Climate change poses a global threat to public health, food security, and economic stability. Addressing it requires evidence-based policies and a nuanced understanding of how the threat is perceived by the public, particularly within visual social media, where narratives quickly evolve through voices of individuals, politicians, NGOs, and institutions. This study investigates climate-related discourse on YouTube within the Brazilian context, a geopolitically significant nation in global environmental negotiations. Through three case studies, we examine (1) which psychological content traits most effectively drive audience engagement, (2) the extent to which these traits influence content popularity, and (3) whether such insights can inform the design of persuasive synthetic campaigns such as climate denialism using recent generative language models. Another contribution of this work is the release of a large publicly available dataset of 226K Brazilian YouTube videos and 2.7M user comments on climate change. The dataset includes fine-grained annotations of persuasive strategies, theory of mind categorizations in user responses, and typologies of content creators. This resource can help support future research on digital climate communication and the ethical risk of algorithmically amplified narratives and generative media.

The Exame Nacional do Ensino Médio (ENEM) is a pivotal test for Brazilian students, required for admission to a significant number of universities in Brazil. The test consists of four objective high-school level tests on Math, Humanities, Natural Sciences and Languages, and one writing essay. Students' answers to the test and to the accompanying socioeconomic status questionnaire are made public every year (albeit anonymized) due to transparency policies from the Brazilian Government. In the context of large language models (LLMs), these data lend themselves nicely to comparing different groups of humans with AI, as we can have access to human and machine answer distributions. We leverage these characteristics of the ENEM dataset and compare GPT-3.5 and 4, and MariTalk, a model trained using Portuguese data, to humans, aiming to ascertain how their answers relate to real societal groups and what that may reveal about the model biases. We divide the human groups by using socioeconomic status (SES), and compare their answer distribution with LLMs for each question and for the essay. We find no significant biases when comparing LLM performance to humans on the multiple-choice Brazilian Portuguese tests, as the distance between model and human answers is mostly determined by the human accuracy. A similar conclusion is found by looking at the generated text as, when analyzing the essays, we observe that human and LLM essays differ in a few key factors, one being the choice of words where model essays were easily separable from human ones. The texts also differ syntactically, with LLM generated essays exhibiting, on average, smaller sentences and less thought units, among other differences. These results suggest that, for Brazilian Portuguese in the ENEM context, LLM outputs represent no group of humans, being significantly different from the answers from Brazilian students across all tests. The appendices may be found at https://arxiv.org/abs/2408.05035.

Politicization is a social phenomenon studied by political science characterized by the extent to which ideas and facts are given a political tone. A range of topics, such as climate change, religion and vaccines has been subject to increasing politicization in the media and social media platforms. In this work, we propose a computational method for assessing politicization in online conversations based on topic shifts, i.e., the degree to which people switch topics in online conversations. The intuition is that topic shifts from a non-political topic to politics are a direct measure of politicization – making something political, and that the more people switch conversations to politics, the more they perceive politics as playing a vital role in their daily lives. A fundamental challenge that must be addressed when one studies politicization in social media is that, a priori, any topic may be politicized. Hence, any keyword-based method or even machine learning approaches that rely on topic labels to classify topics are expensive to run and potentially ineffective. Instead, we learn from a seed of political keywords and use Positive-Unlabeled (PU) Learning to detect political comments in reaction to non-political news articles posted on Twitter, YouTube, and TikTok during the 2022 Brazilian presidential elections. Our findings indicate that all platforms show evidence of politicization as discussion around topics adjacent to politics such as economy, crime and drugs tend to shift to politics. Even the least politicized topics had the rate in which their topics shift to politics increased in the lead up to the elections and after other political events in Brazil – an evidence of politicization. The code is available at https://github.com/marceloslo/Topic-Shifts-as-a-Proxy-for-Assessing-Politicization-in-Social-Media.

The use of intelligent voice assistants is enabling people who previously could not easily interact with a graphical interface to have access to digital services. However recent works have shown that voice assistants fail to attend certain types of user’s profile. Our research focus on the interaction to an IVA with people with illiterate people. An investigation was made into the use of Google Assistant by literate and illiterate people. In order to verify if the assistant can understand the commands spoken to it, an experiment was conducted with 45 people. The experiment indicates that two characteristics are essential to be improved in the IVA while interacting to illiterates: the ability to understand a more diverse and specific vocabulary of this audience, and an ability to understand the grammatical structure of sentences produced in an ad hoc manner by them. As a suggestion to minimize the problem, we think that AVI should be designed to include illiterate people, specially in developing economy countries, otherwise those assistants will be a factor to increase digital exclusion for poor populations.

