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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.