Computação em nuvem

48 p.A computação em nuvem tem se estabelecido nos últimos anos como uma importante plataforma de pesquisa que apresenta uma série vantagens e de estimulantes desafios. A disponibilidade destes recursos agrega uma série de vantagens para as organizações e usuários, visto que preocupações com complexas instalações e manutenções de infra-estruturas deixam de existir e passam a ser de exclusiva responsabilidade dos provedores de serviços, além de possibilitar que os usuários se concentrem exclusivamente nas regras dos negócios que lhes são pertinentes. Este cenário de escalabilidade de serviços, processos e infra-estrutura quase ilimitados não possui precedentes e efetivamente melhora a flexibilidade relacionada a estruturas de tecnologia de informação (TI) bem como pode diminuir o custo total dos negócios pelo provimento de serviços sob demanda.Data provável de publicaçã

Research Opportunities in an Intercloud Environment Using MOSt in SLA4CLOUD Project

International audienceActually, Internet services are becoming essential for different types of users. This evolution impacts how data connections , network routes and resources are configured and used. In this context, the way in which distributed applications and services is becoming more difficult to manage. Cloud computing allows interactions between cloud providers and cloud service providers, and cloud providers can offer deployment services in different datacenters located in different world regions. Much development effort is needed for deploying scalable solutions. One of the these challenges is how to design, develop and deploy cloud solutions that could meet the policies and security requirements of multiple environments needs. The SLA4CLOUD project intends to build an environment where a user can request the deployment of its services anywhere in the underlying infrastructure, using the MOSt platform and its services. This work aims to report some opportunities and research challenges resulting from SLA4CLOUD project in the context of MOSt platform, and the promotion of new projects and partnerships

Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation

This paper proposes a method based on multivariate spatial and temporal correlation to improve prediction accuracy in data reduction for Wireless Sensor Networks (WSN). Prediction of data not sent to the sink node is a technique used to save energy in WSNs by reducing the amount of data traffic. However, it may not be very accurate. Simulations were made involving simple linear regression and multiple linear regression functions to assess the performance of the proposed method. The results show a higher correlation between gathered inputs when compared to time, which is an independent variable widely used for prediction and forecasting. Prediction accuracy is lower when simple linear regression is used, whereas multiple linear regression is the most accurate one. In addition to that, our proposal outperforms some current solutions by about 50% in humidity prediction and 21% in light prediction. To the best of our knowledge, we believe that we are probably the first to address prediction based on multivariate correlation for WSN data reduction

A methodologie for validation, by simulation, of communication protocols specified formally.

Nesse trabalho è proposta uma metodologia para validar, através de simulação, especificações formais de protocolos, realizadas com a técnica de descrição formal (TDF) "Extended State Transition Lançjuaçje (Estelle)", versão de 1983. Essa metodologia utiliza, como ferramenta, o compilador Estelle/83, desenvolvido junto ao Grupo de Redes de Computadores (GRC) da Universidade Federal da Paraíba (UFPB). A esse compilador é acrescentado um núcleo de simulação que permite o acompanhamento da execução do sistema especificado.This work presents a méthodologie for validation, by simulation, of protocol formal specifications described with the formal description technique (FDT ) Extended State Transition Language (Estelle), version 1983. This méthodologie uses the Estelle/83 compiler, developed by the Computer Networks Group (GRC ) of Paraiba University ( UFPb ). A simulation kernel is added to this compiler to observe the execution of the specified system.CNPqCape

Support for incident management in optical networks through critical points identification

In incident management for optical networks, when a fault or event occurs, a network element will often send a notification, in the form of an “alarm”, to operators and managers. Alarms contain valuable information to support the fault management process at the operating level, because it is a persistent indication of a fault. Alarms usually clear only with the triggering of the solution for its cause. To mitigate business risks related to faults in optical networks, service managers need to estimate the impact of a network fault in relation to business needs. In optical networks, identifying redundancy points of high-impact on the network is still a challenge for managers. They often rely on their own experience to prioritize points that possibly need to have redundancy in those networks. This work presents a simulation model capable of locating suitable points for the application of asset redundancy to reduce optical network disruptions, based on business risk. The model is implemented in a software tool and then used in a case study of two reference networks, whose elements may fail according to a realistic failure scenario. Results of the study allow face validity with preliminary evidence that the model is useful to support incident management in optical networks.En la gestión de incidentes de redes ópticas, cuando ocurre una falla o evento, generalmente un componente de la red envía una notificación a los operadores y gerentes. Las alarmas contienen información importante para respaldar el proceso de gestión de fallas a nivel operativo, porque es una indicación persistente de una falla que se borra solo con la solución de condición de disparo. Para mitigar los riesgos comerciales relacionados con las fallas en las redes ópticas, los administradores de servicios deben estimar el impacto de una falla de red en relación con las necesidades del negocio. En las redes ópticas, la identificación de puntos de redundancia de alto impacto de red todavía es un desafío para los gerentes. Usualmente, estos gerentes confían en su propia experiencia para priorizar puntos que posiblemente deban tener redundancia en esas redes. En este trabajo, presentamos una evaluación de un modelo de simulación, capaz de localizar puntos adecuados para la aplicación de redundancia de activos y así reducir las interrupciones de la red óptica, en función del riesgo comercial. El modelo se implementó en una herramienta de software y se procedió a un estudio de caso que incluye dos escenarios de simulación de redes de referencia, con resultados prometedores

A Context-Oriented Framework for Computation Offloading in Vehicular Edge Computing using WAVE and 5G Networks

International audienceDespite technological advances, vehicles are still unable to meet the demands of some applications for massive computational resources in a feasible time. One way to deal with this situation is to integrate the computation offloading technique into a vehicular edge computing system. This integration allows application tasks to be executed on neighboring vehicles or edge servers coupled to base stations. However, the dynamic nature of vehicular networks, allied to over- loaded servers, can lead to failures and reduce the effectiveness of the offloading technique. Therefore, we propose a context-oriented framework for computation offloading to reduce the application execution time and maintain high reliabilityin vehicular edge computing. The framework modules perform computational resources discovery, contextual data gathering, computation tasks distribution, and failure recovery. Its main part is a task assignment algorithm that seeks the best possible server to execute each application task, using contextual information and WAVE and 5G networks. The results of extensive experiments in different vehicular environments show that our framework reduces up to 70.3% of total execution time compared to totally local execution and up to 42.9% compared to other literature approaches. Concerning reliability, our framework achieves to offload up to 89.4% of all tasks and needs to recover only 0.8% of them. Thus, our solution outperforms the totally local execution of the application and other existing computation offloading solutions