As the Internet grows in number of users and in the diversity of services, it becomes more influential on peoples lives. It has the potential of constructing or modifying the opinion, the mental perception, and the values of individuals. What is being created and published online is a reflection of people’s values and beliefs. As a global platform, the Internet is a great source of information for researching the online culture of many different countries. In this work we develop a methodology for measuring data from textual online sources using word embedding models, to create a country-based online human values index that captures cultural traits and values worldwide. Our methodology is applied with a dataset of 1.7 billion tweets, and then we identify their location among 59 countries. We create a list of 22 Online Values Inquiries (OVI) , each one capturing different questions from the World Values Survey, related to several values such as religion, science, and abortion. We observe that our methodology is indeed capable of capturing human values online for different counties and different topics. We also show that some online values are highly correlated (up to c = 0.69, p < 0.05) with the corresponding offline values, especially religion-related ones. Our method is generic, and we believe it is useful for social sciences specialists, such as demographers and sociologists, that can use their domain knowledge and expertise to create their own Online Values Inquiries, allowing them to analyze human values in the online environment.

Attention is a scarce resource disputed by algorithms and people on the Internet. This competition for attention is part of online spaces especially online small groups where there is a limited number of individuals interacting with each other using text and media content that is not controlled by algorithms or human curators. In these groups, as certain participants and piece of content can catch the collective attention, a question that naturally arises is: how to analyze topic attention in online small groups? In this paper, we propose a methodology aimed at answering this question. Our proposal consists of sets of analyses over topical (obtained from topic analysis) transition graphs for characterizing attention allocation, permanence and shifting as well as participant role characterization during discussions in online small groups. We experimented with our methodology using WhatsApp groups as a case study. Among other results, we identified and characterized abrupt and smooth topic transitions as well as patterns of participant activity related to certain topics.

Discrimination-aware models in machine learning are a recent topic of study that aim to minimize the adverse impact of machine learning decisions for certain groups of people due to ethical and legal implications. We propose a benchmark framework for assessing discrimination-aware models. Our framework consists of systematically generated biased datasets that are similar to real world data, created by a Bayesian network approach. Experimental results show that we can assess the quality of techniques through known metrics of discrimination, and our flexible framework can be extended to most real datasets and fairness measures to support a diversity of assessments.

With the recent accelerated increase in the amount of social data available in the Internet, several big data distributed processing frameworks have been proposed and implemented. Hadoop has been used widely to process all kinds of data, not only from social media. Spark is gaining popularity for offering a more flexible, object-functional, programming interface, and also by improving performance in many cases. However, not all data analysis algorithms perform well on Hadoop or Spark. For instance, graph algorithms tend to generate large amounts of messages between processing elements, which may result in poor performance even in Spark. We introduce Faster, a low latency distributed processing framework, designed to explore data locality to reduce processing costs in such algorithms. It offers an API similar to Spark, but with a slightly different execution model and new operators. Our results show that it can significantly outperform Spark on large graphs, being up to one orders of magnitude faster when running PageRank in a partial Google+ friendship graph with more than one billion edges.

Abstract Due to the recent “Right to be Forgotten” (RTBF) ruling, for queries about an individual, Google and other search engines now delist links to web pages that contain “inadequate, irrelevant or no longer relevant, or excessive” information about that individual. In this paper we take a data-driven approach to study the RTBF in the traditional media outlets, its consequences, and its susceptibility to inference attacks. First, we do a content analysis on 283 known delisted UK media pages, using both manual investigation and Latent Dirichlet Allocation (LDA). We find that the strongest topic themes are violent crime, road accidents, drugs, murder, prostitution, financial misconduct, and sexual assault. Informed by this content analysis, we then show how a third party can discover delisted URLs along with the requesters’ names, thereby putting the efficacy of the RTBF for delisted media links in question. As a proof of concept, we perform an experiment that discovers two previously-unknown delisted URLs and their corresponding requesters. We also determine 80 requesters for the 283 known delisted media pages, and examine whether they suffer from the “Streisand effect,” a phenomenon whereby an attempt to hide a piece of information has the unintended consequence of publicizing the information more widely. To measure the presence (or lack of presence) of a Streisand effect, we develop novel metrics and methodology based on Google Trends and Twitter data. Finally, we carry out a demographic analysis of the 80 known requesters. We hope the results and observations in this paper can inform lawmakers as they refine RTBF laws in the future.

