Context. Cloud computing is growing fast and has established itself as the next generationsoftware infrastructure. A major role in cloud computing is the virtualization of hardware toisolate systems from each other. This virtualization is often done with Virtual Machines thatemulate both hardware and software, which in turn makes the process isolation expensive. Newtechniques, known as Microservices or containers, has been developed to deal with the overhead.The infrastructure is conjoint with storing, processing and serving vast and unstructureddata sets. The overall cloud system needs to have high performance while providing scalabilityand easy deployment. Microservices can be introduced for all kinds of applications in a cloudcomputing network, and be a better fit for certain products.Objectives. In this study we investigate how a small system consisting of a Cassandra clusterperform while encapsulated in LXC and Docker containers, compared to a non virtualizedstructure. A specific loader is built to stress the cluster to find the limits of the containers.Methods. We constructed an experiment on a three node Cassandra cluster. Test data is sentfrom the Cassandra-loader from another server in the network. The Cassandra processes are thendeployed in the different architectures and tested. During these tests the metrics CPU, disk I/O,network I/O are monitored on the four servers. The data from the metrics is used in statisticalanalysis to find significant deviations.Results. Three experiments are being conducted and monitored. The Cluster test pointed outthat isolated Docker container indicate major latency during disk reads. A local stress test furtherconfirmed those results. The step-wise test in turn, implied that disk read latencies happened dueto isolated Docker containers needs to read more data to handle these requests. All Microservicesprovide some overheads, but fall behind the most for read requests.Conclusions. The results in this study show that virtualization of Cassandra nodes in a clusterbring latency in comparison to a non virtualized solution for write operations. However, thoselatencies can be neglected if scalability in a system is the main focus. For read operationsall microservices had reduced performance and isolated Docker containers brought out thehighest overhead. This is due to the file system used in those containers, which makes disk I/Oslower compared to the other structures. If a Cassandra cluster is to be launched in a containerenvironment we recommend a Docker container with mounted disks to bypass Dockers filesystem or a LXC solution.

A crescente oferta de programas de código aberto na rede mundial de computadores expõe potenciais usuários a muitas possibilidades de escolha. Em face da pluralidade de interesses desses indivíduos, mecanismos eficientes que os aproximem daquilo que buscam trazem benefícios para eles próprios, assim como para os desenvolvedores dos programas. Este trabalho apresenta o AppRecommender, um recomendador de aplicativos GNU/Linux que realiza uma filtragem no conjunto de programas disponíveis e oferece sugestões individualizadas para os usuários. Tal feito é alcançado por meio da análise de perfis e descoberta de padrões de comportamento na população estudada, de sorte que apenas os aplicativos considerados mais suscetíveis a aceitação sejam oferecidos aos usuários. / The increasing availability of open source software on the World Wide Web exposes potential users to a wide range of choices. Given the individuals plurality of interests, mechanisms that get them close to what they are looking for would benefit users and software developers. This work presents AppRecommender, a recommender system for GNU/Linux applications which performs a filtering on the set of available software and individually offers suggestions to users. This is achieved by analyzing profiles and discovering patterns of behavior of the studied population, in a way that only those applications considered most prone to acceptance are presented to users.

A crescente oferta de programas de código aberto na rede mundial de computadores expõe potenciais usuários a muitas possibilidades de escolha. Em face da pluralidade de interesses desses indivíduos, mecanismos eficientes que os aproximem daquilo que buscam trazem benefícios para eles próprios, assim como para os desenvolvedores dos programas. Este trabalho apresenta o AppRecommender, um recomendador de aplicativos GNU/Linux que realiza uma filtragem no conjunto de programas disponíveis e oferece sugestões individualizadas para os usuários. Tal feito é alcançado por meio da análise de perfis e descoberta de padrões de comportamento na população estudada, de sorte que apenas os aplicativos considerados mais suscetíveis a aceitação sejam oferecidos aos usuários. / The increasing availability of open source software on the World Wide Web exposes potential users to a wide range of choices. Given the individuals plurality of interests, mechanisms that get them close to what they are looking for would benefit users and software developers. This work presents AppRecommender, a recommender system for GNU/Linux applications which performs a filtering on the set of available software and individually offers suggestions to users. This is achieved by analyzing profiles and discovering patterns of behavior of the studied population, in a way that only those applications considered most prone to acceptance are presented to users.