Browsing by Author "Ferreira, Carlos Henrique Gomes"

Now showing 1 - 7 of 7
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    A cloud computing price model based on virtual machine performance degradation.
    (2019) Leite, Dionisio Machado; Peixoto, Maycon Leone Maciel; Ferreira, Carlos Henrique Gomes; Batista, Bruno Guazzelli; Segura, Danilo Costa Marim; Santana, Marcos José; Santana, Regina Helena Carlucci
    This paper reports the interference effects in virtual machines performance running higher workloads to improve the resources payment in cloud computing. The objective is to produce an acceptable pay-as-you-go model to be used by cloud computing providers. Presently, a price of pay-as-you-go model is based on the virtual machine utilised per time. However, this scheme does not consider the interference caused by virtual machines running concurrently, which may cause performance degradation. In order to obtain a fair charging model, this paper proposes an approach considering a recovery over the initial price considering the virtual machine performance interference. Results showed benefits of a fair pay-as-you-go model, ensuring the effective user requirement. This novel model contributes to cloud computing in a fair and transparent price composition.
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    A hierarchical network-oriented analysis of user participation in misinformation spread on WhatsApp.
    (2022) Nobre, Gabriel Peres; Ferreira, Carlos Henrique Gomes; Almeida, Jussara Mendes de
    WhatsApp emerged as a major communication platform in many countries in the recent years. Despite offering only one-to-one and small group conversations, WhatsApp has been shown to enable the formation of a rich underlying network, crossing the boundaries of existing groups, and with structural properties that favor information dissemination at large. Indeed, WhatsApp has reportedly been used as a forum of misinformation campaigns with significant social, political and economic consequences in several countries. In this article, we aim at complementing recent studies on misinformation spread on WhatsApp, mostly focused on content properties and propagation dynamics, by looking into the network that connects users sharing the same piece of content. Specifically, we present a hierarchical network-oriented characterization of the users engaged in misinformation spread by focusing on three perspectives: individuals, WhatsApp groups and user communities, i.e., groupings of users who, intentionally or not, share the same content disproportionately often. By analyzing sharing and network topological properties, our study offers valuable insights into how WhatsApp users leverage the underlying network connecting different groups to gain large reach in the spread of misinformation in the platform.
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    Modeling dynamic ideological behavior in political networks.
    (2019) Ferreira, Carlos Henrique Gomes; Ferreira, Fabrício Murai; Souza, Breno Matos de; Almeida, Jussara Marques de
    In this article, we model and analyze the dynamic behavior of political networks, both at the individual (party member) and ideological community levels. Our study relies on public data covering 15 years of voting sessions of the House of Representatives of two diverse party system, namely, Brazil and the United States. While the former is an example of a highly fragmented party system, the latter illustrates the case of a highly polarized and non-fragmented system. We characterize the ideological communities, their member polarization and how such communities evolve over time. Also, we propose a temporal-ideological space model, based on temporal vertex embeddings, which allows us to assess the individual changes in ideological behavior over time, as expressed by the party members’ voting patterns. Our results unveil very distinct patterns across the two case studies, both in terms of structural and dynamic properties.
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    On network backbone extraction for modeling online collective behavior.
    (2022) Ferreira, Carlos Henrique Gomes; Ferreira, Fabrício Murai; Silva, Ana Paula Couto da; Trevisan, Martino; Vassio, Luca; Drago, Idilio; Mellia, Marco; Almeida, Jussara Marques de
    Collective user behavior in social media applications often drives several important online and offline phenomena linked to the spread of opinions and information. Several studies have focused on the analysis of such phenomena using networks to model user interactions, represented by edges. However, only a fraction of edges contribute to the actual investigation. Even worse, the often large number of non-relevant edges may obfuscate the salient interactions, blurring the underlying structures and user communities that capture the collective behavior patterns driving the target phenomenon. To solve this issue, researchers have proposed several network backbone extraction techniques to obtain a reduced and representative version of the network that better explains the phenomenon of interest. Each technique has its specific assumptions and procedure to extract the backbone. However, the literature lacks a clear methodology to highlight such assumptions, discuss how they affect the choice of a method and offer validation strategies in scenarios where no ground truth exists. In this work, we fill this gap by proposing a principled methodology for comparing and selecting the most appropriate backbone extraction method given a phenomenon of interest. We characterize ten state-of-the-art techniques in terms of their assumptions, requirements, and other aspects that one must consider to apply them in practice. We present four steps to apply, evaluate and select the best method(s) to a given target phenomenon. We validate our approach using two case studies with different requirements: online discussions on Instagram and coordinated behavior in WhatsApp groups. We show that each method can produce very different backbones, underlying that the choice of an adequate method is of utmost importance to reveal valuable knowledge about the particular phenomenon under investigation.