Planners and social psychologists have suggested that the recognizability of the urban environment is linked to people's socio-economic well-being. We build a web game that puts the recognizability of London's streets to the test. It follows as closely as possible one experiment done by Stanley Milgram in 1972. The game picks up random locations from Google Street View and tests users to see if they can judge the location in terms of closest subway station, borough, or region. Each participant dedicates only few minutes to the task (as opposed to 90 minutes in Milgram's). We collect data from 2,255 participants (one order of magnitude a larger sample) and build a recognizability map of London based on their responses. We find that some boroughs have little cognitive representation; that recognizability of an area is explained partly by its exposure to Flickr and Foursquare users and mostly by its exposure to subway passengers; and that areas with low recognizability do not fare any worse on the economic indicators of income, education, and employment, but they do significantly suffer from social problems of housing deprivation, poor living conditions, and crime. These results could not have been produced without analyzing life off- and online: that is, without considering the interactions between urban places in the physical world and their virtual presence on platforms such as Flickr and Foursquare. This line of work is at the crossroad of two emerging themes in computing research - a crossroad where "web science" meets the "smart city" agenda.

With the growing popularity and usage of online social media services, people now have accounts (some times several) on multiple and diverse services like Facebook, Linked In, Twitter and You Tube. Publicly available information can be used to create a digital footprint of any user using these social media services. Generating such digital footprints can be very useful for personalization, profile management, detecting malicious behavior of users. A very important application of analyzing users' online digital footprints is to protect users from potential privacy and security risks arising from the huge publicly available user information. We extracted information about user identities on different social networks through Social Graph API, Friend Feed, and Profilactic, we collated our own dataset to create the digital footprints of the users. We used username, display name, description, location, profile image, and number of connections to generate the digital footprints of the user. We applied context specific techniques (e.g. Jaro Winkler similarity, Word net based ontologies) to measure the similarity of the user profiles on different social networks. We specifically focused on Twitter and Linked In. In this paper, we present the analysis and results from applying automated classifiers for disambiguating profiles belonging to the same user from different social networks. User ID and Name were found to be the most discriminative features for disambiguating user profiles. Using the most promising set of features and similarity metrics, we achieved accuracy, precision and recall of 98%, 99%, and 96%, respectively.

In the last few years, the increasing interest in location-based services (LBS) has favored the introduction of geo-referenced information in various Web 2.0 applications, as well as the rise of location-based social networks (LBSN). Foursquare, one of the most popular LBSNs, gives incentives to users who visit (check in) specific places (venues) by means of, for instance, mayorships to frequent visitors. Moreover, users may leave tips at specific venues as well as mark previous tips as done in sign of agreement. Unlike check ins, which are shared only with friends, the lists of mayorships, tips and dones of a user are publicly available to everyone, thus raising concerns about disclosure of the user's movement patterns and interests. We analyze how users explore these publicly available features, and their potential as sources of information leakage. Specifically, we characterize the use of mayorships, tips and dones in Foursquare based on a dataset with around 13 million users. We also analyze whether it is possible to easily infer the home city (state and country) of a user from these publicly available information. Our results indicate that one can easily infer the home city of around 78% of the analyzed users within 50 kilometers.

This paper presents a detailed analysis of the Google+ social network. We identify the key differences and similarities with other popular networks like Facebook and Twitter, in order to determine whether Google+ is a new paradigm or yet another social network. This work is based on large-scale crawls of over 27 million user profiles that represented nearly 50% of the entire network in 2011. We observe that the average path length between users is slightly higher than other networks, possibly because Google+ is a new system where relationships are still rapidly growing. Google+ shows a higher level of reciprocity than Twitter, which also has directed social links. The newly available "places lived" field could be used to study how users are distributed around the world and how aggressively the service has been adopted in different countries. We find that Google+ is popular in countries with relatively low Internet penetration rate. Based on the amount and types of information publicly shared in user profiles, we also find that the notion of privacy varies significantly across different cultures.

