Online discourse surrounding geopolitical crises is volatile and complex. For example, users can often change their opinions, and apply rationales divergently based on the specific scenario under discussion. This paper explores such stance and rationale divergence in social media discussions. We focus on two major ongoing conflicts: the Russia-Ukraine and Israel-Palestine wars. Through this, we identify a set of users who discuss both conflicts, and then label each user’s comments with their stance and associated rationale. Using this unique dataset, we explore how people apply rationales divergently, and evolve their opinions over time. Our research contributes to the CHI community by providing a reusable, rationale-level annotation methodology. Our findings can inform the design of moderation tools, recommender systems, and discussion interfaces. These can be used to surface disagreements, calibrate echo-chamber exposure, and ultimately foster healthier online discourse.

Archive of Our Own (AO3) is a prominent fanfiction platform widely recognized for its feminist design ethos, with a commitment to inclusive, pluralism and community-driven content creation. Among the content on it, Real Person Fiction (RPF) — creations based on public figures rather than fictional characters — offers a unique lens into how users engage with identity, visibility, and cultural narratives. In this study, we conduct a large-scale computational analysis to examine gender representation, thematic diversity, and occupational portrayals. Our findings reveal a significant gender imbalance, with man characters disproportionately over-represented. The readers themselves are also often portrayed as sexual figures. Overall, the relationship portrayals tend to mirror occupational roles, incorporate sexual elements, and reconstruct gender tropes. We interrogate how these patterns intersect with authorship, identity, and power. This work contributes to ongoing conversations about equity, ethics, and feminist values in digital content ecosystems and feminist HCI development.

Large Language Models (LLMs) offer significant potential for assisting with the design and implementation of social platform moderation. This study evaluates their efficacy as both a replacement to and an augmentation for human moderators. Using Reddit as a case study, we first demonstrate that LLMs can effectively replicate human moderation decisions, achieving 83.9% agreement. Through a mix of LLMs and human annotations, we then evaluate real moderator decisions, uncovering substantial error rates: 15.2% of removals and 13% of approvals are estimated as incorrect, primarily stemming from moderators citing the incorrect rules (84.3% of errors). This motivates us to propose RuleSharpener, a tool that uses LLMs to diagnose the root causes of moderation errors (e.g. ambiguous rules) and generate clearer, more actionable guidelines. Our evaluation shows that RuleSharpener increases the accuracy of identifying the specific rules violated by violation posts by 38.0%. Our work demonstrates how LLMs can augment human moderation, refine community policies, and reduce operational burdens, offering a better solution for platform governance on the web.

Content moderation is a major challenge for online platforms. While user-driven blocking is a common tool, its dynamics are usually hidden as moderation data is private. Bluesky makes moderation actions public-by-design, providing an unprecedented opportunity to study a community-driven moderation ecosystem at scale. We leverage this transparency to (1) map the ecosystem of moderation blocking actions across 34 million users, including both individual blocks and the through blocklists, (2) identify the signals that correlate with blocking, and (3) measure the consequences of these actions. We demonstrate that community blocking is widespread, with a volume several orders of magnitude higher than official takedowns, and affects the visibility of more than 90 % of Bluesky content. The blocked accounts represent the most active, popular, toxic, and politically inclined users. However, different blocklists target different types of accounts and behaviors. Finally, blocking does not decrease the popularity and activity of the blocked users and has a limited effect on the social graph. By quantifying its dynamics and trade-offs, our study provides empirical grounding for designing future moderation systems that are transparent, pluralistic, and resistant to centralized control. Taken together, this study provides the first large-scale, quantitative analysis of a community-driven moderation ecosystem, demonstrating how individual and collective interventions influence user behavior.

Live video streaming has grown significantly, especially on networks with limited bandwidth. Traditional video streaming methods, which drop less important frames, often struggle with high latency. We propose ReparoV2, a novel approach designed specifically for live streaming environments. On the client side, ReparoV2 selectively omits video frames at the source, which substantially reduces bandwidth usage without significantly degrading the QoE. ReparoV2 implements a real time Video Frame Discarding (VFD) algorithm, which categorizes even-indexed frames into three types: preserving, recovering using neural networks and substituting with the previous frame, which is corresponding to the high, medium and low degree of difference between two adjacent frames. Additionally, to gain a better QoE, ReparoV2 integrates an adaptive bitrate strategy and introduces multiple discrete low-frame-rate encoding modes, balancing video quality and bandwidth reduction when the network bandwidth is limited. On the server side, ReparoV2 uses a Video Frame Interpolation Deep Neural Network (VFI-DNN) and frame-copying to reconstruct dropped frames, ensuring a smooth viewing experience. It updates the VFD model based on received video chunks and sends the updated model back to the client. ReparoV2 outperforms conventional Dynamic Adaptive Streaming over HTTP (DASH), achieving higher Structural Similarity Index Measure (SSIM) (+0.024), lower bandwidth usage (-23.19%), and improved QoE (+26.66%) on 25fps videos under 0.974Mbps average bandwidth.

The task of reviewer recommendation is increasingly important, with main techniques utilizing general models of text relevance. However, state of the art (SotA) systems still have relatively high error rates. Two possible reasons for this are: a lack of large datasets and the fact that large language models (LLMs) have not yet been applied. To fill these gaps, we first create a substantial new dataset, in the domain of Internet specification documents; then we introduce the use of LLMs and evaluate their performance. We find that LLMs with prompting can improve on SotA in some cases, but that they are not a cure-all: this task provides a challenging setting for prompt-based methods

Congestion control schemes based on information, such as queue size and traffic load, have become increasingly important, especially in data center networks, where the applications have stringent bandwidth and latency requirements. However, some congestion control schemes employ In-band Network Telemetry (INT), which introduces nontrivial bandwidth overhead. In this paper, we propose an efficient congestion control scheme: Precise ECN Encoding (PEE). PEE refactors the Explicit Congestion Notification (ECN) marking logic and proposes a novel ECN-based multi-packet joint encoding/decoding mechanism to enable more precise congestion perception. Compared with the state-of-the-art schemes, PEE achieves precise congestion control without introducing extra bandwidth overhead, achieving high throughput and low latency simultaneously. Comprehensive experimental results show that, compared to TIMELY, DCQCN, and HPCC, PEE reduces the average Flow Completion Time (FCT) by 102.1%, 26.8%, and 16.6% respectively under 80% network load.

Microblogging is a crucial mode of online communication. However, launching a new microblogging platform remains challenging, largely due to network effects. This has resulted in entrenched (and undesirable) dominance by established players, such as X/Twitter. To overcome these network effects, Bluesky, an emerging microblogging platform, introduced starter packs — curated lists of accounts that users can follow with a single click. We ask if starter packs have the potential to tackle the critical problem of social bootstrapping in new online social networks. We assess whether starter packs have indeed been helpful in supporting Bluesky growth. Our dataset includes 25.05 × 10⁶ users and 335.42 × 10³ starter packs with 1.73 × 10⁶ members, covering the entire lifecycle of Bluesky. We study the usage of these starter packs, their ability to drive network and activity growth, and their potential downsides. We also quantify the benefits of starter packs for members and creators on user visibility and activity while identifying potential challenges. By evaluating starter packs’ effectiveness and limitations, we contribute to the broader discourse on platform growth strategies and competitive innovation in the social media landscape.

In today’s digital landscape, the prevalence of sensitive online content has made trigger warnings essential. These warnings inform viewers that the content they are about to see contains sensitive artifacts (e.g. violence). This paper studies the use of trigger warnings, exploiting data from two major platforms: Does the Dog Die, a crowdsourcing trigger warnings platform, and IMDb, a media database. We first study how different media types (e.g. films, video games, and TV shows) are labeled with varying trigger warnings and the different co-occurrence patterns among different trigger warnings. We also discover controversy surrounding certain trigger warnings, with inconsistent opinions stated by different people. We further show that different jurisdictions (e.g. USA vs. UK) assign different content ratings (e.g. R-18) for the same media, even when the same trigger warnings are present. Finally, we develop automatic detectors to identify trigger warnings from IMDb text. We achieve F1 scores exceeding 0.7 for all 10 selected trigger warnings.

Livestreaming by VTubers --- animated 2D/3D avatars controlled by real individuals --- have recently garnered substantial global followings and achieved significant monetary success. Despite prior research highlighting the importance of realism in audience engagement, VTubers deliberately conceal their identities, cultivating dedicated fan communities through virtual personas. While previous studies underscore that building a core fan community is essential to a streamer's success, we lack an understanding of the characteristics of viewers of this new type of streamer. Gaining a deeper insight into these viewers is critical for VTubers to enhance audience engagement, foster a more robust fan base, and attract a larger viewership. To address this gap, we conduct a comprehensive analysis of VTuber viewers on Bilibili, a leading livestreaming platform where nearly all VTubers in China stream. By compiling a first-of-its-kind dataset covering 2.7M livestreaming sessions, we investigate the characteristics, engagement patterns, and influence of VTuber viewers. Our research yields several valuable insights, which we then leverage to develop a tool to ''recommend'' future subscribers to VTubers. By reversing the typical approach of recommending streams to viewers, this tool assists VTubers in pinpointing potential future fans to pay more attention to, and thereby effectively growing their fan community.

Uploading videos from low-cost cameras to the cloud for retrospective analysis presents challenges in privacy, network, and computation. To address these issues and achieve low latency, we propose READY, a novel client-cloud collaborative system. READY aims to enhance the quality of uploaded frames by selectively uploading only the frames relevant to queries. To achieve this, READY establishes an index during video capture, recording object categories and probabilities for each frame. READY adopts an innovative semi-supervised approach for frame indexing, wherein frames are indexed through a continuously updated feature distribution space constructed by k-nearest neighbors (KNN). This enables resource-constrained low-cost cameras to independently establish long-term frame indexes. Additionally, READY utilizes progressively improving operators (lightweight classification models) dispatched by the cloud to optimize the upload order of frames, prioritizing positive frames. By sharing the backbone of low-performance operators, high-performance operators can be efficiently executed, significantly enhancing the camera’s frame processing capability. The established frame index also enables efficient multiple consecutive queries on different classes. Over 110 h of diverse queries across 11 videos, READY outperformed competing alternative designs by achieving an average response time of 67.8% and reducing the proportion of uploaded videos by an average of 79.8% (compared to CloudOnly).

Recently, a distinct form of online antisocial behavior, known as ''fanchuan'', has emerged across online platforms, particularly in livestreaming chats. Fanchuan is an indirect attack on a specific entity, such as a celebrity, video game, or brand. It entails two main actions: (i) individuals first feign support for the entity, and exhibit this allegiance widely; (ii) they then engage in offensive or irritating behavior, attempting to undermine the entity by association. This deceptive conduct is designed to tarnish the reputation of the target and/or its fan community. Fanchuan is a novel, covert and indirect form of social attack, occurring outside the targeted community (often in a similar or broader community), with strategic long-term objectives. This distinguishes fanchuan from other types of antisocial behavior and presents significant new challenges in moderation. We argue it is crucial to understand and combat this new malicious behavior. Therefore, we conduct the first empirical study on fanchuan behavior in livestreaming chats, focusing on Bilibili, a leading livestreaming platform in China. Our dataset covers 2.7 million livestreaming sessions on Bilibili, featuring 3.6 billion chat messages. We identify 130k instances of fanchuan behavior across 37.4k livestreaming sessions. Through various types of analysis, our research offers valuable insights into fanchuan behavior and its perpetrators.

This paper presents the first large-scale empirical study of commercial personally identifiable information (PII) removal systems --- commercial services that claim to improve privacy by automating the removal of PII from data broker's databases. Popular examples of such services include DeleteMe, Mozilla Monitor, Incogni, among many others. The claims these services make may be very appealing to privacy-conscious Web users, but how effective these services actually are at improving privacy has not been investigated. This work aims to improve our understanding of commercial PII removal services in multiple ways. First, we conduct a user study where participants purchase subscriptions from four popular PII removal services, and report (i) what PII the service find, (ii) from which data brokers, (iii) whether the service is able to have the information removed, and (iv) whether the identified information actually is PII describing the participant. And second, by comparing the claims and promises the services makes (e.g. which and how many data brokers each service claims to cover). We find that these services have significant accuracy and coverage issues that limit the usefulness of these services as a privacy-enhancing technology. For example, we find that the measured services are unable to remove the majority of the identified PII records from data broker's (48.2% of the successfully removed found records) and that most records identified by these services are not PII about the user (study participants found that only 41.1% of records identified by these services were PII about themselves).

