecture in a real network environment, having control only of the network edges, and without setting up any specific hardware. / Durante a ultima década, o advento do processamento em larga escala e a necessidade de rápida modificação de estruturas computacionais fez com que a computação em nuvem se popularizasse, em particular na forma de aprovisionamento de Infraestrutura como Serviço. Diversas companhias investiram em infraestrutura para se tornarem provedores desse tipo de serviço, seja para o publico ou para proverem recursos para seus próprios negócios. Isto aumentou o numero de centros de dados virtualizados e gerou o interesse na interoperabilidade entre os diferentes provedores. Entretanto, devido a falta de padronização de tecnologias, e devido a limitações na arquitetura das redes atuais, essa interoperabilidade ainda e um assunto em aberto. Com base nisso, o presente trabalho apresenta uma arquitetura de redirecionamento de fluxos de rede baseada em OpenFlow para o suporte a continuidade de serviço durante a migração de aplicações entre diferentes provedores de IaaS. Os testes realizados comprovam sua aplicabilidade em um cenário real, controlando apenas as bordas da rede, e sem a instalação de nenhum hardware específico.

L’un des principaux défis des fournisseurs de services cloud est d’offrir aux utilisateurs une performance acceptable, tout en minimisant les besoins en matériel et énergie. Dans cette thèse CIFRE menée avec Outscale, un fournisseur de cloud, nous visons à optimiser l’allocation des ressources en utilisant de nouvelles sources d’information. Nous caractérisons la charge de travail pour comprendre le stress résultant sur l’orchestrateur, et la compétition pour les ressources disponibles qui dégrade la qualité de service. Nous proposons un modèle pour prédire la durée d’exécution des VMs à partir de caractéristiques prédictives disponibles au démarrage. Enfin, nous évaluons la sensibilité aux erreurs d’un algorithme de placement des VMs de la littérature qui se base sur ces prédictions. Nous ne trouvons pas d’intérêt à coupler note système prédictif avec cet algorithme, mais nous proposons d’autres façons d’utiliser les prédictions pour optimiser le placement des VMs. / One of the main challenges for cloud computing providers remains to offer trustable performance for all users, while maintaining an efficient use of hardware and energy resources. In the context of this CIFRE thesis lead with Outscale, apublic cloud provider, we perform an in-depth study aimed at making management algorithms use new sources of information. We characterize Outscale’s workload to understand the resulting stress for the orchestrator, and the contention for hardware resources. We propose models to predict the runtime of VMs based on features which are available when they start. We evaluate the sensitivity with respect to prediction error of a VM placement algorithm from the literature that requires such predictions. We do not find any advantage in coupling our prediction model and the selected algorithm, but we propose alternative ways to use predictions to optimize the placement of VMs.

Virtualisation is one of the underlying technologies that led to the success of cloud computing platforms (CCPs). The technology, along with other features such as multitenancy allows delivering of computing resources in the form of service through efficient sharing of physical resources. As these resources are provided through virtualisation, a robust agreement is outlined for both the quantity and quality-of-service (QoS) in a service level agreement (SLA) documents. QoS is one of the essential components of SLA, where performance is one of its primary aspects. As the technology is progressively maturing and receiving massive acceptance, researchers from industry and academia continue to carry out novel theoretical and practical studies of various essential aspects of CCPs with significant levels of success. This thesis starts with the assessment of the current level of knowledge in the literature of cloud computing in general and CCPs in particular. In this context, a substantive literature review was carried out focusing on performance modelling, testing, analysis and evaluation of Infrastructure as a Service (IaaS), methodologies. To this end, a systematic mapping study (SMSs) of the literature was conducted. SMS guided the choice and direction of this research. The SMS was followed by the development of a novel open queueing network model (QNM) at equilibrium for the performance modelling and analysis of an OpenStack IaaS CCP. Moreover, it was assumed that an external arrival pattern is Poisson while the queueing stations provided exponentially distributed service times. Based on Jackson’s theorem, the model was exactly decomposed into individual M/M/c (c ≥ 1) stations. Each of these queueing stations was analysed in isolation, and closed-form expressions for key performance metrics, such as mean response time, throughput, server (resource) utilisation as well as bottleneck device were determined. Moreover, the research was extended with a proposed open QNM with a bursty external arrival pattern represented by a Compound Poisson Process (CPP) with geometrically distributed batches, or equivalently, variable Generalised Exponential (GE) interarrival and service times. Each queueing station had c (c ≥ 1) GE-type servers. Based on a generic maximum entropy (ME) product form approximation, the proposed open GE-type QNM was decomposed into individual GE/GE/c queueing stations with GE-type interarrival and service times. The evaluation of the performance metrics and bottleneck analysis of the QNM were determined, which provided vital insights for the capacity planning of existing CCP architectures as well as the design and development of new ones. The results also revealed, due to a significant impact on the burstiness of interarrival and service time processes, resulted in worst-case performance bounds scenarios, as appropriate. Finally, an investigation was carried out into modelling and analysis of performance and security trade-offs for a CCP architecture, based on a proposed generalised stochastic Petri net (GSPN) model with security-detection control model (SDCM). In this context, ‘optimal’ combined performance and security metrics were defined with both M-type or GE-type arrival and service times and the impact of security incidents on performance was assessed. Typical numerical experiments on the GSPN model were conducted and implemented using the Möbius package, and an ‘optimal’ trade-offs were determined between performance and security, which are crucial in the SLA of the cloud computing services. / Petroleum technology development fund (PTDF) of the government of Nigeria Usmanu Danfodiyo University, Sokoto

Cloud Computing (CC) is one of the fast growing computer network technologies and many companies offer their services through cloud network. Cloud Computing has many properties with respect to the existing traditional service provisions like scalability, availability, fault tolerance, capability and so on which are supported by many IT companies like Google, Amazon, Salesforce.com. These IT companies have more chances to adapt their services into a new environment, known as Cloud computing systems. There are many cloud computing services which are being provided by many IT companies.The purpose of this thesis is to investigate which cloud environment (public, private and hybrid) and services (Infrastructure as a Service, Software as a Service, and Platform as a Service) are suitable for Swedish Armed Forces (SWAF) with respect to performance, security, cost, flexibility and functionality. SWAF is using private (internal) cloud for communications where both sensitive and non-sensitive information are located in the internal cloud. There are problems like maintenance of hardware, cost issues and secure communication while maintaining the private cloud. In order to overcome those problems we have suggested a hybrid and community cloud environment and SaaS, IaaS, PaaS services for SWAF.For suggesting these cloud environments and cloud services we have performed a literature study and two empirical studies (survey and interviews) with different organizations.A new cloud model is designed based on the suggested cloud environment, separate storage spaces for sensitive and non-sensitive information, suitable services and an effective infrastructure for sharing the internal information for SWAF.