Influential people have an important role in the process of information diffusion. However, there are several ways to be influential, for example, to be the most popular or the first that adopts a new idea. In this paper we present a methodology to find trendsetters in information networks according to a specific topic of interest. Trendsetters are people that adopt and spread new ideas influencing other people before these ideas become popular. At the same time, not all early adopters are trendsetters because only few of them have the ability of propagating their ideas by their social contacts through word-of-mouth. Differently from other influence measures, a trendsetter is not necessarily popular or famous, but the one whose ideas spread over the graph successfully. Other metrics such as node in-degree or even standard Pagerank focus only in the static topology of the network. We propose a ranking strategy that focuses on the ability of some users to push new ideas that will be successful in the future. To that end, we combine temporal attributes of nodes and edges of the network with a Pagerank based algorithm to find the trendsetters for a given topic. To test our algorithm we conduct innovative experiments over a large Twitter dataset. We show that nodes with high in-degree tend to arrive late for new trends, while users in the top of our ranking tend to be early adopters that also influence their social contacts to adopt the new trend.

Online Location Based Social Networks (LBSNs), which combine social network features with geographic information sharing, are becoming increasingly popular. One such application is Foursquare, which doubled its user population in less than six months. Among other features, Foursquare allows users to leave tips (i.e., reviews or recommendations) at specific venues as well as to give feedback on previously posted tips by adding them to their to-do lists or marking them as done. In this paper, we analyze how Foursquare users exploit these three features - tips, dones and to-dos - uncovering different behavior profiles. Our study reveals the existence of very active and influential users, some of which are famous businesses and brands, that seem engaged in posting tips at a large variety of venues while also receiving a great amount of user feedback on them. We also provide evidence of spamming, showing the existence of users that post tips whose contents are unrelated to the nature or domain of the venue where the tips were left.

The multi-million sports-betting market is based on the fact that the task of predicting the outcome of a sports event is very hard. Even with the aid of an uncountable number of descriptive statistics and background information, only a few can correctly guess the outcome of a game or a league. In this work, our approach is to move away from the traditional way of predicting sports events, and instead to model sports leagues as networks of players and teams where the only information available is the work relationships among them. We propose two network-based models to predict the behavior of teams in sports leagues. These models are parameter-free, that is, they do not have a single parameter, and moreover are sport-agnostic: they can be applied directly to any team sports league. First, we view a sports league as a network in evolution, and we infer the implicit feedback behind network changes and properties over the years. Then, we use this knowledge to construct the network-based prediction models, which can, with a significantly high probability, indicate how well a team will perform over a season. We compare our proposed models with other prediction models in two of the most popular sports leagues: the National Basketball Association (NBA) and the Major League Baseball (MLB). Our model shows consistently good results in comparison with the other models and, relying upon the network properties of the teams, we achieved a ≈ 14% rank prediction accuracy improvement over our best competitor.

A number of online video sharing systems, out of which YouTube is the most popular, provide features that allow users to post a video as a response to a discussion topic. These features open opportunities for users to introduce polluted content, or simply pollution, into the system. For instance, spammers may post an unrelated video as response to a popular one, aiming at increasing the likelihood of the response being viewed by a larger number of users. Moreover, content promoters may try to gain visibility to a specific video by posting a large number of (potentially unrelated) responses to boost the rank of the responded video, making it appear in the top lists maintained by the system. Content pollution may jeopardize the trust of users on the system, thus compromising its success in promoting social interactions. In spite of that, the available literature is very limited in providing a deep understanding of this problem. In this paper, we address the issue of detecting video spammers and promoters. Towards that end, we first manually build a test collection of real YouTube users, classifying them as spammers, promoters, and legitimate users. Using our test collection, we provide a characterization of content, individual, and social attributes that help distinguish each user class. We then investigate the feasibility of using supervised classification algorithms to automatically detect spammers and promoters, and assess their effectiveness in our test collection. While our classification approach succeeds at separating spammers and promoters from legitimate users, the high cost of manually labeling vast amounts of examples compromises its full potential in realistic scenarios. For this reason, we further propose an active learning approach that automatically chooses a set of examples to label, which is likely to provide the highest amount of information, drastically reducing the amount of required training data while maintaining comparable classification effectiveness.

Traditionally, users have discovered information on the Web by browsing or searching. Recently, word-of-mouth has emerged as a popular way of discovering the Web, particularly on social networking sites like Facebook and Twitter. On these sites, users discover Web content by following URLs posted by their friends. Such word-of-mouth based content discovery has become a major driver of traffic to many Web sites today. To better understand this popular phenomenon, in this paper we present a detailed analysis of word-of-mouth exchange of URLs among Twitter users. Among our key findings, we show that Twitter yields propagation trees that are wider than they are deep. Our analysis on the geolocation of users indicates that users who are geographically close together are more likely to share the same URL.

