Smart cities aim to address urban challenges such as traffic congestion, pollution, and limited resources. Software platforms play a key role in integrating diverse technologies and services, enabling real-time data processing to support efficient public service delivery. Cloud computing meets the demands of critical applications—like autonomous vehicles and high-load systems—by offering scalable infrastructure with dynamic resource provisioning. However, the lack of structured guidelines for migrating Web applications to the cloud, especially in smart city environments, remains a significant challenge. Migration involves technical adaptation and strategic decisions regarding resource allocation and service configuration. This paper presents a goal-oriented catalogue based on the KAOS (Keep All Objectives Satisfied) model to support web application migration to the cloud in smart city contexts. The catalogue defines goals related to five essential cloud services: relational databases, virtual machines, blob storage, application deployment, and queuing services. It serves as a decision-support tool for developers and urban planners. The proposed approach will be integrated into a smart city platform and validated through case studies and simulations.

A smart city software platform is an integrated environment that helps developers design, implement, deploy, and manage smart city applications. Sentilo is the platform selected to manage smart city solutions in a medium-sized city (Toledo, PR, Brazil) through a partnership between the local university and city administration. This paper aims to evaluate Sentilo’s scalability under different workloads and hardware configurations for both data receiving and provisioning. Results show that Sentilo offers sufficient performance to support the planned and new applications in the city.

Question Answering (QA) systems provide accurate answers to questions; however, they lack the ability to consolidate data from multiple sources, making it difficult to manage complex questions that could be answered with additional data retrieved and integrated on the fly. This integration is inherent to Situational Data Integration (SDI) approaches that deal with dynamic requirements of ad hoc queries that neither traditional database management systems, nor search engines are effective in providing an answer. Thus, if QA systems include SDI characteristics, they could be able to return validated and immediate information for supporting users decisions. For this reason, we surveyed QA-based systems, assessing their capabilities to support SDI features, i.e., Ad hoc Data Retrieval, Data Management, and Timely Decision Support. We also identified patterns concerning these features in the surveyed studies, highlighting them in a timeline that shows the SDI evolution in the QA domain. To the best of your knowledge, this study is precursor in the joint analysis of SDI and QA, showing a combination that can favor the way systems support users. Our analyses show that most of SDI features are rarely addressed in QA systems, and based on that, we discuss directions for further research.

A computação em nuvem trouxe a possibilidade de ter recursos computacionais como um serviço, diferentemente do que estamos acostumados, onde eles são fornecidos como um produto. Um dos serviços oferecidos pelos provedores de nuvem é a gestão de banco de dados relacional, que é um componente indispensável na maioria dos sistemas computacionais hoje em dia. Como existem vários provedores que oferecem o mesmo serviço, surge a necessidade de uma comparação entre eles, a fim de mostrar qual plataforma é viável de acordo com a necessidade da aplicação. Este artigo detalha dois serviços de banco de dados relacional em nuvem, Amazon RDS e Google Cloud SQL, mostrando como eles funcionam, quais são suas limitações e como o custo de ambos é calculado. Com a compreensão desses dois serviços, é feita uma comparação entre eles de acordo com métricas pré-definidas. O serviço Amazon RDS teve um melhor desempenho no tempo de execução das instruções em comparação ao serviço Google Cloud SQL. Empresas que precisam escolher o melhor serviço de banco de dados relacional podem usar esta pesquisa como um indicador.

Traffic reduction in network segments through cache implementations has become an important research topic due to the exponential increase in data requests through the Internet. To support these activities, high-power computing and massive storage must support the creation, retrieval, update, and deletion of large amounts of data. Duplicate requests in the segment from the same clients challenge developing flexible networks. Studies about Information-Centric Networks (ICN) propose to improve the performance of content-based networks because there is a content handling of requests for content, leading to an independent flow from each user. Such as a unicast content delivery ignores identical requests of the other users made to the same service. Therefore, many approaches use Software-Defined Networks (SDN) to provide improved network management to develop a flexible content-based network. This article proposes the PROMID architecture, characterized as an information-centric network through SDN that optimizes the bandwidth consumption in dynamic request-response connections. From the experimental results, we observed that our approach allows us to optimize 84.61% on a total of 32.000 request responses and 34.25% in latency optimization. Moreover, our method measured an increase in the transfer rate from 19.99 Mbps to 164.74 Mbps.

Network Neutrality (NN) ensures an open Internet, in which individual users and organizations have their freedom and rights assured. According to NN rules, Internet service providers cannot block, throttle, or prioritize content on their networks. NN guarantees fairplay and represents an incentive to innovation. The debate around NN has been long and controversial. It is a fact, however, that the most diverse types of NN violations have been identified around the planet. This work presents the ecosystem of agents involved with NN in Brazil and introduces the Network Neutrality Observatory as a social control tool to monitor NN violations and ensure network rights.

