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The evaluation and prediction of parallel programs performance are becoming more and more important, so that they require appropriate techniques to identify the factors which influence the application execution time and also the way they interact. In this paper, we present some contributions of our research in this area by describing PEMPIs, a new methodology applied to the performance analysis and prediction of MPI programs. A new task graph helps us both to understand details of the application and to increase the accuracy of the prediction models. The proposed techniques are detailed and tested through the modeling of a complete application. PEMPIs efficiency has been proved by the results of this application modeling-most tests executed in a cluster of computers showed errors up to 10%.

Practical knowledge is essential for engineering education. With the COVID-19 pandemic, new challenges have arisen for remote practical learning (e.g., collaborations/experimentations with real equipment when face-to-face offerings are not possible). In this context, LabEAD is a remote lab project that aims to provide practical knowledge learning opportunities for Brazilian engineering students. This article describes how engineering project management methods consisting of application domains, requirement identification, technical solution specification, implementation, and delivery phases, were applied to the development of an Internet of Things (IoT) remote lab architecture. The distributed computing environment allows integration between students’ smartphones and IoT devices deployed in campus labs and in student residences. The code is open-source for facilitated replication and reuse, and the remote lab was built in six months to enable six experiments for the digital electronics lab during the COVID-19 pandemic, covering all the experiments of the original face-to-face offering. More than 70% of the 32 students preferred remote labs over simulations, and only 2 were not approved in the digital electronics course offered remotely.Student perceptions collected by questionnaires showed that they could successfully specify, develop, and present their projects using the remote lab infrastructure in four weeks.

A key feature in virtualization technology is the Live Migration, which allows a Virtual Machine (VM) to be moved from a physical host to another without execution interruption. This feature enables the implementation of more sophisticated policies inside a cloud environment, such as energy and computational resources optimization, and improvement of quality-of-service. However live migration can impose severe performance degradation for the VM application and cause multiple impacts in service provider infrastructure, such as network congestion and colocated VM performance degradation. Different of several studies we consider the VM workload an important factor and we argue that carefully choosing a proper moment to migrate a VM can reduce the live migration penalties. This paper introduces a method to identify the workload cycles of a VM and based on that information it can postpone a Live Migration. In our experiments, using relevant benchmarks the proposed method was able to reduce up to 43% of network data transfer and reduce up to 74% of live migration time when compared to traditional consolidation strategies that perform live migration without considering the VM workload.

A análise do genoma é uma área com amplas pesquisas que permitem o estudo de doenças e o desenvolvimento de novos tratamentos. Para isso, pesquisadores utilizam-se do genoma montado através de ferramentas computacionais para realizar sua análise. Este trabalho apresenta uma análise de desempenho acerca de um algoritmo de correção hı́brida de sequências genômicas, sendo esta uma etapa necessária para a montagem do genoma. Foram implementadas sete versões do algoritmo visando comparar seus desempenhos. Os resultados obtidos a partir dos testes revelam que é possı́vel obter ganhos de desempenho de até cerca de 17 vezes em relação à versão sequencial, e que a melhor versão do algoritmo possui escalabilidade superior à linear.

Abstract. Elasticity is an important feature in cloud computing environments. This feature allows a Virtual Machine to adapt resource allocation according to the nature of its workload. Until now, most memory elasticity implementations require human intervention. The implementation of memory elasticity is not very straightforward, due to old Operating System concepts; in general an Operating System assumes that all installed memory will be static and will not increase or decrease until the next shutdown. This paper compares two techniques for the implementation of memory elasticity, one based on the concept of Exponential Moving Average and the other based on Page Faults. To compare these modes of implementation, a method to measure allocation efficiency based on the space-time product was used. With an Exponential Moving Average, memory could be used more efficiently. When Page Faults were used as the main criteria to allocate or remove memory, the performance improved when compared to the Exponential Moving Average technique.

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O período corrente de isolamento social, adotado como medida para conter a pandemia do COVID-19, deixou aparente a necessidade de repensar métodos e técnicas de ensino, visando assegurar o aprendizado tanto teórico quanto prático dos discentes num momento em que o aprendizado presencial está inviabilizado. Este artigo apresenta a percepção docente e discente, que foram obtidas através de questionários realizados em workshops com os mesmos, de um oferecimento à distância de uma disciplina de laboratório de eletrônica digital em andamento. O oferecimento da disciplina é viabilizado por meio de uma infraestrutura desenvolvida utilizando conceitos de Internet das Coisas, permitindo o oferecimento de uma disciplina prática e respeitando as regras impostas de distanciamento social. Os resultados favoráveis obtidos até o momento foram possíveis através de um trabalho conjunto entre professores, monitores, técnicos e alunos.