Real-time interaction, which enables live discussions, has become a key feature of most Web applications. In such an environment, the ability to automatically analyze user opinions and sentiments as discussions develop is a powerful resource known as real time sentiment analysis. However, this task comes with several challenges, including the need to deal with highly dynamic textual content that is characterized by changes in vocabulary and its subjective meaning and the lack of labeled data needed to support supervised classifiers. In this paper, we propose a transfer learning strategy to perform real time sentiment analysis. We identify a task - opinion holder bias prediction - which is strongly related to the sentiment analysis task; however, in constrast to sentiment analysis, it builds accurate models since the underlying relational data follows a stationary distribution.

MapReduce is an important paradigm to support modern data-intensive applications. In this paper we address the challenge of modeling performance of one implementation of MapReduce called Hadoop Online Prototype (HOP), with a specific target on the intra-job pipeline parallelism. We use a hierarchical model that combines a precedence model and a queuing network model to capture the intra-job synchronization constraints. We first show how to build a precedence graph that represents the dependencies among multiple tasks of the same job. We then apply it jointly with an approximate Mean Value Analysis (aMVA) solution to predict mean job response time and resource utilization. We validate our solution against a queuing network simulator in various scenarios, finding that our performance model presents a close agreement, with maximum relative difference under 15%.

Understanding how users behave when they connect to social networking sites creates opportunities for better interface design, richer studies of social interactions, and improved design of content distribution systems. In this paper, we present a first of a kind analysis of user workloads in online social networks. Our study is based on detailed clickstream data, collected over a 12-day period, summarizing HTTP sessions of 37,024 users who accessed four popular social networks: Orkut, MySpace, Hi5, and LinkedIn. The data were collected from a social network aggregator website in Brazil, which enables users to connect to multiple social networks with a single authentication. Our analysis of the clickstream data reveals key features of the social network workloads, such as how frequently people connect to social networks and for how long, as well as the types and sequences of activities that users conduct on these sites. Additionally, we crawled the social network topology of Orkut, so that we could analyze user interaction data in light of the social graph. Our data analysis suggests insights into how users interact with friends in Orkut, such as how frequently users visit their friends' or non-immediate friends' pages. In summary, our analysis demonstrates the power of using clickstream data in identifying patterns in social network workloads and social interactions. Our analysis shows that browsing, which cannot be inferred from crawling publicly available data, accounts for 92% of all user activities. Consequently, compared to using only crawled data, considering silent interactions like browsing friends' pages increases the measured level of interaction among users.

A number of online video social networks, out of which YouTube is the most popular, provides features that allow users to post a video as a response to a discussion topic. These features open opportunities for users to introduce polluted content, or simply pollution, into the system. For instance, spammers may post an unrelated video as response to a popular one aiming at increasing the likelihood of the response being viewed by a larger number of users. Moreover, opportunistic users--promoters--may try to gain visibility to a specific video by posting a large number of (potentially unrelated) responses to boost the rank of the responded video, making it appear in the top lists maintained by the system. Content pollution may jeopardize the trust of users on the system, thus compromising its success in promoting social interactions. In spite of that, the available literature is very limited in providing a deep understanding of this problem.

We analyze knowledge production in Computer Science by means of coauthorship networks. For this, we consider 30 graduate programs of different regions of the world, being 8 programs in Brazil, 16 in North America (3 in Canada and 13 in the United States), and 6 in Europe (2 in France, 1 in Switzerland and 3 in the United Kingdom). We use a dataset that consists of 176,537 authors and 352,766 publication entries distributed among 2,176 publication venues. The results obtained for different metrics of collaboration social networks indicate the process of knowledge creation has changed differently for each region. Research is increasingly done in teams across different fields of Computer Science. The size of the giant component indicates the existence of isolated collaboration groups in the European network, contrasting to the degree of connectivity found in the Brazilian and North-American counterparts. We also analyzed the temporal evolution of the social networks representing the three regions. The number of authors per paper experienced an increase in a time span of 12 years. We observe that the number of collaborations between authors grows faster than the number of authors, benefiting from the existing network structure. The temporal evolution shows differences between well-established fields, such as Databases and Computer Architecture, and emerging fields, like Bioinformatics and Geoinformatics. The patterns of collaboration analyzed in this paper contribute to an overall understanding of Computer Science research in different geographical regions that could not be achieved without the use of complex networks and a large publication database.

E-commerce is growing at an exponential rate. In the last decade, there has been an explosion of online commercial activity enabled by World Wide Web (WWW). These days, many consumers are less attracted to online auctions, preferring to buy merchandise quickly using fixed-price negotiations. Sales at Amazon.com, the leader in online sales of fixed-price goods, rose 37% in the first quarter of 2008. At eBay, where auctions make up 58% of the site's sales, revenue rose 14%. In Brazil, probably by cultural influence, online auctions are not been popular. This work presents a characterization and analysis of fixed-price online negotiations. Using actual data from a Brazilian marketplace, we analyze seller practices, considering seller profiles and strategies. We show that different sellers adopt strategies according to their interests, abilities and experience. Moreover, we confirm that choosing a selling strategy is not simple, since it is important to consider the seller's characteristics to evaluate the applicability of a strategy. The work also provides a comparative analysis of some selling practices in Brazil with popular worldwide marketplaces.

This article characterizes video-based interactions that emerge from YouTube's video response feature, which allows users to discuss themes and to provide reviews for products or places using much richer media than text. Based on crawled data covering a representative subset of videos and users, we present a characterization from two perspectives: the video response view and the interaction network view. In addition to providing valuable statistical models for various characteristics, our study uncovers typical user behavioral patterns in video-based environments and shows evidence of opportunistic behavior.

The United States National Basketball Association (NBA) is one of the most popular sports league in the world and is well known for moving a millionary betting market that uses the countless statistical data generated after each game to feed the wagers. This leads to the existence of a rich historical database that motivates us to discover implicit knowledge in it. In this paper, we use complex network statistics to analyze the NBA database in order to create models to represent the behavior of teams in the NBA. Results of complex network-based models are compared with box score statistics, such as points, rebounds and assists per game. We show the box score statistics play a significant role for only a small fraction of the players in the league. We then propose new models for predicting a team success based on complex network metrics, such as clustering coefficient and node degree. Complex network-based models present good results when compared to box score statistics, which underscore the importance of capturing network relationships in a community such as the NBA.

This paper seeks understanding the user behavior in a social network created essentially by video interactions. We present a characterization of a social network created by the video interactions among users on YouTube, a popular social networking video sharing system. Our results uncover typical user behavioral patterns as well as show evidences of anti-social behavior such as self-promotion and other types of content pollution.

Capacity management of a hosting infrastructure has traditionally focused only on performance goals. However, the quality of service provided to the hosted applications, and ultimately the revenues achieved by the provider, depend also on other aspects, such as security and energy constraints. This paper extends our self-adaptive SLA-driven capacity management solution to capture, in an unified framework, key performance and cost tradeoffs that arise when operating under security attacks and energy constraints. A number of scenarios and strategies based on dynamic SLA contracts are designed to help uncover, via simulation experiments, the main tradeoffs, considering both the provider’s interests (i.e., revenues) and the customer’s interests (i.e., legitimate throughput, response time distribution and costs). Finally, we also assess the cost-effectiveness of our framework under highly variable application service times.

This paper addresses the problem of hosting multiple applications on a provider's virtualized multi-tier infrastructure. Building from a previous model, we design a new self-adaptive capacity management framework, which combines a two-level SLA-driven pricing model, an optimization model and an analytical queuing-based performance model to maximize the provider's business objective. Our main contributions are the more accurate multi-queue performance model, which captures application specific bottlenecks and the parallelism inherent to multi-tier platforms, as well as the solution of the extended and much more complex optimization model. Our approach is evaluated via simulation with synthetic as well as realistic workloads, in various scenarios. The results show that our solution is significantly more cost-effective, in terms of the provider's achieved revenues, than the approach it is built upon, which uses a single-resource performance model. It also significantly outperforms a multi-tier static allocation strategy for heavy and unbalanced workloads. Finally, preliminary experiments assess the applicability of our framework to virtualized environments subjected to capacity variations caused by the processing of management and security-related tasks.

Broadband Internet pricing scheme should charge users based on their consumption pattern. It could avoid resource wasting because light users will not have to subsidize heavy users. Furthermore, a fair pricing scheme could save bandwidth during hours of day, which would improve broadband carriers capacity management and planning efforts. This paper presents a fair broadband pricing scheme where users' adaptive applications are configured to adjust their demand in accordance with several parameters, such as user's budget, estimated future needs, and usage price defined and published by the service provider. We simulate our pricing scheme and three others schemes using real data from a broadband Internet Service Provider (ISP) in order to analyze bandwidth saving and utility obtained by users. We show that in the proposed pricing scheme 15% of users have positive payoff, 1% have negative, and for the rest of the users the payoff is zero. We also present the re-distribution of the workload curves along the time-of-day and show that bandwidth saving happens in almost 60% of all hours of the day. We compare our pricing scheme with others reported in the literature and the simulation results show the potential of our proposal.

Pricing could be used to foster fair use of Internet resources and help to control backbone congestion. This paper presents a mechanism for pricing residential broadband Internet services based on the following information: user subscription, ISP backbone usage historic data, and estimated future consumption of each user. Our proposal combines concepts from flat rate pricing, usage based pricing, and time based pricing models. This is an ongoing work and we are currently working a simulation in order to evaluate the results obtained with the proposed pricing scheme.

This work considers the problem of hosting multiple third-party Internet services in a cost-effective manner so as to maximize a provider's business objective. For this purpose, we present a dynamic capacity management framework based on an optimization model, which links a cost model based on SLA contracts with an analytical queuing-based performance model, in an attempt to adapt the platform to changing capacity needs in real time. In addition, we propose a two-level SLA specification for different operation modes, namely, normal and surge, which allows for per-use service accounting with respect to requirements of throughput and tail distribution response time. The cost model proposed is based on penalties, incurred by the provider due to SLA violation, and rewards, received when the service level expectations are exceeded. Finally, we evaluate approximations for predicting the performance of the hosted services under two different scheduling disciplines, namely FCFS and processor sharing. Through simulation, we assess the effectiveness of the proposed approach as well as the level of accuracy resulting from the performance model approximations.

The rapid increase in the volume of unsolicited commercial e-mails, also known as spam, is beginning to take its toll in system administrators, business corporations and end-users. Widely varying estimates of the cost associated with spam are available in the literature. However, a quantitative analysis of the determinant characteristics of spam traffic is still an open problem. This work fills this gap and presents what we believe to be the first extensive characterization of a spam traffic.

This work presents an architecture that allows users to enhance their privacy control over the computational environment. Web privacy is a topic that is raising, nowadays, many discussions. Usually, people do not know how their privacy can be violated or what can be done to protect it. Among the generated conflicts, we would like to show up the one that happens between privacy and personalization: by one side, users appreciate the idea of receiving personalized services and do not approve the collection, tracing and analysis of their actions; by the other side, personalization services need this type of information in order to profile their users. The architecture presented in this article helps users to understand better how their privacy can be invaded and, at the same time, gives them a better control of their privacy, through anonymity, without preventing them from receiving personalized services.

Current search technologies work in "one size fits all" fashion. Therefore, the answer to a query is independent of specific user information need. In this paper, we describe a novel ranking technique for personalized search services that combines content-based and community-based evidences. The community-based information is used in order to provide context for queries and is influenced by the current interaction of the user with the service. Our algorithm is evaluated using data derived from an actual service available on the Web, an online bookstore. We show that the quality of content-based ranking strategies can be improved by the use of community information as another evidential source of relevance. In our experiments, the improvements reach up to 48% in terms of average precision.

There has been considerable work done in the study of Web reference streams: sequences of requests for Web objects. In particular, many studies have looked at the locality properties of such streams, because of the impact of locality on the design and performance of caching and prefetching systems. However, a general framework for understanding why reference streams exhibit given locality properties has not yet emerged. In this paper we take a first step in this direction. We propose a framework for describing how reference streams are transformed as they pass through the Internet, based on three operations: aggregation, disaggregation, and filtering. We also propose metrics to capture the temporal locality of reference streams in this framework. We argue that these metrics (marginal entropy and interreference coefficient of variation) are more natural and more useful than previously proposed metrics for temporal locality; and we show that these metrics provide insight into the nature of reference stream transformations in the Web.

Abstract—We present a thorough characterization of what we believe to be the first significant live Internet streaming media workload in the scientific literature. Our characterization of over 3.5 million requests spanning a 28-day period is done at three increasingly granular levels, corresponding to clients, sessions, and transfers. Our findings support two important conclusions. First, we show that the nature of interactions between users and objects is fundamentally different for live versus stored objects. Access to stored objects is user driven, whereas access to live objects is object driven. This reversal of active/passive roles of users and objects leads to interesting dualities. For instance, our analysis underscores a Zipf-like profile for user interest in a given object, which is in contrast to the classic Zipf-like popularity of objects for a given user. Also, our analysis reveals that transfer lengths are highly variable and that this variability is due to client stickiness to a particular live object, as opposed to structural (size) properties of objects. Second, by contrasting two live streaming workloads from two radically different applications, we conjecture that some characteristics of live media access workloads are likely to be highly dependent on the nature of the live content being accessed. This dependence is clear from the strong temporal correlation observed in the traces, which we attribute to the impact of synchronous access to live content. Based on our analysis, we present a model for live media workload generation that incorporates many of our findings, and which we implement in GISMO. Index Terms—Internet, live streaming, measurement, multimedia, workload characterization. I.

E-commerce services have become a promising and profitable application of the Internet. In order to keep them growing, solutions must be found to deal with unreliable connections and high latencies, among other problems. The best solutions to such problems tend to depend on the distribution of the service over the network, placing servers in multiple locations, closer to customers. If placement of servers is effective it tends to reduce delays and traffic-related costs. In this paper we discuss the distribution of e-commerce services by introducing a traffic-aware cost model and evaluating it using an actual log from an e-tailer. The results show that the model yields good placement solutions, which perform better than simpler ad-hoc solutions.

Understanding the nature and characteristics of e-business workloads is a crucial step to improve the quality of service offered to customers in electronic business environments.However, the variety and complexity of the interactions between customers and sites make the characterization of ebusiness workloads a challenging problem.Using a multilayer hierarchical model, this paper presents a detailed characterization of the workload of two actual e-business sites: an online bookstore and an electronic auction site.Through the characterization process, we found the presence of autonomous agents, or robots, in the workload and used the hierarchical structure to determine their characteristics.We also found that search terms follow a Zipf distribution.

Article Free Access Share on A methodology for workload characterization of E-commerce sites Authors: Daniel A. Menascé Dept. of Computer Science, George Mason University, Fairfax, VA Dept. of Computer Science, George Mason University, Fairfax, VAView Profile , Virgilio A. F. Almeida Dept. of Computer Science, Univ. Federal de Minas Gerais, Belo Horizonte, MG 30161, Brazil Dept. of Computer Science, Univ. Federal de Minas Gerais, Belo Horizonte, MG 30161, BrazilView Profile , Rodrigo Fonseca Dept. of Computer Science, Univ. Federal de Minas Gerais, Belo Horizonte, MG 30161, Brazil Dept. of Computer Science, Univ. Federal de Minas Gerais, Belo Horizonte, MG 30161, BrazilView Profile , Marco A. Mendes Dept. of Computer Science, Univ. Federal de Minas Gerais, Belo Horizonte, MG 30161, Brazil Dept. of Computer Science, Univ. Federal de Minas Gerais, Belo Horizonte, MG 30161, BrazilView Profile Authors Info & Claims EC '99: Proceedings of the 1st ACM conference on Electronic commerceNovember 1999 Pages 119–128https://doi.org/10.1145/336992.337024Published:01 November 1999Publication History 176citation2,293DownloadsMetricsTotal Citations176Total Downloads2,293Last 12 Months162Last 6 weeks14 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

The authors consider multiprogrammed multiprocessors and parallel programs modeled as random task graphs. A theoretical analytical model for studying combinations of extreme cases of workload parallelism (highly parallel versus highly sequential) and of system utilization (light versus heavy load) is presented. A simulation model was used to study intermediate cases. From these two models, conditions under which heterogeneity presents a significant performance improvement over homogeneous architectures are derived. A study of the effect of scheduling policies for heterogeneous architectures on workloads of different degrees of parallelism under various system load conditions is presented.>

The cost-performance of heterogeneity in supercomputer architectures is analyzed. Queuing models are used to study the performance of homogeneous and heterogeneous supercomputer models. Grosch's law, which states that computer performance increases as the square of its cost, is used to analyze cost aspects of the models. It is concluded that heterogeneity in supercomputer architectures is a quite promising design approach that deserves further investigation.>