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    On the dynamics of political discussions on Instagram : a network perspective.
    (2021) Ferreira, Carlos Henrique Gomes; Ferreira, Fabrício Murai; Silva, Ana Paula Couto da; Almeida, Jussara Marques de; Trevisan, Martino; Vassio, Luca; Mellia, Marco; Drago, Idilio
    Instagram has been increasingly used as a source of information especially among the youth. As a result, political figures now leverage the platform to spread opinions and political agenda. We here analyze online discussions on Instagram, notably in political topics, from a network perspective. Specifically, we investigate the emergence of communities of co-commenters, that is, groups of users who often interact by commenting on the same posts and may be driving the ongoing online discussions. In particular, we are interested in salient co-interactions, i.e., interactions of co-commenters that occur more often than expected by chance and under independent behavior. Unlike casual and accidental co-interactions which normally happen in large volumes, salient co-interactions are key elements driving the online discussions and, ultimately, the information dissemination. We base our study on the analysis of 10 weeks of data centered around major elections in Brazil and Italy, following both politicians and other celebrities. We extract and characterize the communities of co-commenters in terms of topological structure, properties of the discussions carried out by community members, and how some community properties, notably community membership and topics, evolve over time. We show that communities discussing political topics tend to be more engaged in the debate by writing longer comments, using more emojis, hashtags and negative words than in other subjects. Also, communities built around political discussions tend to be more dynamic, although top commenters remain active and preserve community membership over time. Moreover, we observe a great diversity in discussed topics over time: whereas some topics attract attention only momentarily, others, centered around more fundamental political discussions, remain consistently active over time.
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    Platoon grouping network offloading mechanism for VANETs.
    (2021) Kamoi, Roger Nobuyuki; Pereira Júnior, Lourenço Alves; Verri, Filipe Alves Neto; Marcondes, Cesar Augusto Cavalheiro; Ferreira, Carlos Henrique Gomes; Meneguette, Rodolfo Ipolito; Cunha, Adilson Marques da
    The growth in the number of connected vehicles in ad-hoc networks or VANETs enables the use of increasingly sophisticated services and applications. One emerging application in this scenario is Platooning, a stable stream of connected autonomous vehicles. This application improves safety, vehicle flow on the road, energy consumption, among other variables. However, vehicles using this technology need to share space with vehicles not connected or with other automation levels. Among the bottlenecks in scenarios like this, Platoons formation and splits stand out, as these maneuvers involve costs and risk. Therefore, our contribution is developing a new strategy for Platoons formation in a mixed universe of vehicles to bring some of these desired benefits. The results show that the proposed solution achieved an accuracy of 89% of the entire amount of platoons in the datasets. Our experiments demonstrate cases with collisions, and the proposed solution provides a solution to a more safe operation on roads. Finally, we conclude that our solution provides more determinism in the traffic conditions, as vehicles achieving stationary speeds with low variation.
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    Understanding mobility in networks : a node embedding approach.
    (2021) Barros, Matheus Fellipe do Carmo; Ferreira, Carlos Henrique Gomes; Santos, Bruno Pereira dos; Pereira Júnior, Lourenço Alves; Mellia, Marco; Almeida, Jussara Marques de
    Motivated by the growing number of mobile devices capable of connecting and exchanging messages, we propose a methodology aiming to model and analyze node mobility in networks. We note that many existing solutions in the literature rely on topological measurements calculated directly on the graph of node contacts, aiming to capture the notion of the node’s importance in terms of connectivity and obility patterns beneficial for prototyping, design, and deployment of mobile networks. However, each measure has its specificity and fails to generalize the node importance notions that ultimately change over time. Unlike previous approaches, our methodology is based on a node embedding method that models and unveils the nodes’ importance in mobility and connectivity patterns while preserving their spatial and temporal characteristics. We focus on a case study based on a trace of group meetings. The results show that our methodology provides a rich representation for extracting different mobility and connectivity patterns, which can be helpful for various applications and services in mobile networks.