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Resumo: O trabalho investiga a potência do dispositivo enquanto estratégia criativa e política no documentário contemporâneo, adotando como recorte três filmes brasileiros recentes (Pacific, Rua de mão dupla e Câmara escura). Na análise, destacamos a vitalidade estética de cada obra -como elas enfrentam e sugerem alternativas para questões relevantes na prática documentária -, os pontos de convergência e distanciamento entre as produções, bem como as performances instigadas nos personagens pelo "efeito-câmera": condutas ambivalentes que oscilam entre o crescente anseio de exposição e o desejo de restabelecimento da intimidade. O percurso analítico parte de uma revisão do conceito de dispositivo para, em seguida, mensurar sua aplicação e resultado em cada filme.Palavras-chave: documentário contemporâneo; dispositivo, efeito-câmera.Abstract: Notes on the dispositif in contemporary documentary -This paper investigates the power of the "dispositif" as a creative and political strategy in contemporary documentary, adopting as object three recent Brazilian movies (Pacific, Rua de mão dupla e Câmara escura). In the analysis, we highlight their aesthetic vitality -the way they confront and suggest alternatives to important problems in documentary tradition -, their points of convergence and divergence, as well as the performances instigated by "the camera effect" in each of them: ambivalent performances that fluctuate between the growing need of exposure and the strong desire of restoring intimacy. Our analytical course starts with a theoretical review of the concept of "dispositif", followed by a study of its application and results in each movie stated.

Mobile Edge Computing (MEC) is a network architecture that takes advantage of resources available at the edge of the network to enhance the mobile user experience by decreasing the service latency. MEC solutions need to dynamically allocate the requests as close as possible to their users to avoid high latency. However, the request allocation does not depend only on the geographical location of the servers, but also on their requirements. The task of choosing and allocating appropriate servers in a MEC environment is challenging because it involves many parameters. This paper proposes a Stochastic Petri Net (SPN) model to represent a MEC scenario and analyze its performance. The model focuses on parameters that can directly impact the service Mean Response Time (MRT) and resource utilization level. We propose case studies with numerical analyzes using real-world values to validate the proposed model. The main objective is to provide a practical guide to assist infrastructure administrators to adapt their architectures, finding a trade-off between MRT and resource utilization level.

Low latency and high availability of resources are essential characteristics to guarantee the quality of services in health systems. Hospital systems must be efficient to prevent loss of human life. Smart hospitals promise a health revolution by capturing and transmitting patient data to doctors in real-time via a wireless sensor network. However, there is a significant difficulty in assessing the performance and availability of such systems in real contexts due to failures not being tolerated and high implementation costs. This paper adopts analytical models to assess the performance and availability of intelligent hospital systems without having to invest in real equipment beforehand. Two Stochastic Petri Net models were proposed to represent intelligent hospital architectures. One model is used to assess performance, and another to assess availability. The models are pretty parametric, making it possible to calibrate the resources, service times, times between failures, and times between repairs. The availability model, for example, allows you to define 48 parameters, allowing you to evaluate a large number of scenarios. The analysis showed that the arrival rate in the performance model is an impacting parameter. It was possible to observe the close relationship between MRT, resource utilization, and discard rate in different scenarios, especially for high arrival rates. Three scenarios were explored considering the second model. The highest availability results were observed in scenario A, composed of server redundancy (local and remote). Such scenario—with redundancy—presented an availability of 99.9199%, that is, 7.01 h/year of inactivity. In addition, this work presents a sensitivity analysis that identifies the most critical components of the architecture. Therefore, this work can help hospital system administrators plan more optimized architectures according to their needs.

Mobile Edge Computing (MEC) has emerged as a promising network computing paradigm associated with mobile devices at local areas to diminish network latency under the employment and utilization of cloud/edge computing resources. In that context, MEC solutions are required to dynamically allocate mobile requests as close as possible to their computing resources. Moreover, the computing power and resource capacity of MEC server machines can directly impact the performance and operational availability of mobile apps and services. The systems practitioners must understand the trade off between performance and availability in systems design stages. The analytical models are suited to such an objective. Therefore, this paper proposes Stochastic Petri Net (SPN) models to evaluate both performance and availability of MEC environments. Different to previous work, our proposal includes unique metrics such as discard probability and a sensitivity analysis that guides the evaluation decisions. The models are highly flexible by considering fourteen transitions at the base model and twenty-five transitions at the extended model. The performance model was validated with a real experiment, the result of which indicated equality between experiment and model with p-value equal to 0.684 by t-Test. Regarding availability, the results of the extended model, different from the base model, always remain above 99%, since it presents redundancy in the components that were impacting availability in the base model. A numerical analysis is performed in a comprehensive manner, and the output results of this study can serve as a practical guide in designing MEC computing system architectures by making it possible to evaluate the trade-off between Mean Response Time (MRT) and resource utilization.

Surveillance monitoring systems are highly necessary, aiming to prevent many social problems in smart cities. The internet of things (IoT) nowadays offers a variety of technologies to capture and process massive and heterogeneous data. Due to the fact that (i) advanced analyses of video streams are performed on powerful recording devices; while (ii) surveillance monitoring services require high availability levels in the way that the service must remain connected, for example, to a connection network that offers higher speed than conventional connections; and that (iii) the trust-worthy dependability of a surveillance system depends on various factors, it is not easy to identify which components/devices in a system architecture have the most impact on the dependability for a specific surveillance system in smart cities. In this paper, we developed stochastic Petri net models for a surveillance monitoring system with regard to varying several parameters to obtain the highest dependability. Two main metrics of interest in the dependability of a surveillance system including reliability and availability were analyzed in a comprehensive manner. The analysis results show that the variation in the number of long-term evolution (LTE)-based stations contributes to a number of nines (#9s) increase in availability. The obtained results show that the variation of the mean time to failure (MTTF) of surveillance cameras exposes a high impact on the reliability of the system. The findings of this work have the potential of assisting system architects in planning more optimized systems in this field based on the proposed models.

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