A Neutralidade da Rede (NR) assegura uma Internet aberta, na qual os usuários têm direitos assegurados. Em linhas gerais, a NR determina que os provedores de Internet não podem bloquear, estrangular, ou priorizar o conteúdo que trafega em suas redes. Além disso, a NR garante a livre concorrência e incentiva a inovação na rede. O debate sobre a NR tem sido longo e controverso. É fato, entretanto, que as mais diversas violações da NR têm sido identificadas ao redor do planeta. Este trabalho descreve o ecossistema de agentes envolvidos na fiscalização da NR no Brasil e apresenta o Observatório da Neutralidade da Rede como ferramenta de controle social, para fiscalização de violações visando a garantia de direitos.

Finding information may be a complex task for end users, either due to the format in which data is stored, or difficulty in formulating a query that fits the database structure. Conversational interfaces, such as chatbots, can minimize this issue, by facilitating query formulation through natural language. Despite the several applications of chatbots for data querying, the multidimensional aspect of data is rarely addressed in the literature, making the search for information even more challenging. Chatbots can be used for allowing the user to “talk to the data” by adding metrics and dimensions to a query, without relying on technical expertise. Thus, this paper presents a chatbot approach for querying multidimensional data, which captures users intentions and links them to the multidimensional metadata. This linking process allows the bot to set query parameters, using them for accessing the data. The chatbot was implemented for querying an open database containing about 2.5 billions records and over 1700 attributes (including dimensions and metrics), and it was evaluated through an empirical user study involving a group of participants performing a set of search tasks. The evaluation results supported the usefulness of the proposed approach in querying multidimensional data and retrieving information.

Users metadata collection is a concerning issue related to Instant Messaging (IM) due to the potential of privacy violation. Even with messages' content encryption, metadata such as relationships and other communication patterns are exchanged in clear text, incurring in information leakage. In this paper, we investigate popular IM solutions to identify existing metadata, propose a broader nomenclature encompassing similar information, and assess the impact of metadata leakage on users' privacy. We also present a hierarchy of metadata based on our proposed nomenclature to allow for fair comparison among related work and easy gathering of privacy needs of users.

The principle of Network Neutrality (NN) has been debated around the world for nearly two decades. NN states that all traffic in the Internet must be treated equally, regardless of content, origin and/or destination. The main motivation for this principle is to protect fair competition, innovation, and ensure freedom of choice for consumers. The global debate revolves around whether NN should be enforced through regulations or not, as well as the potential impact of such regulations – or lack thereof – on the telecommunications market. In this context, multiple governments worldwide have already implemented NN regulations. In this work, we give an overview of NN regulations in 50 countries across five continents. We first give a brief introduction to the NN global debate. Then, we describe some of the main aspects related to the regulatory process of each country/region. Finally, we compare the different regulations according to common and divergent features identified.

Source discovery aims to facilitate the search for specific information, whose access can be complex and dependent on several distributed data sources. These challenges are often observed in Open Data, where users experience lack of support and difficulty in finding what they need. In this context, Source Discovery tasks could enable the retrieval of a data source most likely to contain the desired information, facilitating Open Data access and transparency. This work presents an approach that blends Latent Dirichlet Allocation (LDA), Word2Vec, and Cosine Similarity for discovering the best open data source given a user query, supported by joint union of the methods' semantic and syntactic capabilities. Our approach was evaluated on its ability to discover, among eight candidates, the right source for a set of queries. Three rounds of experiments were conducted, alternating the number of data sources and test questions. In all rounds, our approach showed superior results when compared with the baseline methods separately, reaching a classification accuracy above 93%, even when all candidate sources had similar content.

The worldwide debate over Network Neutrality (NN) has been raging on for nearly two decades.According to NN principles, all traffic in the Internet must be treated with impartiality.In particular, unfair Traffic Differentiation (TD) is not allowed.Several strategies have been proposed for detecting TD, but locating the source of TD is still an under-explored topic.In this work, we present a holistic approach for unifying TD detection solutions into a single framework with the purpose of locating the source of TD.We propose an algorithm for combining measurements from multiple vantage points, and a strategy for selecting good vantage points.Our proposals leverage Internet peering properties to infer the behavior of individual Autonomous Systems (ASes), without requiring knowledge of the exact routes traversed by measurement probes.To evaluate our proposals, we first ran several experiments to confirm that indeed Internet routes do present the required properties.Then, several simulations were performed to assess the efficiency of our proposals.Results show that our approach is capable of locating TD under several different conditions.Another finding is that issuing measurements from a few end-hosts of core Internet ASes achieves similar results than from a much larger number of end-hosts at the edge.

The purpose of this demo is to visually show a testbed monitoring strategy used to select "stable" sets of nodes to run new protocols. The stability of a set of nodes is defined in terms of the ability of the nodes to communicate among themselves within given time bounds during reasonable intervals of time. We assume an unstable network, in which some nodes may not be able to communicate with some others, and this condition varies with time. In order to measure stability, the communication between pairs of nodes is continuously monitored by measuring the corresponding Round Trip Time (RTT). A stability graph is generated from the monitoring data in which vertices represent network nodes and an each edge means the corresponding nodes are considered to be stable during an observation period. Multiple different structures have been embedded on the stability graph to select a large enough number of nodes on which the new protocols are executed: based on degree, clique, and k-core. We compare the different strategies both in terms of the quality of the set of nodes returned and how they fare as time passes.

The evolution of computing and networking allowed multiple computers to be interconnected, aggregating their processing powers to form High-Performance Computing (HPC) architectures. Applications running in these computational environments process and communicate huge amounts of information, taking several hours or even days to complete their executions so, understanding their computation and communication demands is essential for management purposes. Moreover, although most of HPC applications are implemented with well-known algorithms that tend to follow a given pattern in computation and communication, the classical methods of traffic analysis have not been accurate to classify them. In this sense, we argue that observing and understanding the visual patterns in these applications' traffic matrices (TMs) can provide an accurate classification method. In this paper, we propose TReco, a framework that maintains a database with visual features extracted from these TMs and applies machine learning techniques to classify the HPC applications that are consuming the network, regardless of the number of computational nodes executing it. In our experiments, we reached accuracy rate over 99.75%.

The large availability of tabular Open Data sources with hundreds of attributes and relations makes the query development a difficult task, where analytic queries are common. When writing such queries, often called SPJG (Select-Project-Join-GroupBy), it is necessary to understand a data model and to write JOIN operations. The most common approach is to use business intelligence frameworks, or recent solutions based on keywords or examples. However, they require the utilization of specific applications and there is a lack of support for web-based APIs. We present a solution that eases the task of query development for tabular Open Data analytics through an API, using a simplified query representation where it is not allowed to specify the data relations, and consequently neither the joins over them, called Relation-Free Query. We define a single virtual schema that captures the database structure, which allows the use of relation-free queries in existent DBMS's. The concrete queries are exposed by a RESTful API, which is then translated into a database query language using known query generation solutions. The API is available as a microservice. We present a case study to describe solution, using a real world scenario to query in an integrated database of several Brazilian open databases with hundreds of attributes.

Recent proposals of emerging data storage devices make it necessary to reevaluate all levels of the storage hierarchy to optimize the software stack performance. However, these new devices are not always widely available and therefore early experiments may be impossible. Emulators aim at mimicking as close as possible the behavior of a component, nonetheless, emulating new and fast storage devices is a challenging task due to time perception. In this work, we propose an approach to emulate storage devices using virtual machines (VMs) allowing the evaluation of a new device within a real system. We use a technique called freezing time, which pauses a VM to manipulate its clock and hide the real I/O completion time. Our approach is implemented at the hypervisor level and it is transparent to the guest operating system or application. We evaluate the technique under a real system using regular magnetic disks to emulate faster storage devices. Our method presented a latency error of 6.5% compared to a real device. Moreover, decoupled experiment between two laboratories, at the Barcelona Super Computing Center (BSC) in Spain, and the Center of Computer Science and Free Software (C3SL) in Brazil, demonstrated that our approach is reproducible and promising to allow the virtual evaluation of next-gen storage devices.

O Android é o sistema operacional mais utilizado por dispositivos móveis no mundo. Esse fato tem atraído cada vez mais desenvolvedores para a plataforma devido a sua característica opensource e desenvolvimento gratuito de aplicativos. Um problema que surgiu a partir disso são os aplicativos maliciosos, que visam prejudicar o usuário final e que muitas vezes são difíceis de identificar, o que tem levado autores a propor soluções para diferenciá-los dos benignos. Nesse sentido, neste trabalho será apresentado o DroiDiagnosis, uma solução que utiliza aprendizado de máquina e que classifica 80% das amostras entre benignas e maliciosas baseada em suas características dinâmicas e estáticas.

Network Neutrality states that all traffic in the Internet must be treated equally and thus cannot suffer unfair traffic differentiation (TD). Several solutions for detecting the presence of TD in the Internet have been proposed. However, locating where in the network TD is happening is still an open problem. In this work, we propose a strategy to locate Autonomous Systems (ASes) that are differentiating traffic. The proposed strategy takes advantage of AS-level routing properties to identify valid AS-level paths between end-hosts. It is then possible to select measurement points between which the AS-level paths traverse suspect ASes. Probes are sent from the measurement points and processed using end-to-end TD detectors based on statistical inference. The main idea is to check suspect ASes until only the AS that is actually discriminating traffic is filtered out. We first present results of experiments executed to validate the routing properties employed. Then the efficiency of the proposal for locating TD is evaluated using simulation. The results show that the proposed strategy is effective and efficient.

A neutralidade da rede preconiza uma rede sem discriminação de tráfego, independente de origem, destino e ou conteúdo. Apesar de existirem diversas soluções para a detecção de diferenciação de tráfego (DT), há uma lacuna em termos da localização efetiva do ponto da rede onde a DT ocorre. Neste trabalho, propomos uma solução para a detecção e localização de DT, que tira proveito das propriedades do roteamento na Internet entre Sistemas Autônomos. A estratégia explora diferenças e semelhanças dos caminhos entre os pontos de medição. As premissas acerca do roteamento na Internet foram verificadas através de experimentos executados no PlanetLab. A acurácia e eficiência da proposta foi avaliada por meio de simulação.