Out-of-context news is a common type of misinformation on online media platforms. This involves posting a caption, alongside a mismatched news image. Reflecting its importance, researchers have developed models to detect such misinformation. However, a common limitation of these models is that they only consider the scenario where pre-labeled data is available for each news topic or agency, failing to address the out-of-context news detection on unverified news of other topics or agencies. In this work, we therefore focus on domain adaptive out-of-context news detection. We regard news topic or news agency as the domain . In order to effectively adapt the detection model to unlabeled news topics or agencies, we propose Con trastive D omain A daptation with T est- T ime T raining (ConDA-TTT). It first applies contrastive learning to learn a more separable representation space for news inputs, and then uses maximum mean discrepancy (MMD) to remove the domain-specific features so as to keep the domain-invariant features. During test time, it uses the trained model to predict pseudo labels for the target domain test data, and selects those with higher confidence scores to train the classifier of the model, in order to further adapt the model to the target domain data distribution. This approach adapts the model at both training and test phase, making the domain adaptation more robust to distribution shifts. Experimental results demonstrate that our approach outperforms state-of-the-art baselines in all the domain adaptation settings on two benchmark datasets, by as much as 2.6% in F1 and 2.4% in accuracy.

Web games, that are directly playable within web browsers, have recently garnered substantial popularity, particularly among younger demographics. The absence of a paywall for these games has raised concerns regarding the potential privacy-compromising monetization strategies. Comprehensive investigations have been carried out into domains like paid online games, multiplayer online video games, mobile gaming, and their associated privacy and security concerns, but a significant gap persists in the characterization and examination of freely accessible web games. To address these voids, our research conducts an exhaustive analysis of the web games ecosystem. We rigorously scrutinize 22 distinct web game websites with the goal of understanding player experience and addressing privacy concerns. Our methodology involves simulating player interactions across approximately 100,000 individual web games to extract insights into player behavior and privacy risks. The outcomes of our work demonstrate substantial insights into the popularity, ownership, geolocation, and game environment, including metadata diversity. It also reveals the privacy risks faced by users on these websites, encompassing various aspects, such as the presence of questionable third-party advertisements designed for revenue generation, sporadic instances of objectionable content, and the persistence of tracking mechanisms like persistent cookies. Most of all, there is also a discouraging absence of transparent privacy policy statements, a website's privacy policy statement is a legal document to protect users' rights. Also, this deficiency of clear policy information hinders users' capacity to make informed decisions about opting out and understanding the potential consequences. In short, the insights from our study highlight substantial concerns regarding prevalent privacy practices within the domain of free web game websites. Moreover, we contribute our dataset and code, which contain metadata collected from approximately 100 K web games originating from the 22 websites examined in our investigation, thereby we provide a valuable resource for further research and analysis of this less investigated genre of websites.

Deep reinforcement learning (DRL) has been used in congestion control algorithms (CCAs) for its ability to adapt to different network environments. However, its effectiveness is often hindered by the limited availability of training data and constrained training scales. While it has been proved that combining rule-based (expert) CCAs as a guide for DRL (namely hybrid CCAs) can address this limitation, we show through experimental measurements that rule-based CCAs potentially restrict action exploration of DRL models and may cause the DRL models to overly rely on them for higher reward gains. To address this gap, this paper proposes Marten, a framework that improves the effectiveness of rule-based CCAs for DRL. Marten’s key innovations include an entropy-based dynamic exploration scheme that expands the exploration of DRL, and a reward adjustment scheme to prevent the DRL models’ over-reliance on experts in hybrid CCAs. We have implemented Marten in both simulation platform OpenAI Gym and deployment platform QUIC. Experimental results in both emulated and production networks demonstrate Marten can improve throughput by 0.31% and reduce latency by 12.69% on average compared to the state-of-the-art hybrid CCAs. Compared to BBR, Marten achieves a 2.79% increase in throughput and an 11.73% reduction in latency on average.

This paper introduces Mastodoner, a command-line tool and Python library aimed at simplifying access to public data on Mastodon, a prominent player in the Fediverse --- a decentralized network of interconnected social media platforms. Mastodoner addresses the challenges posed by Mastodon's decentralized nature by providing a unified interface for data collection, instance discovery, and secure data sharing. Through examples and demonstrations, this paper illustrates Mastodoner's capabilities in facilitating researchers' access to and analysis of public Mastodon data, thus advancing research in decentralized social media analytics. The tool and documentation are available at: https://github.com/harisbinzia/mastodoner.

Understanding online behaviors, communities, and trends through social media analytics is becoming increasingly important. Recent changes in the accessibility of platforms like Twitter have made Mastodon a valuable alternative for researchers. In this tutorial, we will explore methods for collecting and analyzing public data from Mastodon, a decentralized micro-blogging social network. Participants will learn about the architecture of Mastodon, techniques and best practices for data collection, and various analytical methods to derive insights from the collected data. This session aims to equip researchers with the skills necessary to harness the potential of Mastodon data in computational social science and social data science research.

Deploying machine learning models within the data plane can facilitate traffic analysis at line rate. Nevertheless, existing schemes suffer from performance degradation when faced with hardware resource constraints. They further cannot accommodate incremental model updates as the underlying traffic distributions change. This paper presents Linc, a novel framework for low-resource and incrementally updatable innetwork classification. To circumvent the complicated P4 program that consumes extensive resources, Linc generalizes explicit deployable classification rules by interpreting a customized neural network. For incremental updates with minimal new training data, we design a divide-and-conquer strategy that decomposes the update task into binary classification tasks within distinct subspaces to simplify the task. We then update the model locally within each subspace to improve accuracy for new categories without compromising the accuracy of existing categories. Experimental results reveal that, compared to state-of-the-art solutions, Linc significantly reduces switch hardware resources by up to$15.6 \times$and achieves up to$11.63 \%$higher classification accuracy. During the incremental model updates, Linc not only improves the accuracy by$26.1 \%$but also decreases the table entries by$6 \times$.

The recent development of decentralised and interoperable social networks (such as the "fediverse") creates new challenges for content moderators. This is because millions of posts generated on one server can easily "spread" to another, even if the recipient server has very different moderation policies. An obvious solution would be to leverage moderation tools to automatically tag (and filter) posts that contravene moderation policies, e.g. related to toxic speech. Recent work has exploited the conversational context of a post to improve this automatic tagging, e.g. using the replies to a post to help classify if it contains toxic speech. This has shown particular potential in environments with large training sets that contain complete conversations. This, however, creates challenges in a decentralised context, as a single conversation may be fragmented across multiple servers. Thus, each server only has a partial view of an entire conversation because conversations are often federated across servers in a non-synchronized fashion. To address this, we propose a decentralised conversation-aware content moderation approach suitable for the fediverse. Our approach employs a graph deep learning model (GraphNLI) trained locally on each server. The model exploits local data to train a model that combines post and conversational information captured through random walks to detect toxicity. We evaluate our approach with data from Pleroma, a major decentralised and interoperable micro-blogging network containing 2 million conversations. Our model effectively detects toxicity on larger instances, exclusively trained using their local post information (0.8837 macro-F1). Yet, we show that this approach does not perform well on smaller instances that do not possess sufficient local training data. Thus, in cases where a server contains insufficient data, we strategically retrieve information (posts or model parameters) from other servers to reconstruct larger conversations and improve results. With this, we show that we can attain a macro-F1 of 0.8826. Our approach has considerable scope to improve moderation in decentralised and interoperable social networks such as Pleroma or Mastodon.

An important concept in organisational behaviour is how hierarchy affects the voice of individuals, whereby members of a given organisation exhibit differing power relations based on their hierarchical position. Although there have been prior studies of the relationship between hierarchy and voice, they tend to focus on more qualitative small-scale methods and do not account for structural aspects of the organisation. This paper develops large-scale computational techniques utilising temporal network analysis to measure the effect that organisational hierarchy has on communication patterns throughout an organisation, focusing on the structure of pairwise interactions between individuals. To this end, we focus on one major organisation as a case study --- the Internet Engineering Task Force (IETF) --- a major technical standards development organisation for the Internet. A particularly useful feature of the IETF is a transparent hierarchy, where participants take on explicit roles (e.g., Area Directors, Working Group Chairs), and because its processes are open we have visibility into the communication of people at different hierarchy levels over a long time period. Exploiting this, we utilise a temporal network dataset of 989,911 email interactions among 23,741 participants to study how hierarchy impacts communication patterns. We show that the middle levels of the IETF are growing in terms of their dominance in communications. Higher levels consistently experience a higher proportion of incoming communication than lower levels, with higher levels initiating more communications too. We find that, overall, communication tends to flow "up" the hierarchy more than "down". Finally, we find that communication with higher-levels is associated with future communication more than for lower-levels, which we interpret as "facilitation". We conclude by discussing the implications this has on patterns within the wider IETF and the impact our analysis can have for other organisations.

Online comments within news articles are a key way people share opinions. Discovering insightful comments can, however, be challenging for readers. A solution to this problem is using comment curation, whereby professional editors select the highest quality comments manually --- referred to as ''editor-picks''. This paper studies the growing use of professional editor-curation for user-generated comments. We focus on the New York Times as a case study, using a dataset covering 80k articles. We study the characteristics of editor-pick comments, highlighting how editor criteria vary across news sections (e.g. sports, entertainment). We find that editor-pick comments tend to be longer, more relevant to the article, positive in sentiment, and contain low toxicity. Our analysis further reveals that editors within different news sections exhibit differing criteria when they perform comment selection. Thus, we finally propose a set of models that can automatically identify good candidate editor-picks. Our ultimate goal is to reduce editor and journalistic workload, increasing productivity and the quality of curated comments.

Since the Russian invasion of Ukraine, a large volume of biased and partisan news has been spread via social media platforms. As this may lead to wider societal issues, we argue that understanding how partisan news sharing impacts users' communication is crucial for better governance of online communities. In this paper, we perform a measurement study of partisan news sharing. We aim to characterize the role of such sharing in influencing users' communications. Our analysis covers an eight-month dataset across six Reddit communities related to the Russian invasion. We first perform an analysis of the temporal evolution of partisan news sharing. We confirm that the invasion stimulates discussion in the observed communities, accompanied by an increased volume of partisan news sharing. Next, we characterize users' response to such sharing. We observe that partisan bias plays a role in narrowing its propagation. More biased media is less likely to be spread across multiple subreddits. However, we find that partisan news sharing attracts more users to engage in the discussion, by generating more comments. We then built a predictive model to identify users likely to spread partisan news. The prediction is challenging though, with 61.57% accuracy on average. Our centrality analysis on the commenting network further indicates that the users who disseminate partisan news possess lower network influence in comparison to those who propagate neutral news.

There have been numerous recent attempts to “decentralize” social media platforms, loosely referred to as Web3. Such ideas, often underpinned by blockchain solutions, offer decentralized equivalents of well-known services (e.g., forums, social networks, video sharing sites, microblogs). One particularly challenging function to implement in such a design is content moderation, due to the lack of central control. Consequently, they often rely on user-controlled moderation, whereby each user must create their own personal block list to filter out content they do not wish to see. This paper presents a first study of user-controlled moderation on one exemplar Web3 social microblogging platform called memo.cash. Based on a dataset covering 391K posts, we study the factors that lead users to “mute” each other. We find that the most crucial factor is the platform action count, rather than the presence of things like hate speech. We also show that the followership network plays a pivotal role in determining their visibility on the platform, further influencing their muting behavior. This leads us to design tooling to automate the muting process on a per-user basis. We model this as a recommendation problem, and experiment with a number of state-of-the-art recommender engines. We show that our system can generate effective personalized mute lists for users.

The pitfalls of centralized social networks, such as Facebook and Twitter/X, have led to concerns about control, transparency, and accountability. Decentralized social networks have emerged as a result with the goal of empowering users. These decentralized approaches come with their own trade-offs, and therefore multiple architectures exist. In this paper, we conduct the first large-scale analysis of Bluesky, a prominent decentralized microblogging platform. In contrast to alternative approaches (e.g. Mastodon), Bluesky decomposes and opens the key functions of the platform into subcomponents that can be provided by third party stakeholders. We collect a comprehensive dataset covering all the key elements of Bluesky, study user activity and assess the diversity of providers for each sub-components.

We present the first measurement of the user-effect and privacy impact of "Related Website Sets," a recent proposal to reduce browser privacy protections between two sites if those sites are related to each other. An assumption (both explicitly and implicitly) underpinning the Related Website Sets proposal is that users can accurately determine if two sites are related via the same entity. In this work, we probe this assumption via measurements and a user study of 30 participants, to assess the ability of Web users to determine if two sites are (according to the Related Website Sets feature) related to each other. We find that this is largely not the case. Our findings indicate that 42 (36.8%) of the user determinations in our study are incorrect in privacy-harming ways, where users think that sites are not related, but would be treated as related (and so due less privacy protections) by the Related Website Sets feature. Additionally, 22 (73.3%) of participants made at least one incorrect evaluation during the study. We also characterise the Related Website Sets list, its composition over time, and its governance.

As the demand for online video content drives up bandwidth costs for content providers (CPs), there have been efforts to integrate cost-effective techniques to mitigate their bandwidth expenditure (e.g. using set-top boxes to share content). However, the use of such resources requires considerable effort to balance cost vs. user-perceived quality of service. This paper serves as a first step to quantify this trade-off. We collect and analyze data from a major CP that serves millions of users per day using both traditional CDN resources and alternative cheaper resources. Our analysis reveals that introducing cheaper alternative resources does not always yield anticipated cost savings and may lead to a reduction in quality of experience for users. We provide insights into the reasons behind these issues and propose strategies for better utilization of alternative network resources. We work with a major CP to deploy our proposals, and offer insights on how to better leverage different kinds of bandwidth resources for improved cost-efficiency and streaming delivery.

The advent of regulation, such as the Digital Markets Act, will foster greater interoperability across competing digital platforms. In such regulatory environments, decentralized platforms like Mastodon have pioneered the principles of social data portability. Such platforms are composed of thousands of independent servers, each of which hosts their own social community. To enable transparent interoperability, users can easily migrate their accounts from one server provider to another. In this paper, we examine 8,745 users who switch their server instances in Mastodon. We use this as a case study to examine account portability behavior more broadly. We explore the factors that affect users' decision to switch instances, as well as the impact of switching on their social media engagement and discussion topics. This leads us to build a classifier to show that switching is predictable, with an F1 score of 0.891. We argue that Mastodon serves as an early exemplar of a social media platform that advocates account interoperability and portability. We hope that this study can bring unique insights to a wider and open digital world in the future.

To reduce costs and improve performance, video Content Delivery Networks (CDNs) have started to incorporate lightweight edge nodes, e.g., WiFi access points. Because of this, it is necessary for CDNs to intelligently select which video files should be placed at their core data centers vs. these edge nodes. This is more complex than traditional CDN management, as lightweight edge nodes are much more numerous and unstable than data centers. With this in mind, we present SDPush —- a system for managing content placement in edge CDNs. SDPush tackles two problems. First, it is necessary for SDPush to select which files to proactive push. To address this, we build a file popularity prediction model that effectively identifies video files that will receive many views. Second, SDPush should determine how many replicas of each file to push. To address this, we design a model to predict the benefits of pushing particular files (regarding traffic savings) and then formulate the replica decision problem as a lightweight problem, which is solvable within seconds, even for platforms that accommodate millions of daily active users. Through a trace-driven evaluation and a live deployment on a real video platform, we validate SDPush’s effectiveness, offloading peak-period traffic by 12.1% to 23.9% from the data center to edge nodes, thereby reducing the CDN costs.

The centralization of web services has raised concerns about critical single points of failure, such as content hosting, name resolution, and certification. To address these issues, the "Decentralized Web" movement advocates for de-centralized alternatives. Distributed Hash Tables (DHTs) have emerged as a key component facilitating this movement, as they offer efficient key/value indexing. The InterPlanetary File System (IPFS) exemplifies this approach by leveraging DHTs for data indexing and distribution. A critical finding of previous studies is that DHT PUT performance for record storage is unacceptably slow, sometimes taking minutes to complete and hindering the adoption of delay-intolerant applications. To address this challenge, this research paper presents three significant contributions. First, we present the design of Optimistic Provide, an approach to accelerate DHT PUT operations in Kademlia-based IPFS networks while maintaining full backward compatibility. Second, we implement and deploy the mechanism and see its usage in the de-facto IPFS deployment, Kubo. Third, we evaluate its effectiveness in the IPFS and Filecoin DHTs. We confirm that we enable sub-second record storage from North America and Europe for 90% of PUT operations while reducing networking overhead by over 40% and maintaining record availability.

In the last two years, Artificial Intelligence Generated Content (AIGC) has received significant attention, leading to an anecdotal rise in the amount of AIGC being shared via social media platforms. The impact of AIGC and its implications are of key importance to social platforms, e.g., regarding the implementation of policies, community formation, and algorithmic design. Yet, to date, we know little about how the arrival of AIGC has impacted the social media ecosystem. To fill this gap, we present a comprehensive study of Pixiv, an online community for artists who wish to share and receive feedback on their illustrations. Pixiv hosts over 100 million artistic submissions and receives more than 1 billion page views per month (as of 2023). Importantly, it allows both human and AI generated content to be uploaded. Exploiting this, we perform the first analysis of the impact that AIGC has had on the social media ecosystem, through the lens of Pixiv. Based on a dataset of 15.2 million posts (including 2.4 million AI-generated images), we measure the impact of AIGC on the Pixiv community, as well as the differences between AIGC and human-generated content in terms of content creation and consumption patterns. Our results offer key insight to how AIGC is changing the dynamics of social media platforms like Pixiv.

The rise of generative AI is transforming the landscape of digital imagery, and exerting a significant influence on online creative communities. This has led to the emergence of AI-Generated Content (AIGC) social platforms, such as Civitai. These distinctive social platforms allow users to build and share their own generative AI models, thereby enhancing the potential for more diverse artistic expression. They also provide artists with the means to showcase their creations (generated from the models), engage in discussions, and obtain feedback, thus nurturing a sense of community. Yet, this openness also raises concerns about the abuse of such platforms, e.g., using models to disseminate deceptive deepfakes or infringe upon copyrights. To explore this, we conduct the first comprehensive empirical study of an AIGC social platform, focusing on its use for generating abusive content. As an exemplar, we construct a comprehensive dataset covering Civitai, the largest available AIGC social platform. Based on this dataset of 87K models and 2M images, we explore the characteristics of content and discuss strategies for moderation to better govern these platforms.

App stores allow developers to globally distribute their apps to gain more users and attention. In the highly competitive market of app stores, developers need to cater to a large number of users spanning multiple countries. We posit that the characteristics of diverse geographical, linguistic, cultural, societal, and economic environments may impact the adoption of apps. In this paper, we take the first step to characterize popular apps across over 150 countries worldwide, and explore the potential correlations to a number of underlying factors including geography, language as well as cultural, societal, and economic dimensions. Our study is based on a longitudinal (one-year) dataset of daily app popularity from the iOS app stores, covering 154 regions around the world. We reveal that app popularity shows great diversity across the world, while similarities exist among countries that share geographical proximity and linguistic convergence. The differences in app popularity across regions can be further correlated with the cultural model and socioeconomic indices we adopt. On top of the dataset and findings, we implement a prediction task that contributes to app distribution, helping developers choose the right market to distribute and promote their apps. To the best of our knowledge, we are the first to attempt to provide a global understanding of the characteristics of app popularity across the mobile app ecosystem. Our observations can benefit stakeholders in the ecosystem, striving to improve app uptake.

Unlike fungible tokens (e.g., cryptocurrency), a Non-Fungible Token (NFT) is unique and indivisible. As such, they can be used to authenticate ownership of digital assets (e.g., a photo) in a decentralized fashion. Given that NFTs have generated significant media attention since 2021, we perform a large-scale measurement study of the NFT ecosystem. We collect over 242M transfer logs and over 97M marketplace transactions until Aug 1st, 2023, by far the largest NFT dataset, to the best of our knowledge. We characterize the on-chain behavior of NFTs and their trading across five major marketplaces. We find that, although the NFT ecosystem is growing rapidly, it is driven by a relatively small set of dominant centralized players, with suspicious trade activities, e.g., over 23% of the monetary volume is generated by malicious wash trading and the ecosystem has experienced over 157K cases of NFT arbitrage, with a total sum of over \25M profit. Our observations motivate the need for more research efforts in the NFT security analysis.

Recent research has highlighted the potential of LLMs, like ChatGPT, for performing label annotation on social computing data. However, it is already well known that performance hinges on the quality of the input prompts. To address this, there has been a flurry of research into prompt tuning --- techniques and guidelines that attempt to improve the quality of prompts. Yet these largely rely on manual effort and prior knowledge of the dataset being annotated. To address this limitation, we propose APT-Pipe, an automated prompt-tuning pipeline. APT-Pipe aims to automatically tune prompts to enhance ChatGPT's text classification performance on any given dataset. We implement APT-Pipe and test it across twelve distinct text classification datasets. We find that prompts tuned by APT-Pipe help ChatGPT achieve higher weighted F1-score on nine out of twelve experimented datasets, with an improvement of 7.01% on average. We further highlight APT-Pipe's flexibility as a framework by showing how it can be extended to support additional tuning mechanisms.

Real-time video analytics on edge devices has gained increasing attention across a wide range of business areas. However, edge devices usually have limited computing resources. Consequently, conventional approaches to video analytics either deploy simplified models on the edge (resulting in low accuracy) or transmit video content to the cloud (resulting in high latency and network overheads) to enable deep learning inference (e.g., object detection). In this paper, we introduce ParaLoupe, a novel real-time video analytics system that parallelizes deep learning inference in the edge cluster with task-oriented mini models. These mini models do not attain State-of-the-Art accuracy individually, but collectively can achieve much better accuracy-latency tradeoff than State-of-the-Art models. To achieve this, ParaLoupe crops multiple single-object patches from a given video frame. These single-object patches are then sent to multiple edge devices for parallel inference with specifically designed mini models. A patch-based task scheduling algorithm is further proposed to leverage the computing resources of the edge cluster to meet the service-level objectives. Our experimental results on real-world datasets show that ParaLoupe significantly outperforms baseline methods, achieving up to 14.1× inference speedup with accuracy on par with state-of-the-art models, or improving accuracy up to 45.1% under the same latency constraints.

Device fingerprinting can be used by Internet Service Providers (ISPs) to identify vulnerable IoT devices for early prevention of threats. However, due to the wide deployment of middleboxes in ISP networks, some important data, e.g., 5-tuples and flow statistics, are often obscured, rendering many existing approaches invalid. It is further challenged by the high-speed traffic of hundreds of terabytes per day in ISP networks. This paper proposes DeviceRadar, an online IoT device fingerprinting framework that achieves accurate, real-time processing in ISPs using programmable switches. We innovatively exploit “key packets” as a basis of fingerprints only using packet sizes and directions, which appear periodically while exhibiting differences across different IoT devices. To utilize them, we propose a packet size embedding model to discover the spatial relationships between packets. Meanwhile, we design an algorithm to extract the “key packets” of each device, and propose an approach that jointly considers the spatial relationships and the key packets to produce a neighboring key packet distribution, which can serve as a feature vector for machine learning models for inference. Last, we design a model transformation method and a feature extraction process to deploy the model on a programmable data plane within its constrained arithmetic operations and memory to achieve line-speed processing. Our experiments show that DeviceRadar can achieve state-of-the-art accuracy across 77 IoT devices with 40 Gbps throughput, and requires only 1.3% of the processing time compared to GPU-accelerated approaches.

WallStreetBets (WSB), a Reddit community, impacted stock markets during the 2021 GameStop Short Squeeze. We examine the content and user properties that influence engagement in WSB. Despite WSB's association with emojis and informal terms, engagement among community members depends on more than surface-level factors. Although emojis are commonly used, they are not as effective at fostering interactions among users. Community members engage more with posts that have longer and topic-specific text. Simply producing a high volume of posts is not enough to attract an audience. Consistent topical focus, reciprocal interactions, and previous authorship of catalyst posts influence engagement. WSB posts, regardless of length, generally remain relevant to the community's theme of stock trading. Our findings provide insights into WSB engagement patterns and can be useful for downstream research, such as financial predictive tasks using WSB data.

The ongoing Russia-Ukraine war has been extensively discussed on social media. One commonly observed problem in such discourse is the emergence of echo chambers, where users are rarely exposed to opinions outside their own worldview. Prior literature on this topic has assumed that such users hold a single consistent view. However, recent work has revealed that complex topics often trigger bipartisanship among certain people. With this in mind, we study the presence of echo chambers on Twitter related to the Russo-Ukrainian war. We measure their presence and identify an important subset of bipartisan users who vary their opinion during the invasion. We then explore the role they play in the communications graph and their impact on echo chambers.

From health to education, income impacts a huge range of life choices. Earlier research has leveraged data from online social networks to study precisely this impact. In this paper, we ask the opposite question: do different levels of income result in different online behaviors? We demonstrate it does. We present the first large-scale study of Nextdoor, a popular location-based social network. We collect 2.6 Million posts from 64,283 neighborhoods in the United States and 3,325 neighborhoods in the United Kingdom, to examine whether online discourse reflects the income and income inequality of a neighborhood. We show that posts from neighborhoods with different incomes indeed differ, e.g. richer neighborhoods have a more positive sentiment and discuss crimes more, even though their actual crime rates are much lower. We then show that user-generated content can predict both income and inequality. We train multiple machine learning models and predict both income (R2=0.841) and inequality (R2=0.77).

The acquisition of Twitter by Elon Musk has spurred controversy and uncertainty among Twitter users. The move raised both praise and concerns, particularly regarding Musk's views on free speech. As a result, a large number of Twitter users have looked for alternatives to Twitter. Mastodon, a decentralized micro-blogging social network, has attracted the attention of many users and the general media. In this paper, we analyze the migration of 136,009 users from Twitter to Mastodon. We inspect the impact that this has on the wider Mastodon ecosystem, particularly in terms of user-driven pressure towards centralization. We further explore factors that influence users to migrate, highlighting the effect of users' social networks. Finally, we inspect the behavior of individual users, showing how they utilize both Twitter and Mastodon in parallel. We find a clear difference in the topics discussed on the two platforms. This leads us to build classifiers to explore if migration is predictable. Through feature analysis, we find that the content of tweets as well as the number of URLs, the number of likes, and the length of tweets are effective metrics for the prediction of user migration.

The public suffix list is a community-maintained list of rules that can be applied to domain names to determine how they should be grouped into logical organizations or companies. We present the first large-scale measurement study of how the public suffix list is used by open-source software on the Web and the privacy harm resulting from projects using outdated versions of the list. We measure how often developers include out-of-date versions of the public suffix list in their projects, how old included lists are, and estimate the real-world privacy harm with a model based on a large-scale crawl of the Web. We find that incorrect use of the public suffix list is common in open-source software, and that at least 43 open-source projects use hard-coded, outdated versions of the public suffix list. These include popular, security-focused projects, such as password managers and digital forensics tools. We also estimate that, because of these out-of-date lists, these projects make incorrect privacy decisions for 1313 effective top-level domains (eTLDs), affecting 50,750 domains, by extrapolating from data gathered by the HTTP Archive project.

With the development of cloud applications, large monolithic services have been replaced by loosely-coupled and single-purpose microservices. To improve localization performance, deep learning techniques have been widely used for root cause analysis. Existing research focuses on performance, yet the lack of interpretability creates key barriers to applying deep learning models in practice. This paper presents Grace, an interpretable root cause localization framework. Our work has three aims. First, to more accurately localize root causes using a Spatial-Temporal Graph Convolutional Network (STGCN). To the best of our knowledge, we are the first to apply an STGCN in the domain. Second, to design an interpreter that helps engineers to understand the system decision (beyond the simple binary black-box result). Third, we apply our interpreter to two real cases, which replaces expert knowledge to help build prior knowledge for fault type diagnosis. Our results show that Grace consistently achieves improvements over other state-of-the-art models by 4%-140%.

Live video streaming has grown dramatically in recent years. A key challenge is achieving high video quality of experience (QoE) in low-rate networks. To tackle this problem, recent streaming approaches strategically drop video frames, thus reducing the bandwidth required. However, these methods are usually designed for video on demand (VoD) services and perform poorly in live video streaming. In this paper, we design a new live video streaming approach, Reparo, which aims to improve users' QoE in low-rate networks. On the upload client side, Reparo discards video frames such that they are never encoded or transmitted. To decide which frames should be dropped, we design a real-time Video Frame Discarding (VFD) model, which strives to minimize the impact on video quality while maximizing bandwidth savings. To complement this, Reparo further proposes a modified adaptive bitrate algorithm and two encoding modes, targeting low-frame-rate encoding. On the server side, Reparo then recovers the dropped frames using a lightweight Video Frame Interpolation Deep Neural Network (VFI-DNN). Experimental results show that, compared with vanilla DASH, Reparo reaches an SSIM gain of 0.018, or reduces bandwidth consumption by 30.86%. With an average bandwidth of 0.974Mbps, it improves QoE by 18.13% on average compared to DASH.

Numerous open-source and commercial malware detectors are available. However, their efficacy is threatened by new adversarial attacks, whereby malware attempts to evade detection, e.g., by performing feature-space manipulation. In this work, we propose an explainability-guided and model-agnostic testing framework for robustness of malware detectors when confronted with adversarial attacks. The framework introduces the concept of Accrued Malicious Magnitude (AMM) to identify which malware features could be manipulated to maximize the likelihood of evading detection. We then use this framework to test several state-of-the-art malware detectors' ability to detect manipulated malware. We find that (i) commercial antivirus engines are vulnerable to AMM-guided test cases; (ii) the ability of a manipulated malware generated using one detector to evade detection by another detector (i.e., transferability) depends on the overlap of features with large AMM values between the different detectors; and (iii) AMM values effectively measure the fragility of features (i.e., capability of feature-space manipulation to flip the prediction results) and explain the robustness of malware detectors facing evasion attacks. Our findings shed light on the limitations of current malware detectors, as well as how they can be improved.

As an alternative to Twitter and other centralized social networks, the Fediverse is growing in popularity. The recent, and polemical, takeover of Twitter by Elon Musk has exacerbated this trend. The Fediverse includes a growing number of decentralized social networks, such as Pleroma or Mastodon, that share the same subscription protocol (ActivityPub). Each of these decentralized social networks is composed of independent instances that are run by different administrators. Users, however, can interact with other users across the Fediverse regardless of the instance they are signed up to. The growing user base of the Fediverse creates key challenges for the administrators, who may experience a growing burden. In this paper, we explore how large that overhead is, and whether there are solutions to alleviate the burden. We study the overhead of moderation on the administrators. We observe a diversity of administrator strategies, with evidence that administrators on larger instances struggle to find sufficient resources. We then propose a tool, WatchGen, to semi-automate the process.

The digital age has brought a world of opportunity to children. Connectivity can be a game-changer for some of the world’s most marginalized children. However, while legislatures around the world have enacted regulations to protect children’s online privacy, and app stores have instituted various protections, privacy in mobile apps remains a growing concern for parents and wider society. In this paper, we explore the potential privacy issues and threats that exist in these apps. We investigate 20195 mobile apps from the Google Play store that are designed particularly for children (Family apps) or include children in their target user groups (Normal apps). Using both static and dynamic analysis, we find that 4.47% of Family apps request location permissions, even though collecting location information from children is forbidden by the Play store, and 81.25% of Family apps use trackers (which are not allowed in children’s apps). Even major developers with 40+ kids apps on the Play store use ad trackers. Furthermore, we find that most permission request notifications are not well designed for children, and 19.25% apps have inconsistent content age ratings across the different protection authorities. Our findings suggest that, despite significant attention to children’s privacy, a large gap between regulatory provisions, app store policies, and actual development practices exist. Our research sheds light for government policymakers, app stores, and developers.

Adult video-sharing has undergone dramatic shifts. New platforms that directly interconnect (often amateur) producers and consumers now allow content creators to promote material across the web and directly monetize the content they produce. OnlyFans is the most prominent example of this new trend. OnlyFans is a content subscription service where creators earn money from users who subscribe to their material. In contrast to prior adult platforms, OnlyFans emphasizes creator-consumer interaction for audience accumulation and maintenance. This results in a wide cross-platform ecosystem geared towards bringing consumers to creators’ accounts. In this paper, we inspect this emerging ecosystem, focusing on content creators and the third-party platforms they connect to.

The user experience of mobile web video streaming is often impacted by insufficient and dynamic network bandwidth. In this paper, we design Bidirectionally Optimized Super-Resolution (BiSR) to improve the quality of experience (QoE) for mobile web users under limited bandwidth. BiSR exploits a deep neural network (DNN)-based model to super-resolve key frames efficiently without changing the inter-frame spatial-temporal information. We then propose a downscaling DNN and a mobile-specific optimized lightweight super-resolution DNN to enhance the performance. Finally, a novel reinforcement learning-based adaptive bitrate (ABR) algorithm is proposed to verify the performance of BiSR on real network traces. Our evaluation, using a full system implementation, shows that BiSR saves 26% of bitrate compared to the traditional H.264 codec and improves the SSIM of video by 3.7% compared to the prior state-of-the-art. Overall, BiSR enhances the user-perceived quality of experience by up to 30.6%.

There has been growing interest in the so-called “Web3” movement. This loosely refers to a mix of decentralized technologies, often underpinned by blockchain technologies. Among these, Web3 social media platforms have begun to emerge. These store all social interaction data (e.g., posts) on a public ledger, removing the need for centralized data ownership and management. But this comes at a cost, which some argue is prohibitively expensive. As an exemplar within this growing ecosytem, we explore memo.cash, a microblogging service built on the Bitcoin Cash (BCH) blockchain. We gather data for 24K users, 317K posts, 2.57M user actions, which have facilitated $6.75M worth of transactions. A particularly unique feature is that users must pay BCH tokens for each interaction (e.g., posting, following). We study how this may impact the social makeup of the platform. We therefore study memo.cash as both a social network and a transaction platform.

Mobile Live Streaming (MLS) services are one of the most popular types of mobile apps. They involve a (often amateur) user broadcasting content to a potentially large online audience via unreliable networks. Nevertheless, we still lack a deep understanding of MLS user behavior that is critical for optimizing MLS systems, despite some active measurements on viewer-side behavior. Using detailed logs obtained from a major MLS provider, this paper first conducts an in-depth measurement study of both viewer-side and broadcaster-side behavior. Key findings include large wasteful uploads, strong viewing locality, and traffic dominance of loyal viewers. Specifically, 33.3% of uploads go unwatched, and the viewership of broadcasters tends to be localized. Inspired by our findings, we propose EDGEOPT– a centralized control center for MLS services for optimizing both the first-mile and the last-mile transmission in MLS. Specifically, EDGEOPT reduces wasteful uploading by 71% through adaptive uploading and enhances the replay quality of popular video segments by 10% via highlights retransmission. EDGEOPT also uses a learning-based content pre-fetching scheme that boosts the viewing startup by 29.5% and offloads at most 80% of the viewing workload from the edge servers with peer-assisted delivery.

Most congestion control algorithms (CCAs) are designed for specific network environments. As such, there is no known algorithm that achieves uniformly good performance in all scenarios for all flows. Rather than devising a one-size-fits-all algorithm (which is a likely impossible task), we propose a system to dynamically switch between the most suitable CCAs for specific flows in specific environments. This raises a number of challenges, which we address through the design and implementation of Antelope, a system that can dynamically reconfigure the stack to use the most suitable CCA for individual flows. We build a machine learning model to learn which algorithm works best for individual conditions and implement kernel-level support for dynamically switching between CCAs. The framework also takes application requirements of performance into consideration to fine-tune the selection based on application-layer needs. Moreover, to reduce the overhead introduced by machine learning on individual front-end servers, we (optionally) implement the CCA selection process in the cloud, which allows the share of models and the selection among front-end servers. We have implemented Antelope in Linux, and evaluated it in both emulated and production networks. The results demonstrate the effectiveness of Antelope via dynamic adjusting the CCAs for individual flows. Specifically, Antelope achieves an average 16% improvement in throughput compared with BBR, and an average 19% improvement in throughput and 10% reduction in delay compared with CUBIC.

Large cloud service providers have built an increasing number of geo-distributed data centers (DCs) connected by Wide Area Networks (WANs). These DC-WANs carry both high-priority traffic from interactive services and low-priority traffic from bulk transfers. Given that a DC-WAN is an expensive resource, providers often manage it via traffic engineering algorithms that rely on accurate predictions of inter-DC high-priority (delay-sensitive) traffic. In this article, we perform a large-scale measurement study of high-priority inter-DC traffic from Baidu. We measure how inter-DC traffic varies across their global DC-WAN and show that most existing traffic prediction methods either cannot capture the complex traffic dynamics or overlook traffic interrelations among DCs. Building on our measurements, we propose theInterrelated-TemporalGraph ConvolutionalNetwork(IntegNet) model for inter-DC traffic prediction. In contrast to prior efforts, our model exploits both temporal traffic patterns and inferred co-dependencies between DC pairs. IntegNet forecasts the capacity needed for high-priority traffic demands by accounting for the balance between resource provisioning (i.e., allocating resources exceeding actual demand) and QoS losses (i.e., allocating fewer resources than actual demand). Our experiments show that IntegNet can keep a very limited QoS loss, while also reducing overprovisioning by up to 42.1% compared to the state-of-the-art and up to 66.2% compared to the traditional method used in DC-WAN traffic engineering.

There has been a significant expansion in the use of online social networks (OSNs) to support people experiencing mental health issues. This paper studies the role of Instagram influencers who specialize in coaching people with mental health issues. Using a dataset of 97k posts, we characterize such users' linguistic and behavioural features. We explore how these observations impact audience engagement (as measured by likes). We show that the support provided by these accounts varies based on their self-declared professional identities. For instance, Instagram accounts that declare themselves as Authors offer less support than accounts that label themselves as a Coach. We show that increasing information support in general communication positively affects user engagement. However, the effect of vocabulary on engagement is not consistent across the Instagram account types. Our findings shed light on this understudied topic and guide how mental health practitioners can improve outreach.

Socioeconomic gaps, particularly income inequality, affect crime and public opinion. Although official data sources can identify these patterns of income-based social disparity, a fundamental question remains: Can similar social inequalities be found using abundant online user activity? We explore two sub-questions. (i) How does a neighbourhood's income affect crime discussion in a geographical neighbourhood? (ii) Can user-generated data predict a neighbourhood's income? To answer these questions, we collect 2.5 million Nextdoor posts from 67608 USA and UK neighbourhoods between November 2020 and September 2021. We use official USA and UK data sources for crime and income information.

DNS has often been criticized for inherent design flaws, which make the system vulnerable to attack. Further, domain names are not fully controlled by users, meaning that they can easily be taken down by authorities and registrars. Due to this, there have been efforts to build a decentralized name service that gives greater control to domain owners. The Ethereum Name Service (ENS) is a major example. Yet, no existing work has systematically studied this emerging system, particularly regarding security and misbehavior. To address this gap, we present the first large-scale measurement study of ENS. Our findings suggest that ENS has shown growth during its four years' evolution. We identify several security issues, including traditional name system problems, as well as new issues introduced by the unique properties of ENS. We find that attackers are abusing the system with thousands of squatting ENS names, a number of scam blockchain addresses and indexing of malicious websites. We further develop a new record persistence attack, to find that 22,716 .eth names (3.7% of all names) are vulnerable to name hijacking. Our exploration suggests that our community should invest more effort into the detection and mitigation of issues in decentralized name services.

Mobile Live Streaming (MLS) services are now one of the most popular types of mobile apps. They involve a (often amateur) user broadcasting content to a potentially large online audience via unreliable networks (e.g., LTE). Although prior work has focused on viewer-side behavior, it is equally important to study and improve broadcaster operations. Using detailed logs obtained from a major MLS provider, we first conduct an in-depth measurement study of uploading behavior. Our key findings include large wasteful uploads, strong viewing locality, and traffic dominance of loyal viewers. Specifically, 33.3% of uploads go unwatched, and the viewership of broadcasters tends to be localized to a small set of broadcaster-specific network regions. Inspired by our findings, we propose two system innovations to streamline MLS systems: adaptive uploading and edge server pre-fetching. These optimizations leverage machine learning for reduced waste and improved QoE. Trace-driven experiments show that the adaptive uploading reduces the resources wastage by 63%, and the pre-fetching boosts the startup by 29.5%.

Various congestion control (CC) algorithms have been designed to target specific scenarios. To automate this process, researchers have begun to use machine learning to automatically control the congestion window. These, however, often rely on heavyweight learning models (e.g., neural networks). This can make them unsuitable for resource-constrained mobile devices. On the other hand, lightweight models (e.g., decision trees) are often incapable of reflecting the complexity of diverse mobile wireless environments. To address this, we present Muses, a learning-based approach for generating lightweight congestion control algorithms. Muses relies on imitation learning to train a universal (heavy) LSTM model, which is then used to extract (lightweight) decision tree models that are each targeted at an individual environment. Muses then dynamically selects the most appropriate decision tree on a per-flow basis. We show that Muses can generate high throughput policies across a diverse set of environments, and it is sufficiently light to operate on mobile devices.

The metaverse is a network of shared virtual environments where people can interact synchronously through their avatars. To enable this, it is necessary to accurately capture and recreate (physical) human motion. This is used to render avatars correctly, reflecting the motion of their corresponding users. In large-scale environments this must be done in real-time. This paper proposes a human-avatar framework with full-body motion capture. Its goal is to deliver high-accuracy capture with low computational and network overheads. It relies on a lightweight Octree data structure to record and transmit motion to other users. We conduct a user study with 22 participants and perform a preliminary evaluation of its scalability. Our user study shows that Octree with Inverse Kinematic achieves the best trade-off, achieving low delay and high accuracy. Our proposed solution delivers the lowest delay, with an average of 67ms in an environment of 8 concurrent users. It attains a 55.7% improvement over the prior techniques.

Low-latency live streaming has imposed stringent latency requirements on video transport networks. In this paper we report on the design and operation of the Alibaba low-latency video transport network, LiveNet. LiveNet builds on a flat CDN overlay with a centralized controller for global optimization. As part of this, we present our design of the global routing computation and path assignment, as well as our fast data transmission architecture with fine-grained control of video frames. The performance results obtained from three years of operation demonstrate the effectiveness of LiveNet in improving CDN performance and QoE metrics. Compared with our prior state-of-the-art hierarchical CDN deployment, LiveNet halves the CDN delay and ensures 98% of views do not experience stalls and that 95% can start playback within 1 second. We further report our experiences of running LiveNet over the last 3 years.

Recent years have witnessed growing consolidation of web operations. For example, the majority of web traffic now originates from a few organizations, and even micro-websites often choose to host on large pre-existing cloud infrastructures. In response to this, the "Decentralized Web" attempts to distribute ownership and operation of web services more evenly. This paper describes the design and implementation of the largest and most widely used Decentralized Web platform --- the InterPlanetary File System (IPFS) --- an open-source, content-addressable peer-to-peer network that provides distributed data storage and delivery. IPFS has millions of daily content retrievals and already underpins dozens of third-party applications. This paper evaluates the performance of IPFS by introducing a set of measurement methodologies that allow us to uncover the characteristics of peers in the IPFS network. We reveal presence in more than 2700 Autonomous Systems and 152 countries, the majority of which operate outside large central cloud providers like Amazon or Azure. We further evaluate IPFS performance, showing that both publication and retrieval delays are acceptable for a wide range of use cases. Finally, we share our datasets, experiences and lessons learned.

WhatsApp is a popular messaging app used by over a billion users around the globe. Due to this popularity, understanding misbehavior on WhatsApp is an important issue. The sending of unwanted junk messages by unknown contacts via WhatsApp remains understudied by researchers, in part because of the end-to-end encryption offered by the platform. We address this gap by studying junk messaging on a multilingual dataset of 2.6M messages sent to 5K public WhatsApp groups in India. We characterise both junk content and senders. We find that nearly 1 in 10 messages is unwanted content sent by junk senders, and a number of unique strategies are employed to reflect challenges faced on WhatsApp, e.g., the need to change phone numbers regularly. We finally experiment with on-device classification to automate the detection of junk, whilst respecting end-to-end encryption.

Distributed Deep Learning (DDL) is widely used to accelerate deep neural network training for various Web applications. In each iteration of DDL training, each worker synchronizes neural network gradients with other workers. This introduces communication overhead and degrades the scaling performance. In this paper, we propose a recursive model, OSF (Scaling Factor considering Overlap), for estimating the scaling performance of DDL training of neural network models, given the settings of the DDL system. OSF captures two main characteristics of DDL training: the overlap between computation and communication, and the tensor fusion for batching updates. Measurements on a real-world DDL system show that OSF obtains a low estimation error (ranging from 0.5% to 8.4% for different models). Using OSF, we identify the factors that degrade the scaling performance, and propose solutions to effectively mitigate their impacts. Specifically, the proposed adaptive tensor fusion improves the scaling performance by 32.2%∼ 150% compared to the constant tensor fusion buffer size.

Home security cameras (HSCs) are becoming increasingly important in protecting people’s household property and caring for family members. As an emerging type of home IoT devices, HSCs are distinct from traditional IoT devices in that they are often installed in intimate places, detecting movements constantly. Such close integration with users’ daily life may result in distinct user behavioral patterns and privacy concerns. To explore this, we perform a detailed measurement study based on a large-scale service log dataset from a major HSC service provider. Our analysis reveals unique usage patterns of HSCs, including significant wasted uploads, asymmetrical upload and download traffic, skewed user engagement, and limited watching locations. We further identify three types of privacy risks in current HSC services using both passive logs and active measurements. These risks can be exploited by attackers, through observing only the traffic rates of HSCs, to infer the working state of cameras and even the daily activity routine in places where the camera is installed. Moreover, we find the premium users who pay an extra fee are especially vulnerable to such privacy inferences. We propose countermeasures from the perspectives of susceptible users and HSC providers to mitigate the risks.

Locality Sensitive Hashing (LSH) is widely adopted to index similar data in high-dimensional space for approximate nearest neighbor search. Demanding applications (e.g. web search) mean that LSH must exhibit low response times and high throughput. To achieve this, they tend to load balance between multiple machines. However, as the scale of concurrent queries and the volume of data grow, large numbers of index messages are required. Hence, the network is a key bottleneck. To address this gap, we propose NetSHa, which exploits the computational capacity of programmable switches. Specifically, we introduce a heuristic sort-reduce approach to drop potentially poor candidate answers while preserving search quality. Then, NetSHa aggregates good candidate answers from different index messages when transmitting them. Through this, it reduces the network communication cost. Furthermore, we introduce a best-effort replacement mechanism to improve its concurrency. We implement NetSHa on a Barefoot Tofino programmable switch and evaluate it using 7 real-world datasets. The experimental results show that NetSHa reduces the packet volume by$4\sim 10$times and improves the search efficiency by least 3× in comparison with typical LSH-based distributed search frameworks.

Decentralising the Web is a desirable but challenging goal. One particular challenge is achieving decentralised content moderation in the face of various adversaries (e.g. trolls). To overcome this challenge, many Decentralised Web (DW) implementations rely on federation policies. Administrators use these policies to create rules that ban or modify content that matches specific rules. This, however, can have unintended consequences for many users. In this paper, we present the first study of federation policies on the DW, their in-the-wild usage, and their impact on users. We identify how these policies may negatively impact "innocent" users and outline possible solutions to avoid this problem in the future.

Ella Guest, Bertie Vidgen, Alexandros Mittos, Nishanth Sastry, Gareth Tyson, Helen Margetts. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. 2021.

Most congestion control mechanisms are designed for specific network environments. Hence, there is no known algorithm that achieves uniformly good performance in all scenarios for all flows. Rather than devising such a one-size-fits-all algorithm, we propose a system to dynamically switch between the most suitable congestion control mechanisms for specific flows in specific environments. This raises a number of challenges, which we address through the design and implementation of Antelope, a system that can dynamically reconfigure to use the most suitable congestion control mechanism for an individual flow. We build a machine learning approach to learn which algorithm works best for individual conditions and implement kernel-level support for dynamically adjusting congestion control algorithms. We have implemented Antelope in Linux, and evaluated it in both emulated and production networks. We show that in WAN, DCN, and cellular networks, Antelope achieves an average 16% improvement in throughput compared with BBR; compared with Cubic, Antelope achieves an average 19% improvement in throughput and 10% reduction in delay.

The rapid spread of COVID-19 has made manual contact tracing difficult. Thus, various public health authorities have experimented with automatic contact tracing using mobile applications (or "apps"). These apps, however, have raised security and privacy concerns. In this paper, we propose an automated security and privacy assessment tool - COVIDGUARDIAN - which combines identification and analysis of Personal Identification Information (PII), static program analysis and data flow analysis, to determine security and privacy weaknesses. Furthermore, in light of our findings, we undertake a user study to investigate concerns regarding contact tracing apps. We hope that COVIDGUARDIAN, and the issues raised through responsible disclosure to vendors, can contribute to the safe deployment of mobile contact tracing. As part of this, we offer concrete guidelines, and highlight gaps between user requirements and app performance.

Protocol standards, defined by the Internet Engineering Task Force (IETF), are crucial to the successful operation of the Internet. This paper presents a large-scale empirical study of IETF activities, with a focus on understanding collaborative activities, and how these underpin the publication of standards documents (RFCs). Using a unique dataset of 2.4 million emails, 8,711 RFCs and 4,512 authors, we examine the shifts and trends within the standards development process, showing how protocol complexity and time to produce standards has increased. With these observations in mind, we develop statistical models to understand the factors that lead to successful uptake and deployment of protocols, deriving insights to improve the standardisation process.

Locality Sensitive Hashing (LSH) is widely adopted to index similar data in high-dimensional space for approximate nearest neighbor search. With the rapid increase of datasets, recent interests in LSH have moved to the implementation of distributed search systems with low response time and high throughput. However, as the scale of the concurrent queries and the volume of available data grow, large amounts of index messages still need to be transmitted to centralized servers for the candidate answer reducing and resorting. Hence, the network remains the bottleneck in distributed search systems.To address this gap, we turn our efforts to the network itself and propose NetSHa. NetSHa exploits the in-network computational capacity provided by programmable switches. Specially, NetSHa designs a sort-reduce approach to drop the potential poor candidate answers and aggregates the good candidate answers on programmable switches, while preserving the search quality. We implement NetSHa on Barefoot Tofino switches and evaluate it using 3 datasets (i.e., Random, Wiki and Image). The experimental results show that NetSHa reduces the packet volume by 10 times at most and improves the search efficiency by 3x at least, in comparison with typical LSH-based distributed search frameworks.

The worldwide spread of COVID-19 has prompted extensive online discussions, creating an ‘infodemic’ on social media platforms such as WhatsApp and Twitter. However, the information shared on these platforms is prone to be unreliable and/or misleading. In this paper, we present the first analysis of COVID-19 discourse on public WhatsApp groups from Pakistan. Building on a large scale annotation of thousands of messages containing text and images, we identify the main categories of discussion. We focus on COVID-19 messages and understand the different types of images/text messages being propagated. By exploring user behavior related to COVID messages, we inspect how misinformation is spread. Finally, by quantifying the flow of information across WhatsApp and Twitter, we show how information spreads across platforms and how WhatsApp acts as a source for much of the information shared on Twitter.

Impersonators are playing an important role in the production and propagation of the content on Online Social Networks, notably on Instagram. These entities are nefarious fake accounts that intend to disguise a legitimate account by making similar profiles and then striking social media by fake content, which makes it considerably harder to understand which posts are genuinely produced. In this study, we focus on three important communities with legitimate verified accounts. Among them, we identify a collection of 2.2K impersonator profiles with nearly 10k generated posts, 68K comments, and 90K likes. Then, based on profile characteristics and user behaviours, we cluster them into two collections of `bot' and `fan'. In order to separate the impersonator-generated post from genuine content, we propose a Deep Neural Network architecture that measures `profiles' and `posts' features to predict the content type: `bot-generated', `fan-generated', or `genuine' content. Our study shed light into this interesting phenomena and provides interesting observation on bot-generated content that can help us to understand the role of impersonators in the production of fake content on Instagram.

Recent years have seen a new form of advertisement campaigns emerge: those involving so-called social media influencers. These influencers accept money in return for promoting products via their social media feeds. We gather a large-scale Instagram dataset covering thousands of accounts advertising products, and create a categorisation based on the number of users they reach. We then provide a detailed analysis of the types of products being advertised by these accounts, their potential reach, and the engagement they receive from their followers. Based on our findings, we train machine learning models to distinguish sponsored content from non-sponsored, and identify cases where people are generating sponsored posts without labelling them.

Distributed deep learning (DDL) uses a cluster of servers to train models in parallel. This has been applied to a multiplicity of problems, e.g. online advertisement, friend recommendations. However, the distribution of training means that the communication network becomes a key component in system performance. In this paper, we measure the Alibaba's DDL system, with a focus on understanding the bottlenecks introduced by the network. Our key finding is that the communications overhead has a surprisingly large impact on performance. To explore this, we analyse latency logs of 1.38M Remote Procedure Calls between servers during model training for two real applications of high-dimensional sparse data. We reveal the major contributors of the latency, including concurrent write/read operations of different connections and network connection management. We further observe a skewed distribution of update frequency for individual parameters, motivating us to propose using in-network computation capacity to offload server tasks.

Once considered a luxury, Home Security Cameras (HSCs) are now commonplace and constitute a growing part of the wider online video ecosystem. This paper argues that their expanding coverage and close integration with daily life may result in not only unique behavioral patterns, but also key privacy concerns. This motivates us to perform a detailed measurement study of a major HSC provider, covering 15.4M streams and 211K users. Our study takes two perspectives: (i) we explore the per-user behaviour, identifying core clusters of users; and (ii) we build on this analysis to extract and predict privacy-compromising insight. Key observations include a highly asymmetrical traffic distribution, distinct usage patterns, wasted resources and fixed viewing locations. Furthermore, we identify three privacy risks and explore them in detail. We find that paid users are more likely to be exposed to attacks due to their heavier usage patterns. We conclude by proposing simple mitigations that can alleviate these risks.

Anomaly diagnosis is a critical task for building a reliable cloud system and speeding up the system recovery form failures. With the increase of scales and applications of clouds, they are more vulnerable to various anomalies, and it is more challenging for anomaly troubleshooting. System logs that record significant events at critical time points become excellent sources of information to perform anomaly diagnosis. Never-theless, existing log-based anomaly diagnosis approaches fail to achieve high precision in highly concurrent environments due to interleaved unstructured logs. Besides, transient anomalies that have no obvious features are hard to detect by these approaches. To address this gap, this paper proposes LogSayer, a log pattern-driven anomaly detection model. LogSayer represents the system state by identifying suitable statistical features (e.g. frequency, surge), which are not sensitive to the exact log sequence. It then measures changes in the log pattern when a transient anomaly occurs. LogSayer uses Long Short-Term Memory (LSTM) neural networks to learn the historical correlation of log patterns and applies a BP neural network for adaptive anomaly decisions. Our experimental evaluations over the HDFS and OpenStack data sets show that LogSayer outperforms the state-of-the-art log-based approaches with precision over 98%.

The choice of the corpus on which word embeddings are trained can have a sizable effect on the learned representations, the types of analyses that can be performed with them, and their utility as features for machine learning models. To contribute to the existing sets of pre-trained word embeddings, we introduce and release the first set of word embeddings trained on the content of Urban Dictionary, a crowd-sourced dictionary for slang words and phrases. We show that although these embeddings are trained on fewer total tokens (by at least an order of magnitude compared to most popular pre-trained embeddings), they have high performance across a range of common word embedding evaluations, ranging from semantic similarity to word clustering tasks. Further, for some extrinsic tasks such as sentiment analysis and sarcasm detection where we expect to require some knowledge of colloquial language on social media data, initializing classifiers with the Urban Dictionary Embeddings resulted in improved performance compared to initializing with a range of other well-known, pre-trained embeddings that are order of magnitude larger in size.

Rapid spread of the COVID-19 pandemic is making traditional manual contact tracing challenging; in response, digital contact tracing mobile apps have been developed by the software industry and promoted by governments and health authorities worldwide. However, deploying contact tracing apps across a population at scale have raised many privacy concerns. In this paper, we propose a venue-access-based contact tracing solution, VenueTrace, which preserves user privacy by designs by: (i) enabling the contact tracing of venue-to-user, instead of user-to-user; (ii) avoiding information exchanges between users; and (iii) ensuring no private data is exposed to back-end servers, while enabling proximity contact tracing.

Domain squatting, the adversarial tactic where attackers register domain names that mimic popular ones, has been observed for decades. However, there has been growing anecdotal evidence that this style of attack has spread to other domains. In this paper, we explore the presence of squatting attacks in the mobile app ecosystem. In “App Squatting”, attackers release apps with identifiers (e.g., app name or package name) that are confusingly similar to those of popular apps or well-known Internet brands. This paper presents the first in-depth measurement study of app squatting showing its prevalence and implications. We first identify 11 common deformation approaches of app squatters and propose “AppCrazy”, a tool for automatically generating variations of app identifiers. We have applied AppCrazy to the top-500 most popular apps in Google Play, generating 224,322 deformation keywords which we then use to test for app squatters on popular markets. Through this, we confirm the scale of the problem, identifying 10,553 squatting apps (an average of over 20 squatting apps for each legitimate one). Our investigation reveals that more than 51% of the squatting apps are malicious, with some being extremely popular (up to 10 million downloads). Meanwhile, we also find that mobile app markets have not been successful in identifying and eliminating squatting apps. Our findings demonstrate the urgency to identify and prevent app squatting abuses. To this end, we have publicly released all the identified squatting apps, as well as our tool AppCrazy.

The web is a tangled mass of interconnected services, whereby websites import a range of external resources from various third-party domains. The latter can also load further resources hosted on other domains. For each website, this creates a dependency chain underpinned by a form of implicit trust between the first-party and transitively connected third parties. The chain can only be loosely controlled as first-party websites often have little, if any, visibility on where these resources are loaded from. This article performs a large-scale study of dependency chains in the web to find that around 50% of first-party websites render content that they do not directly load. Although the majority (84.91%) of websites have short dependency chains (below three levels), we find websites with dependency chains exceeding 30. Using VirusTotal, we show that 1.2% of these third parties are classified as suspicious—although seemingly small, this limited set of suspicious third parties have remarkable reach into the wider ecosystem. We find that 73% of websites under-study load resources from suspicious third parties, and 24.8% of first-party webpages contain at least three third parties classified as suspicious in their dependency chain. By running sandboxed experiments, we observe a range of activities with the majority of suspicious JavaScript codes downloading malware.

The barrier to entry for the development of independent, browser-based multiplayer games is high for two reasons: complexity and cost. In this work, we introduce and evaluate a method and library that aims to make this barrier as small as possible, by utilising appropriate development abstractions and peer-to-peer communication between players. Our preliminary evaluation shows that we can lower both the technical development overhead, as well as minimise server costs, at no loss to performance.

A novel resource allocation scheme for cache-aided mobile-edge computing (MEC) is proposed, to efficiently offer communication, storage and computing service for intensive computation and sensitive latency computational tasks. In this paper, the considered resource allocation problem is formulated as a mixed integer non-linear program (MINLP) that involves a joint optimization of tasks offloading decision, cache allocation, computation allocation, and dynamic power distribution. To tackle this non-trivial problem, Markov decision process (MDP) is invoked for mobile users and the access point (AP) to learn the optimal offloading and resource allocation policy from historical experience and automatically improve allocation efficiency. In particular, to break the curse of high dimensionality in the state space of MDP, a deep reinforcement learning (DRL) algorithm is proposed to solve this optimization problem with low complexity. Moreover, extensive simulations demonstrate that the proposed algorithm is capable of achieving a quasi-optimal performance under various system setups, and significantly outperform the other representative benchmark methods considered. The effectiveness of the proposed algorithm is confirmed from the comparison with the results of the optimal solution.

DNS is a vital component for almost every networked application. Originally it was designed as an unencrypted protocol, making user security a concern. DNS-over-HTTPS (DoH) is the latest proposal to make name resolution more secure.

The Decentralised Web (DW) has recently seen a renewed momentum, with a number of DW platforms like Mastodon, PeerTube, and Hubzilla gaining increasing traction. These offer alternatives to traditional social networks like Twitter, YouTube, and Facebook, by enabling the operation of web infrastructure and services without centralised ownership or control. Although their services differ greatly, modern DW platforms mostly rely on two key innovations: first, their open source software allows anybody to setup independent servers ("instances") that people can sign-up to and use within a local community; and second, they build on top of federation protocols so that instances can mesh together, in a peer-to-peer fashion, to offer a globally integrated platform.

Under increasing scrutiny, many web companies now offer bespoke mechanisms allowing any third party to file complaints (e.g., requesting the de-listing of a URL from a search engine). While this self-regulation might be a valuable web governance tool, it places huge responsibility within the hands of these organisations that demands close examination. We present the first large-scale study of web complaints (over 1 billion URLs). We find a range of complainants, largely focused on copyright enforcement. Whereas the majority of organisations are occasional users of the complaint system, we find a number of bulk senders specialised in targeting specific types of domain. We identify a series of trends and patterns amongst both the domains and complainants. By inspecting the availability of the domains, we also observe that a sizeable portion go offline shortly after complaints are generated. This paper sheds critical light on how complaints are issued, who they pertain to and which domains go offline after complaints are issued.

The Web is a tangled mass of interconnected services, where websites import a range of external resources from various third-party domains. The latter can also load resources hosted on other domains. For each website, this creates a dependency chain underpinned by a form of implicit trust between the first-party and transitively connected third-parties. The chain can only be loosely controlled as first-party websites often have little, if any, visibility on where these resources are loaded from. This paper performs a large-scale study of dependency chains in the Web, to find that around 50% of first-party websites render content that they did not directly load. Although the majority (84.91%) of websites have short dependency chains (below 3 levels), we find websites with dependency chains exceeding 30. Using VirusTotal, we show that 1.2% of these third-parties are classified as suspicious - although seemingly small, this limited set of suspicious third-parties have remarkable reach into the wider ecosystem.

A common approach when setting up a website is to utilize third party Web hosting and content delivery networks. Without taking this trend into account, any measurement study inspecting the deployment and operation of websites can be heavily skewed. Unfortunately, the research community lacks generalizable tools that can be used to identify how and where a given website is hosted. Instead, a number of ad hoc techniques have emerged, e.g., using Autonomous System databases, domain prefixes for CNAME records. In this work we propose Pythia , a novel lightweight approach for identifying Web content hosted on third-party infrastructures, including both traditional Web hosts and content delivery networks. Our framework identifies the organization to which a given Web page belongs, and it detects which Web servers are self-hosted and which ones leverage third-party services to provide contents. To test our framework we run it on 40,000 URLs and evaluate its accuracy, both by comparing the results with similar services and with a manually validated groundtruth. Our tool achieves an accuracy of 90% and detects that under 11% of popular domains are self-hosted. We publicly release our tool to allow other researchers to reproduce our findings, and to apply it to their own studies.

Recent research has shown a substantial active presence of bots in online social networks (OSNs). In this article, we perform a comparative analysis of the usage and impact of bots and humans on Twitter—one of the largest OSNs in the world. We collect a large-scale Twitter dataset and define various metrics based on tweet metadata. Using a human annotation task, we assign “bot” and “human” ground-truth labels to the dataset and compare the annotations against an online bot detection tool for evaluation. We then ask a series of questions to discern important behavioural characteristics of bots and humans using metrics within and among four popularity groups. From the comparative analysis, we draw clear differences and interesting similarities between the two entities.

Object storage clouds (e.g., Amazon S3) have become extremely popular due to their highly usable interface and cost-effectiveness. They are, therefore, widely used by various applications (e.g., Dropbox) to host user data. However, because object storage clouds are flat and lack the concept of a directory, it becomes necessary to maintain file meta-data and directory structure in a separate index cloud. This paper investigates the possibility of using a single object storage cloud to efficiently host the whole filesystem for users, including both the file content and directories, while avoiding meta-data loss caused by index cloud failures. We design a novel data structure, Hierarchical Hash (or H2), to natively enable the efficient mapping from filesystem operations to object-level operations. Based on H2, we implement a prototype system, H2Cloud, that can maintain large filesystems of users in an object storage cloud and support fast directory operations. Both theoretical analysis and real-world experiments confirm the efficacy of our solution: H2Cloud achieves faster directory operations than OpenStack Swift by orders of magnitude, and has similar performance to Dropbox but yet does not need a separate index cloud.

The Internet in Africa is evolving rapidly, yet remains significantly behind other regions in terms of performance and ubiquity of access. This clearly has negative consequences for the residents of Africa, but also has implications for organisations designing future networked technologies that might see deployment in the region. This paper presents a measurement campaign methodology to explore the current state of the African Internet. Using vantage points across the continent, we perform the first large-scale mapping of inter-country delays in Africa. Our analysis reveals a number of clusters, where countries have built up low delay interconnectivity, dispelling the myth that intra-communications in Africa are universally poor. Unfortunately, this does not extend to the remainder of the continent, which typically suffers from excessively high delays, often exceeding 300ms. We find that in many cases it is faster to reach European or North American networks that those in other regions of Africa. By mapping the internetwork topology, we identify a number of shortcomings in the infrastructure, most notably an excessive reliance on intercontinental transit providers.

The era of live-broadcast is back but with two major changes. First, unlike traditional TV broadcasts, content is now streamed over the Internet enabling it to reach a wider audience. Second, due to various user-generated content platforms it has become possible for anyone to get involved, streaming their own content to the world. This emerging trend of going live usually happens via social platforms, where users perform live social broadcasts predominantly from their mobile devices, allowing their friends (and the general public) to engage with the stream in real-time. With the growing popularity of such platforms, the burden on the current Internet infrastructure is therefore expected to multiply. With this in mind, we explore one such prominent platform - Facebook Live. We gather 3TB of data, representing one month of global activity and explore the characteristics of live social broadcast. From this, we derive simple yet effective principles which can decrease the network burden. We then dissect global and hyper-local properties of the video while on-air, by capturing the geography of the broadcasters or the users who produce the video and the viewers or the users who interact with it. Finally, we study the social engagement while the video is live and distinguish the key aspects when the same video goes on-demand. A common theme throughout the paper is that, despite its name, many attributes of Facebook Live deviate from both the concepts of live and broadcast.

It is well known that internet infrastructure deployment is progressing at a rapid pace in the African continent. A flurry of recent research has quantified this, highlighting the expansion of its underlying connectivity network. However, improving the infrastructure is not useful without appropriately provisioned services to exploit it. This article measures the availability and utilisation of web infrastructure in Africa. Whereas others have explored web infrastructure in developed regions, we shed light on practices in developing regions. To achieve this, we apply a comprehensive measurement methodology to collect data from a variety of sources. We first focus on Google to reveal that its content infrastructure in Africa is, indeed, expanding. That said, we find that much of its web content is still served from the US and Europe, despite being the most popular website in many African countries. We repeat the same analysis across a number of other regionally popular websites to find that even top African websites prefer to host their content abroad. To explore the reasons for this, we evaluate some of the major bottlenecks facing content delivery networks (CDNs) in Africa. Amongst other factors, we find a lack of peering between the networks hosting our probes, preventing the sharing of CDN servers, as well as poorly configured DNS resolvers. Finally, our mapping of middleboxes in the region reveals that there is a greater presence of transparent proxies in Africa than in Europe or the US. We conclude the work with a number of suggestions for alleviating the issues observed.

Recent research has shown a substantial active presence of bots in online social networks (OSNs). In this paper we utilise our previous work (Stweeler) to comparatively analyse the usage and impact of bots and humans on Twitter, one of the largest OSNs in the world. We collect a large-scale Twitter dataset and define various metrics based on tweet metadata. Using a human annotation task we assign 'bot' and 'human' ground-truth labels to the dataset, and compare the annotations against an online bot detection tool for evaluation. We then ask a series of questions to discern important behavioural characteristics of bots and humans using metrics within and among four popularity groups. From the comparative analysis we draw differences and interesting similarities between the two entities, thus paving the way for reliable classification of bots, and studying automated political infiltration and advertisement campaigns.

Many adult content websites incorporate social networking features. Although these are popular, they raise significant challenges, including the potential for users to "catfish", i.e., to create fake profiles to deceive other users. This paper takes an initial step towards automated catfish detection. We explore the characteristics of the different age and gender groups, identifying a number of distinctions. Through this, we train models based on user profiles and comments, via the ground truth of specially verified profiles. When applying our models for age and gender estimation to unverified profiles, 38% of profiles are classified as lying about their age, and 25% are predicted to be lying about their gender. The results suggest that women have a greater propensity to catfish than men. Our preliminary work has notable implications on operators of such online social networks, as well as users who may worry about interacting with catfishes.

Sampling social graphs is critical for studying things like information diffusion. However, it is often necessary to laboriously obtain unbiased and well-connected datasets because existing survey algorithms are unable to generate well-connected samples, and current random-walk based unbiased sampling algorithms adopt rejection sampling, which heavily undermines performance. This paper proposes a novel random-walk based algorithm which implements Unbiased Sampling using Dummy Edges (USDE). It injects dummy edges between nodes, on which the walkers would otherwise experience excessive rejections before moving out from such nodes. We propose a rejection probability estimation algorithm to facilitate the construction of dummy edges and the computation of moving probabilities. Finally, we apply USDE in two real-life social media: Twitter and Sina Weibo. The results demonstrate that USDE generates well-connected samples, and outperforms existing approaches in terms of sampling efficiency and quality of samples.

The strong divide between applications and the network control plane is desirable, but keeps the network in the dark regarding the ultimate purpose of applications and, as a result, is unable to optimize for these. An alternative approach is for applications to declare to the network their abstract desires; e.g. “I require group multicast”, or “I will run within a local domain and am latency sensitive”. Such an enriched semantic has the potential to enable the network to better fulfill application intent, while also helping optimize network resource usage across applications. We refer to this approach as intent driven networking (IDN). We sketch an incrementally-deployable design to serve as a stepping stone towards a practical realization of IDN within today's Internet.

As a distributed system, Hadoop heavily relies on the network to complete data processing jobs. While Hadoop traffic is perceived to be critical for job execution performance, the actual behaviour of Hadoop network traffic is still poorly understood. This lack of understanding greatly complicates research relying on Hadoop workloads. In this paper, we explore Hadoop traffic through experimentation. We analyse the generated traffic of multiple types of MapReduce jobs, with varying input sizes, and cluster configuration parameters. As a result, we present Keddah, a toolchain for capturing, modelling and reproducing Hadoop traffic, for use with network simulators. Keddah can be used to create empirical Hadoop traffic models, enabling reproducible Hadoop research in more realistic scenarios.

Headers are a critical part of HTTP, and it has been shown that they are increasingly subject to middlebox manipulation. Although this is well known, little is understood about the general regional and network trends that underpin these manipulations. In this paper, we collect data on thousands of networks to understand how they intercept HTTP headers in-the-wild. Our analysis reveals that 25% of measured ASes modify HTTP headers. Beyond this, we witness distinct trends among different regions and AS types; e.g., we observe high numbers of cache headers in poorly connected regions. Finally, we perform an in-depth analysis of the types of manipulations and how they differ across regions.

Information-centric networking (ICN) is a popular research topic. At its heart is the concept of in-network caching. Various algorithms have been proposed for optimizing ICN caching, many of which rely on collaborative principles, i.e. multiple caches interacting to decide what to store. Past work has assumed altruistic nodes that will sacrifice their own performance for the global optimum. We argue that this assumption is insufficient and oversimplifies the reality. We address this problem by modeling the in-network caching problem as a Nash bargaining game. We develop optimal and heuristic caching solutions that consider both performance and fairness. We argue that only algorithms that are fair to all parties involved in caching will encourage engagement and cooperation. Through extensive simulations, we show our heuristic solution, FairCache, ensures that all collaborative caches achieve performance gains without undermining the performance of others.

Mobile dating apps have become a popular means to meet potential partners. Although several exist, one recent addition stands out amongst all others. Tinder presents its users with pictures of people geographically nearby, whom they can either like or dislike based on first impressions. If two users like each other, they are allowed to initiate a conversation via the chat feature. In this paper we use a set of curated profiles to explore the behaviour of men and women in Tinder. We reveal differences between the way men and women interact with the app, highlighting the strategies employed. Women attain large numbers of matches rapidly, whilst men only slowly accumulate matches. Most notably, our results indicate that a little effort in grooming profiles, especially for male users, goes a long way in attracting attention.

Caching is a core principle of information-centric networking (ICN). Many novel algorithms have been proposed for enabling ICN caching, many of which rely on collaborative principles, i.e. multiple caches interacting to decide what to store. Past work has assumed entirely altruistic nodes that will sacrifice their own performance for the global optimum. In this paper, we argue that this assumption is flawed. We address this problem by modelling the in-network caching problem as a Nash bargaining game. We develop optimal and heuristic caching solutions that explicitly consider both performance and fairness. We argue that only algorithms that are fair to all parties will encourage engagement and cooperation. Through extensive simulations, we show our heuristic solution, FairCache, ensures that all collaborative caches achieve performance gains without undermining the performance of others.

In this paper a novel cloud-based Emergency Warning System (EWS) is described, using India as a primary case study. India is a country with nearly two dozen officially recognized languages and divergent communication technologies spread across a large geographical area. As of yet, this diversity has made the deployment of a single nationwide EWS near impossible. To address this deficiency, we have developed an EWS with adaptation as a central design tenet. This adaptation occurs on three levels: Dissemination adaptation addresses civilians' heterogeneous communication technologies; information adaptation morphs warnings to best reflect the capabilities of these communication technologies and users; while presentation adaptation is used to render information to the user in the most appropriate manner. We have developed a full cloud-based prototype, including the full EWS infrastructure and a civilian Android app.

It is well known that Africa's mobile and fixed Internet infrastructure is progressing at a rapid pace. A flurry of recent research has quantified this, highlighting the expansion of its underlying connectivity network. However, improving the infrastructure is not useful without appropriately provisioned services to utilise it. This paper measures the availability of web content infrastructure in Africa. Whereas others have explored web infrastructure in developed regions, we shed light on practices in developing regions. To achieve this, we apply a comprehensive measurement methodology to collect data from a variety of sources. We focus on a large content delivery network to reveal that Africa's content infrastructure is, indeed, expanding. However, we find much web content is still served from the US and Europe. We discover that many of the problems faced are actually caused by significant inter-AS delays in Africa, which contribute to local ISPs not sharing their cache capacity. We discover that a related problem is the poor DNS configuration used by some ISPs, which confounds the attempts of providers to optimise their delivery. We then explore a number of other websites to show that large web infrastructure deployments are a rarity in Africa and that even regional websites host their services abroad. We conclude by making suggestions for improvements.

The prevalence of Internet video streaming challenges the design and operation of modern networks. Content centric networking (CCN) has been proposed to address the challenges through ubiquitous in-network caching. While the expected benefits include higher performance and lower bandwidth consumption, CCN introduces new privacy issues at layer 3. This is because adversaries could infer the content consumed by others by checking cached data in routers. In this paper, we first analyze the design space to improve both caching performance and cache privacy for video delivery in CCN. In light of the observation that these two metrics need to be balanced, we propose CodingCache. It adopts network coding and random forwarding to exploit the potentials of multipath routing in CCN to improve both the diversity of cached content along different paths and the anonymity set for consumers. We evaluate CodingCache through extensive experiments based on a real-world topology and a unique data set of video access logs from a large-scale commercial video service. Our results demonstrate that, compared with the existing CCN strategies, CodingCache is able to increase the cache hit rate while also improve the use of caches across the network, together with reasonable cache privacy.

Content-centric networking (CCN) is a promising framework to rebuild the Internet's forwarding substrate around the concept of content. CCN advocates ubiquitous in-network caching to enhance content delivery, and thus each router has storage space to cache frequently requested content. In this work, we focus on the cache allocation problem, namely, how to distribute the cache capacity across routers under a constrained total storage budget for the network. We first formulate this problem as a content placement problem and obtain the optimal solution by a two-step method. We then propose a suboptimal heuristic method based on node centrality, which is more practical in dynamic networks with frequent content publishing. We investigate through simulations the factors that affect the optimal cache allocation, and perhaps more importantly we use a real-life Internet topology and video access logs from a large scale Internet video provider to evaluate the performance of various cache allocation methods. We observe that network topology and content popularity are two important factors that affect where exactly should cache capacity be placed. Further, the heuristic method comes with only a very limited performance penalty compared to the optimal allocation. Finally, using our findings, we provide recommendations for network operators on the best deployment of CCN caches capacity over routers.

Today, the Internet is a large multimedia delivery infrastructure, with websites such as YouTube appearing at the top of most measurement studies. However, most traffic studies have ignored an important domain: adult multimedia distribution. Whereas, traditionally, such services were provided primarily via bespoke websites, recently these have converged towards what is known as “Porn 2.0”. These services allow users to upload, view, rate, and comment on videos for free (much like YouTube). Despite their scale, we still lack even a basic understanding of their operation. This article addresses this gap by performing a large-scale study of one of the most popular Porn 2.0 websites: YouPorn. Our measurements reveal a global delivery infrastructure that we have repeatedly crawled to collect statistics (on 183k videos). We use this data to characterise the corpus, as well as to inspect popularity trends and how they relate to other features, for example, categories and ratings. To explore our discoveries further, we use a small-scale user study, highlighting key system implications.

The last 5 years have seen a dramatic shift in media distribution. For decades, TV and radio were solely provisioned using push-based broadcast technologies, forcing people to adhere to fixed schedules. The introduction of catch-up services, however, has now augmented such delivery with online pull-based alternatives. Typically, these allow users to fetch content for a limited period after initial broadcast, allowing users flexibility in accessing content. Whereas previous work has investigated both of these technologies, this paper explores and contrasts them, focusing on the network consequences of moving towards this multifaceted delivery model. Using traces from nearly 6 million users of BBC iPlayer, one of the largest catch-up TV services, we study this shift from push- to pull-based access. We propose a novel technique for unifying both push- and pull-based delivery: the Speculative Content Offloading and Recording Engine (SCORE). SCORE operates as a set-top box, which interacts with both broadcast push and online pull services. Whenever users wish to access media, it automatically switches between these distribution mechanisms in an attempt to optimize energy efficiency and network resource utilization. SCORE also can predict user viewing patterns, automatically recording certain shows from the broadcast interface. Evaluations using our BBC iPlayer traces show that, based on parameter settings, an oracle with complete knowledge of user consumption can save nearly 77% of the energy, and over 90% of the peak bandwidth, of pure IP streaming. Optimizing for energy consumption, SCORE can recover nearly half of both traffic and energy savings.

Web content delivery is one of the most important services on the Internet. Access to websites is typically secured via TLS. However, this security model does not account for prefix hijacking on the network layer, which may lead to traffic blackholing or transparent interception. Thus, to achieve comprehensive security and service availability, additional protective mechanisms are necessary such as the RPKI, a recently deployed Resource Public Key Infrastructure to prevent hijacking of traffic by networks. This paper argues two positions. First, that modern web hosting practices make route protection challenging due to the propensity to spread servers across many different networks, often with unpredictable client redirection strategies; and, second, that we need a better understanding why protection mechanisms are not deployed. To initiate this, we empirically explore the relationship between web hosting infrastructure and RPKI deployment. Perversely, we find that less popular websites are more likely to be secured than the prominent sites. Worryingly, we find many large-scale CDNs do not support RPKI, thus making their customers vulnerable. This leads us to explore business reasons why operators are hesitant to deploy RPKI, which may help to guide future research on improving Internet security.

Abstract Commercial Virtual Private Network (VPN) services have become a popular and convenient technology for users seeking privacy and anonymity. They have been applied to a wide range of use cases, with commercial providers often making bold claims regarding their ability to fulfil each of these needs, e.g., censorship circumvention, anonymity and protection from monitoring and tracking. However, as of yet, the claims made by these providers have not received a sufficiently detailed scrutiny. This paper thus investigates the claims of privacy and anonymity in commercial VPN services. We analyse 14 of the most popular ones, inspecting their internals and their infrastructures. Despite being a known issue, our experimental study reveals that the majority of VPN services suffer from IPv6 traffic leakage. The work is extended by developing more sophisticated DNS hijacking attacks that allow all traffic to be transparently captured.We conclude discussing a range of best practices and countermeasures that can address these vulnerabilities

HTTP is a successful Internet technology on top of which a lot of the web resides. However, limitations with its current specification have encouraged some to look for the next generation of HTTP. In SPDY, Google has come up with such a proposal that has growing community acceptance, especially after being adopted by the IETF HTTPbis-WG as the basis for HTTP/2.0. SPDY has the potential to greatly improve web experience with little deployment overhead, but we still lack an understanding of its true potential in different environments. This paper offers a comprehensive evaluation of SPDY's performance using extensive experiments. We identify the impact of network characteristics and website infrastructure on SPDY's potential page loading benefits, finding that these factors are decisive for an optimal SPDY deployment strategy. Through exploring such key aspects that affect SPDY, and accordingly HTTP/2.0, we feed into the wider debate regarding the impact of future protocols.

Content-Centric Networking (CCN) is a promising framework for evolving the current network architecture, advocating ubiquitous in-network caching to enhance content delivery. Consequently, in CCN, each router has storage space to cache frequently requested content. In this work, we focus on the cache allocation problem: namely, how to distribute the cache capacity across routers under a constrained total storage budget for the network. We formulate this problem as a content placement problem and obtain the exact optimal solution by a two-step method. Through simulations, we use this algorithm to investigate the factors that affect the optimal cache allocation in CCN, such as the network topology and the popularity of content. We find that a highly heterogeneous topology tends to put most of the capacity over a few central nodes. On the other hand, heterogeneous content popularity has the opposite effect, by spreading capacity across far more nodes. Using our findings, we make observations on how network operators could best deploy CCN caches capacity.

The Internet has evolved into a huge video delivery infrastructure, with websites such as YouTube and Netflix appearing at the top of most traffic measurement studies. However, most traffic studies have largely kept silent about an area of the Internet that (even today) is poorly understood: adult media distribution. Whereas ten years ago, such services were provided primarily via peer-to-peer file sharing and bespoke websites, recently these have converged towards what is known as ``Porn 2.0''. These popular web portals allow users to upload, view, rate and comment videos for free. Despite this, we still lack even a basic understanding of how users interact with these services. This paper seeks to address this gap by performing the first large-scale measurement study of one of the most popular Porn 2.0 websites: YouPorn. We have repeatedly crawled the website to collect statistics about 183k videos, witnessing over 60 billion views. Through this, we offer the first characterisation of this type of corpus, highlighting the nature of YouPorn's repository. We also inspect the popularity of objects and how they relate to other features such as the categories to which they belong. We find evidence for a high level of flexibility in the interests of its user base, manifested in the extremely rapid decay of content popularity over time, as well as high susceptibility to browsing order. Using a small-scale user study, we validate some of our findings and explore the infrastructure design and management implications of our observations.

Data-sets derived from subjective experiments are often exploited to construct objective quality models using parametric statistics such as MOS and multiple regression. In this paper, using data type and normality tests, we verify that non-normally distributed user opinion scores in nominal or ordinal responses should not be analysed using parametric statistics. The paper introduces a number of non-parametric statistics for valid model building and parameter estimation based on user opinion scores. A set of modelling results are also presented to demonstrate the effectiveness of non-parametric statistics.

This paper proposes a new delivery-centric abstraction. A delivery-centric abstraction allows applications to generate content requests agnostic to location or protocol, with the additional ability to stipulate high-level requirements regarding such things as performance, security, resource consumption and monetary cost. A delivery-centric system therefore constantly adapts to fulfil these requirements, given the constraints of the environment. This abstraction has been realised through a delivery-centric middleware called Juno, which uses a reconfigurable software architecture to (i) discover multiple sources of an item of content, (ii) model each source's ability to provide the content, then (iii) adapt to interact with the source(s) that can best fulfil the application's requirements. Juno therefore utilises existing providers in a backwards compatible way, supporting immediate deployment. This paper evaluates Juno using Emulab to validate its ability to adapt to its environment.

A content-centric network is one which supports host-to-content routing, rather than the host-to-host routing of the existing Internet. This paper investigates the potential of caching data at the router-level in content-centric networks. To achieve this, two measurement sets are combined to gain an understanding of the potential caching benefits of deploying content-centric protocols over the current Internet topology. The first set of measurements is a study of the BitTorrent network, which provides detailed traces of content request patterns. This is then combined with CAIDA's ITDK Internet traces to replay the content requests over a real-world topology. Using this data, simulations are performed to measure how effective content-centric networking would have been if it were available to these consumers/providers. We find that larger cache sizes (10,000 packets) can create significant reductions in packet path lengths. On average, 2.02 hops are saved through caching (a 20% reduction), whilst also allowing 11% of data requests to be maintained within the requester's AS. Importantly, we also show that these benefits extend significantly beyond that of edge caching by allowing transit ASes to also reduce traffic.

This article proposes a new delivery-centric abstraction which extends the existing content-centric networking API. A delivery-centric abstraction allows applications to generate content requests agnostic to location or protocol, with the additional ability to stipulate high-level requirements regarding such things as performance, security, and resource consumption. Fulfilling these requirements, however, is complex as often the ability of a provider to satisfy requirements will vary between different consumers and over time. Therefore, we argue that it is vital to manage this variance to ensure an application fulfils its needs. To this end, we present the Juno middleware, which implements delivery-centric support using a reconfigurable software architecture to: (i) discover multiple sources of an item of content; (ii) model each source’s ability to provide the content; then (iii) adapt to interact with the source(s) that can best fulfil the application’s requirements. