Serverless computing, also known as Function-as-a-Service (FaaS), triggers web applications in the form of function chains. It uses a central orchestrator to route all requests from end-users and internal functions. Such architecture simplifies application deployment for developers. However, the convenient centralized network architecture compromises the efficiency of function chain communications. Specifically, (i) a centralized API gateway assists in routing requests between functions. This indirect routing scheme raises invocation latency. (ii) The control flow for invoking functions and the data flow for passing function data packets are both forwarded by the API gateway. This results in the API gateway consuming a significant amount of resources. (iii) All data packets of internal function communications go through the same API gateway. This expands the additional attack surface in multi-tenant scenarios. In this paper, we propose DirectFaaS, a clean-slate network architecture to improve the function chain communication performance. By separating coupled control flow and data flow, DirectFaaS releases the API gateway from heavy traffic forwarding, reducing its resource consumption. For this goal, DirectFaaS exploits the network control capabilities of Software-Defined Networking (SDN) to establish direct data forwarding channels to accelerate function chain invocations. In addition, the data flow constrained by fine-grained network policies consolidates multi-tenant traffic security. We implement the DirectFaaS prototype on the popular OpenFaaS platform. Evaluations under real-world serverless applications show that DirectFaaS achieves a reduction in application execution time by up to 30.9% and CPU consumption by up to 30.1% compared to the current architecture.
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We confront two challenges in the management of a vast and diverse array of online web applications deployed on enterprise-grade auto-scaling infrastructure, primarily focused on ensuring Quality of Service (QoS) for large-scale applications and optimizing resource costs. Firstly, reacting to increased load with a response-based approach can temporarily degrade QoS because many web applications need a few minutes to warm up. Therefore, precise workload prediction is critical for predictive scaling. However, our analysis of real-world applications underscores the substantial challenges arising from the limited precision and robustness of existing single prediction algorithms in the context of predictive auto-scaling. Secondly, guaranteeing the QoS of online applications within a cost-effective structure is crucial, as it is inherently linked to corporate profitability. Nevertheless, our study shows that mainstream auto-scaling methods exhibit various limitations, either being unsuitable for online environments or inadequately ensuring QoS. To address these issues, we introduce PASS, a Predictive Auto-Scaling System tailored for large-scale online web applications in enterprise settings. Our highly robust and accurate prediction framework dynamically integrates and calibrates appropriate prediction algorithms based on the unique characteristics of each application to effectively manage workload diversity. We further establish a performance model derived from online historical logs, enhancing auto-scaling to ensure diverse QoS without adverse impacts on online applications. Additionally, we implement a reactive strategy grounded in queuing theory to promptly address QoS violations resulting from inaccurate predictions or unexpected events. Across a wide spectrum of applications and real-world workloads, PASS outperforms state-of-the-art methods, achieving higher workload prediction accuracy and a superior QoS guarantee rate with less resource cost.
The global deployment of the 5G network has led to a substantial increase in the deployment of edge servers to host web applications, catering to the growing demand for low service latency by edge web users. Yet, running edge servers 24/7 leads to enormous energy consumption and excessive carbon emissions. Energy-efficient edge resource provision is desired to achieve sustainable development goals in the new multi-access edge computing (MEC) architecture. Recently, several approaches have been proposed to solve the demand response problem for energy saving in cloud computing and MEC. However, accurate location information of edge web users should always be provided, which sacrifices users' privacy. To protect edge web users' location privacy while saving energy in MEC, we systematically formulate this location privacy-preserving edge demand response (LEDR) problem. To solve the LEDR problem effectively and efficiently, we propose a system named GEES by incorporating differential geo-obfuscation to secure user privacy while maximizing system utility and energy efficiency through inferences with theoretical analysis. Extensive and comprehensive experiments are conducted based on a synthetic real-world dataset, and the results demonstrate that GEES outperforms representative approaches by 23.02%, 31.47%, and 17.29% on average in terms of energy efficiency, user privacy and system utility.
The Starlink network from SpaceX stands out as the only commercial LEO network with over 2M+ customers and more than 4000 operational satellites. In this paper, we conduct a first-of-its-kind extensive multi-faceted analysis of Starlink performance leveraging several measurement sources. First, based on 19.2M crowdsourced M-Lab speed tests from 34 countries since 2021, we analyze Starlink global performance relative to terrestrial cellular networks. Second, we examine Starlink's ability to support real-time latency and bandwidth-critical applications by analyzing the performance of (i) Zoom conferencing, and (ii) Luna cloud gaming, comparing it to 5G and fiber. Third, we perform measurements from Starlink-enabled RIPE Atlas probes to shed light on the last-mile access and other factors affecting its performance.Finally, we conduct controlled experiments from Starlink dishes in two countries and analyze the impact of globally synchronized "15-second reconfiguration intervals'' of the satellite links that cause substantial latency and throughput variations. Our unique analysis paints the most comprehensive picture of Starlink's global and last-mile performance to date.
QUIC is expected to be a game-changer in improving web application performance. In this paper, we conduct a systematic examination of QUIC's performance over high-speed networks. We find that over fast Internet, the UDP+QUIC+HTTP/3 stack suffers a data rate reduction of up to 45.2% compared to the TCP+TLS+HTTP/2 counterpart. Moreover, the performance gap between QUIC and HTTP/2 grows as the underlying bandwidth increases. We observe this issue on lightweight data transfer clients and major web browsers (Chrome, Edge, Firefox, Opera), on different hosts (desktop, mobile), and over diverse networks (wired broadband, cellular). It affects not only file transfers, but also various applications such as video streaming (up to 9.8% video bitrate reduction) and web browsing. Through rigorous packet trace analysis and kernel- and user-space profiling, we identify the root cause to be high receiver-side processing overhead, in particular, excessive data packets and QUIC's user-space ACKs. We make concrete recommendations for mitigating the observed performance issues.
Emerging web applications (e.g., video streaming and Web of Things applications) account for a large share of traffic in Wide Area Networks (WANs) and provide traffic with various Quality of Service (QoS) requirements. Software-Defined Wide Area Networks (SD-WANs) offer a promising opportunity to enhance the performance of Traffic Engineering (TE), which aims to enable differentiable QoS for numerous web applications. Nevertheless, SD-WANs are managed by controllers, and unpredictable controller failures may undermine flexible network management. Switches previously controlled by the failed controllers may become offline, and flows traversing these offline switches lose the path programmability to route flows on available forwarding paths. Thus, these offline flows cannot be routed/rerouted on previous paths to accommodate potential traffic variations, leading to severe TE performance degradation. Existing recovery solutions reassign offline switches to other active controllers to recover the degraded path programmability but fail to promise good TE performance since higher path programmability does not necessarily guarantee satisfactory TE performance. In this paper, we propose ARES to provide predictable TE performance under controller failures. We formulate an optimization problem to maintain predictable TE performance by jointly considering fine-grained flow-controller reassignment using P4 Runtime and flow rerouting and propose ARES to efficiently solve this problem. Extensive simulation results demonstrate that our problem formulation exhibits comparable load balancing performance to optimal TE solution without controller failures, and the proposed ARES significantly improves average load balancing performance by up to 43.36% with low computation time compared with existing solutions.
Timeline algorithms are key parts of online social networks, but during recent years they have been blamed for increasing polarization and disagreement in our society. Opinion-dynamics models have been used to study a variety of phenomena in online social networks, but an open question remains on how thesemodels can be augmented to take into account the fine-grained impact of user-level timeline algorithms. We make progress on this question by providing a way to model the impact of timeline algorithms on opinion dynamics. Specifically, we show how the popular Friedkin--Johnsen opinion-formation model can be augmented based on aggregate information, extracted from timeline data. We use our model to study the problem of minimizing the polarization and disagreement; we assume that we are allowed to make small changes to the users' timeline compositions by strengthening some topics of discussion and penalizing some others. We present a gradient descent-based algorithm for this problem, and show that under realistic parameter settings, our algorithm computes a (1+\varepsilon)-approximate solution in time~\tO(m\sqrtn łg(1/\varepsilon)), where m~is the number of edges in the graph and n~is the number of vertices. We also present an algorithm that provably computes an \varepsilon-approximation of our model in near-linear time. We evaluate our method on real-world data and show that it effectively reduces the polarization and disagreement in the network. Finally, we release an anonymized graph dataset with ground-truth opinions and more than 27\,000~nodes (the previously largest publicly available dataset contains less than 550~nodes).
Anomaly detection on graphs has recently attracted considerable attention due to its broad range of high-impact applications, including cybersecurity, financial transactions, and recommendation systems. Although many efforts have thus far been made, how to effectively handle the high inconsistency between users' behavior and labels, a fundamental issue in anomaly detection, has not yet received sufficient concern. Moreover, the inconsistency problem is hard to investigate and even deteriorates the performance of anomaly detectors. To this end, we propose a novel graph self-supervised learning framework, Capsule Graph Infomax (termed CapsGI), to overcome the inconsistency of anomaly detection. Inspired by the recent advances of capsules on images, we explore another possibility of reforming the node embedding by capsule ideas to represent the unique node's properties. Concretely, by disentangling heterogeneous factors underlying each node representation, we can establish node capsules such that their representation can reflect intrinsic node properties. To strengthen the connection among normal nodes, CapsGI further represents the part-whole contrastive learning between lower-level capsules (part) and higher-level capsules (whole) by explicitly considering the context graph relations. Extensive experiments on multiple real-world datasets demonstrate that our model significantly outperforms state-of-the-art models.
While exposure to diverse viewpoints may reduce polarization, it can also have a backfire effect and exacerbate polarization when the discussion is adversarial. Here, we examine the question whether intergroup interactions around important events affect polarization between majority and minority groups in social networks. We compile data on the religious identity of nearly 700,000 Indian Twitter users engaging in COVID-19-related discourse during 2020. We introduce a new measure for an individual's group conformity based on contextualized embeddings of tweet text, which helps us assess polarization between religious groups. We then use a meta-learning framework to examine heterogeneous treatment effects of intergroup interactions on an individual's group conformity in the light of communal, political, and socio-economic events. We find that for political and social events, intergroup interactions reduce polarization. This decline is weaker for individuals at the extreme who already exhibit high conformity to their group. In contrast, during communal events, intergroup interactions can increase group conformity. Finally, we decompose the differential effects across religious groups in terms of emotions and topics of discussion. The results show that the dynamics of religious polarization are sensitive to the context and have important implications for understanding the role of intergroup interactions.
The study of continuous-time information diffusion has been an important area of research for many applications in recent years. When only the diffusion traces (cascades) are accessible, cascade-based network inference and influence estimation are two essential problems to explore. Alas, existing methods exhibit limited capability to infer and process networks with more than a few thousand nodes, suffering from scalability issues. In this paper, we view the diffusion process as a continuous-time dynamical system, based on which we establish a continuous-time diffusion model. Subsequently, we instantiate the model to a scalable and effective framework (FIM) to approximate the diffusion propagation from available cascades, thereby inferring the underlying network structure. Furthermore, we undertake an analysis of the approximation error of FIM for network inference. To achieve the desired scalability for influence estimation, we devise an advanced sampling technique and significantly boost the efficiency. We also quantify the effect of the approximation error on influence estimation theoretically. Experimental results showcase the effectiveness and superior scalability of FIM on network inference and influence estimation.
From 2018 to 2023, Brazil experienced its most fiercely contested elections in history, resulting in the election of far-right candidate Jair Bolsonaro followed by the left-wing, Lula da Silva. This period was marked by a murder attempt, a coup attempt, the pandemic, and a plethora of conspiracy theories and controversies. This paper analyses 437 million tweets originating from 13 million accounts associated with Brazilian politics during these two presidential election cycles. We focus on accounts' behavioural patterns. We noted a quasi-monotonic escalation in bot engagement, marked by notable surges both during COVID-19 and in the aftermath of the 2022 election. The data revealed a strong correlation between bot engagement and the number of replies during a single day (r=0.66, p<0.01). Furthermore, we identified a range of suspicious activities, including an unusually high number of accounts being created on the same day, with some days witnessing over 20,000 new accounts and super-prolific accounts generating close to 100,000 tweets. Lastly, we uncovered a sprawling network of accounts sharing Twitter handles, with a select few managing to utilise more than 100 distinct handles. This work can be instrumental in dismantling coordinated campaigns and offer valuable insights for the enhancement of bot detection algorithms.
News coverage profoundly affects how countries and individuals behave in international relations. Yet, we have little empirical evidence of how news coverage varies across countries. To enable studies of global news coverage, we develop an efficient computational methodology that comprises three components: (i) a transformer model to estimate multilingual news similarity; (ii) a global event identification system that clusters news based on a similarity network of news articles; and (iii) measures of news synchrony across countries and news diversity within a country, based on country-specific distributions of news coverage of the global events. Each component achieves state-of-the art performance, scaling seamlessly to massive datasets of millions of news articles. We apply the methodology to 60 million news articles published globally between January 1 and June 30, 2020, across 124 countries and 10 languages, detecting 4357 news events. We identify the factors explaining diversity and synchrony of news coverage across countries. Our study reveals that news media tend to cover a more diverse set of events in countries with larger Internet penetration, more official languages, larger religious diversity, higher economic inequality, and larger populations. Coverage of news events is more synchronized between countries that not only actively participate in commercial and political relations---such as, pairs of countries with high bilateral trade volume, and countries that belong to the NATO military alliance or BRICS group of major emerging economies---but also countries that share certain traits: an official language, high GDP, and high democracy indices.
Large language models (LLMs) are transforming the ways the general public accesses and consumes information. Their influence is particularly pronounced in pivotal sectors like healthcare, where lay individuals are increasingly appropriating LLMs as conversational agents for everyday queries. While LLMs demonstrate impressive language understanding and generation proficiencies, concerns regarding their safety remain paramount in these high-stake domains. Moreover, the development of LLMs is disproportionately focused on English. It remains unclear how these LLMs perform in the context of non-English languages, a gap that is critical for ensuring equity in the real-world use of these systems. This paper provides a framework to investigate the effectiveness of LLMs as multi-lingual dialogue systems for healthcare queries. Our empirically-derived framework XlingEval focuses on three fundamental criteria for evaluating LLM responses to naturalistic human-authored health-related questions: correctness, consistency, and verifiability. Through extensive experiments on four major global languages, including English, Spanish, Chinese, and Hindi, spanning three expert-annotated large health Q&A datasets, and through an amalgamation of algorithmic and human-evaluation strategies, we found a pronounced disparity in LLM responses across these languages, indicating a need for enhanced cross-lingual capabilities. We further propose XLingHealth, a cross-lingual benchmark for examining the multilingual capabilities of LLMs in the healthcare context. Our findings underscore the pressing need to bolster the cross-lingual capacities of these models, and to provide an equitable information ecosystem accessible to all.
Recent policy initiatives have acknowledged the importance of disaggregating data pertaining to diverse Asian ethnic communities to gain a more comprehensive understanding of their current status and to improve their overall well-being. However, research on anti-Asian racism has thus far fallen short of properly incorporating data disaggregation practices. Our study addresses this gap by collecting 12-month-long data from X (formerly known as Twitter) that contain diverse sub-ethnic group representations within Asian communities. In this dataset, we break down anti-Asian toxic messages based on both temporal and ethnic factors and conduct a series of comparative analyses of toxic messages, targeting different ethnic groups. Using temporal persistence analysis, n-gram-based correspondence analysis, and topic modeling, this study provides compelling evidence that anti-Asian messages comprise various distinctive narratives. Certain messages targeting sub-ethnic Asian groups entail different topics that distinguish them from those targeting Asians in a generic manner or those aimed at major ethnic groups, such as Chinese and Indian. By introducing several techniques that facilitate comparisons of online anti-Asian hate towards diverse ethnic communities, this study highlights the importance of taking a nuanced and disaggregated approach for understanding racial hatred to formulate effective mitigation strategies.
Large Language Models (LLMs) have revolutionized solutions for general natural language processing (NLP) tasks. However, deploying these models in specific domains still faces challenges like hallucination. While existing knowledge graph retrieval-based approaches offer partial solutions, they cannot be well adapted to the political domain. On one hand, existing generic knowledge graphs lack vital political context, hindering deductions for practical tasks. On the other hand, the nature of political questions often renders the direct facts elusive, necessitating deeper aggregation and comprehension of retrieved evidence. To address these challenges, we propose a Political Experts through Knowledge Graph Integration (PEG) framework. PEG entails the creation and utilization of a multi-view political knowledge graph (MVPKG), which integrates U.S. legislative, election, and diplomatic data, as well as conceptual knowledge from Wikidata. With MVPKG as its foundation, PEG enhances existing methods through knowledge acquisition, aggregation, and injection. This process begins with refining evidence through semantic filtering, followed by its aggregation into global knowledge via implicit or explicit methods. The integrated knowledge is then utilized by LLMs through prompts. Experiments on three real-world datasets across diverse LLMs confirm PEG's superiority in tackling political modeling tasks.
In this paper, we investigate the conditions under which link analysis algorithms prevent minority groups from reaching high ranking slots. We find that the most common link-based algorithms using centrality metrics, such as PageRank and HITS, can reproduce and even amplify bias against minority groups in networks. Yet, their behavior differs: one one hand, we empirically show that PageRank mirrors the degree distribution for most of the ranking positions and it can equalize representation of minorities among the top ranked nodes; on the other hand, we find that HITS amplifies pre-existing bias in homophilic networks through a novel theoretical analysis, supported by empirical results. We find the root cause of bias amplification in HITS to be the level of homophily present in the network, modeled through an evolving network model with two communities. We illustrate our theoretical analysis on both synthetic and real datasets and we present directions for future work.
The prevalent perspective in quantitative research on opinion dynamics flattens the landscape of the online political discourse into a traditional left--right dichotomy. While this approach helps simplify the analysis and modeling effort, it also neglects the intrinsic multidimensional richness of ideologies. In this study, we analyze social interactions on Reddit, under the lens of a multi-dimensional ideological framework: the political compass. We examine over 8 million comments posted on the subreddits /r/PoliticalCompass and /r/PoliticalCompassMemes during 2020--2022. By leveraging their self-declarations, we disentangle the ideological dimensions of users into economic (left--right) and social (libertarian--authoritarian) axes. In addition, we characterize users by their demographic attributes (age, gender, and affluence). We find significant homophily for interactions along the social axis of the political compass and demographic attributes. Compared to a null model, interactions among individuals of similar ideology surpass expectations by 6%. In contrast, we uncover a significant heterophily along the economic axis: left/right interactions exceed expectations by 10%. Furthermore, heterophilic interactions are characterized by a higher language toxicity than homophilic interactions, which hints at a conflictual discourse between every opposite ideology. Our results help reconcile apparent contradictions in recent literature, which found a superposition of homophilic and heterophilic interactions in online political discussions. By disentangling such interactions into the economic and social axes we pave the way for a deeper understanding of opinion dynamics on social media.
Local news outlets play a vital role in providing trusted and relevant information to communities and addressing their specific needs and concerns. The emergence of news outlets posing as local sources and their spread on social media present a significant challenge in the digital information landscape. This paper presents a comprehensive study investigating posts featuring "pink slime'' news, which is a term that has been used to refer to these news outlets due to its deceptive nature. By analyzing a large dataset of posts, we gain valuable insights into the patterns of these posts and the origin of these posts. We show in this work that extracting syntactical features proves valuable in developing a classification approach for detecting such posts and that the approach achieves 92.5% accuracy. We also show that our approach achieves near-perfect detection when grouping the posts by URL.
Online social networks are ubiquitous parts of modern societies and the discussions that take place in these networks impact people's opinions on diverse topics, such as politics or vaccination. One of the most popular models to formally describe this opinion formation process is the Friedkin--Johnsen (FJ) model, which allows to define measures, such as the polarization and the disagreement of a network. Recently, Xu, Bao and Zhang (WebConf'21) showed that all opinions and relevant measures in the FJ model can be approximated in near-linear time. However, their algorithm requires the entire network and the opinions of all nodes as input. Given the sheer size of online social networks and increasing data-access limitations, obtaining the entirety of this data might, however, be unrealistic in practice. In this paper, we show that node opinions and all relevant measures, like polarization and disagreement, can be efficiently approximated in time that is sublinear in the size of the network. Particularly, our algorithms only require query-access to the network and do not have to preprocess the graph. Furthermore, we use a connection between FJ opinion dynamics and personalized PageRank, and show that in d-regular graphs, we can deterministically approximate each node's opinion by only looking at a constant-size neighborhood, independently of the network size. We also experimentally validate that our estimation algorithms perform well in practice.
Causal effect estimation from networked observational data encounters notable challenges, primarily hidden confounders arising from network structure, or spillover effects that influence unit's outcomes based on neighboring treatment assignments. Existing graph neural network (GNN)-based methods have endeavored to address these challenges, utilizing the GNN's message-passing mechanism to capture hidden confounders or model spillover effects. However, they mainly focus on transductive causal effect learning on a single networked data, limiting their efficacy in inductive settings for real-world applications where networked data often originates from multiple environments influenced by potentially varying time or geographical regions. In light of this, we introduce the principle of invariance to the task of causal effect estimation on networked data, culminating in our Invariant Graph Learning (IGL) framework. Specifically, it first generates multiple networked data to simulate diverse environments from a given observational data. Then it further encourages the model to learn environment-invariant representations for confounders and spillover effects. Such a design enables the model to extrapolate beyond a single observed environment, thereby improving the performance of causal effect estimation in potential new environments. Extensive experiments on two real-world datasets demonstrates the superiority of our approach.