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The Domain Name System (DNS) service is one of the pillars of the Internet. This service allows users to access websites on the Internet through easy-to-remember domain names rather than complex numeric IP addresses. DNS acts as a directory that translates the domain names into a corresponding IP address, allowing communication between computers on different networks. However, the concentration of DNS service providers on the Internet affects user security, privacy, and network accessibility. The reliance on a small number of large DNS providers can lead to (a) risks of data breaches and disruption of service in the event of failures and (b) concerns about the digital sovereignty of countries regarding DNS hosting. In this sense, this work approaches this issue of DNS concentration on the Internet by presenting a solution to measure DNS hosting centralization and digital sovereignty in countries. With the data obtained through these measurements, relevant questions are answered, such as which are the top-10 DNS providers, if there is DNS centralization, and how dependent countries are on such providers., Comment: 8 pages, 7 figures

There have been growing concerns about the concentration and centralization of Internet infrastructure. In this work, we scrutinize the hosting industry on the Internet by using active measurements, covering 19 Top-Level Domains (TLDs). We show how the market is heavily concentrated: 1/3 of the domains are hosted by only 5 hosting providers, all US-based companies. For the country-code TLDs (ccTLDs), however, hosting is primarily done by local, national hosting providers and not by the large American cloud and content providers. We show how shared languages (and borders) shape the hosting market -- German hosting companies have a notable presence in Austrian and Swiss markets, given they all share German as official language. While hosting concentration has been relatively high and stable over the past four years, we see that American hosting companies have been continuously increasing their presence in the market related to high traffic, popular domains within ccTLDs -- except for Russia, notably., Comment: to appear in IEEE/IFIP Network Operations and Management Symposium https://noms2022.ieee-noms.org/

The 2019 Coronavirus disease (COVID-19) pandemic, caused by a quick dissemination of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has had a deep impact worldwide, both in terms of the loss of human life and the economic and social disruption. The use of digital technologies has been seen as an important effort to combat the pandemic and one of such technologies is contact tracing applications. These applications were successfully employed to face other infectious diseases, thus they have been used during the current pandemic. However, the use of contact tracing poses several privacy concerns since it is necessary to store and process data which can lead to the user/device identification as well as location and behavior tracking. These concerns are even more relevant when considering nationwide implementations since they can lead to mass surveillance by authoritarian governments. Despite the restrictions imposed by data protection laws from several countries, there are still doubts on the preservation of the privacy of the users. In this article, we analyze the privacy features in national contact tracing COVID-19 applications considering their intrinsic characteristics. As a case study, we discuss in more depth the Brazilian COVID-19 application Coronav\'irus-SUS, since Brazil is one of the most impacted countries by the current pandemic. Finally, as we believe contact tracing will continue to be employed as part of the strategy for the current and potential future pandemics, we present key research challenges., Comment: 7 pages, 3 figures, to be translated to Portuguese and submitted to the Regional Computer Networking Conference

Critical networked services enable significant revenue for network operators and, in turn, are regulated by Service Level Agreements (SLAs). In order to ensure SLAs are being met, service levels need to be monitored. One technique for this involves active measurement mechanisms which employ measurement probes along the network to inject synthetic traffic and compute the network performance. However, these mechanisms are expensive in terms of resources consumption. Thus, these mechanisms usually can cover only a fraction of what could be measured, which can lead to SLA violations being missed. Besides that, the definition of this fraction is a practice done by human administrators, which does not scale well and does not adapt to highly dynamic networking patterns. In this article, we examine the potential benefits of using P2P technology to improve the detection of SLA Violations. We first describe the principles of a P2P-based steering of active measurement mechanisms. These principles are characterized by a high degree of decentralized decision making across a network using a self-organizing overlay. In a second step, we present measurement session activation strategies based on these principles. These strategies do not require human intervention, are adaptive to changes in network conditions, and independent of the underlying active measurement technology.

Anycast routing is an area of studies that has been attracting interest of several researchers in recent years. Most anycast studies conducted in the past relied on coarse measurement data, mainly due to the lack of infrastructure where it is possible to test and collect data at same time. In this paper we present Tangled, an anycast test environment where researchers can run experiments and better understand the impacts of their proposals on a global infrastructure connected to the Internet.

Recent advances in artificial intelligence (AI) offer an opportunity for the adoption of self-driving networks. However, network operators or home-network users still do not have the right tools to exploit these new advancements in AI, since they have to rely on low-level languages to specify network policies. Intent-based networking (IBN) allows operators to specify high-level policies that dictate how the network should behave without worrying how they are translated into configuration commands in the network devices. However, the existing research proposals for IBN fail to exploit the knowledge and feedback from the network operator to validate or improve the translation of intents. In this paper, we introduce a novel intent-refinement process that uses machine learning and feedback from the operator to translate the operator's utterances into network configurations. Our refinement process uses a sequence-to-sequence learning model to extract intents from natural language and the feedback from the operator to improve learning. The key insight of our process is an intermediate representation that resembles natural language that is suitable to collect feedback from the operator but is structured enough to facilitate precise translations. Our prototype interacts with a network operator using natural language and translates the operator input to the intermediate representation before translating to SDN rules. Our experimental results show that our process achieves a correlation coefficient squared (i.e., R-squared) of 0.99 for a dataset with 5000 entries and the operator feedback significantly improves the accuracy of our model., Comment: 9 pages, 5 figures, 3 listings, 1 grammar

Complex Dynamic Networks can be exploited in solving problems where traditional solutions may not be sufficient. The increasing complexity of computer networks imposes problems to the current network management solutions. In this context, network management is an example of a research area that could benefit from the use of CDNs. However, the consistency of state of management data among the elements that build management CDNs (management nodes) is an important challenge. Traditional mechanisms to maintain consistency of these states are supported by some centralization which wastes some desirable properties of CDNs (e.g., robustness). In contrast to these mechanisms, we propose a distributed, scalable and robust mechanism to maintain the consistency of state of management data in management CDNs. Our mechanism introduces multi-agent truth maintenance features and communication strategies based on dynamic process to provide consistency maintenance of state of management data. We developed a model of a management CDN on Peersim simulator to perform experiments. Besides, 2 case studies are presented. The result obtained supports our scalability and robustness claims., Comment: 25 pages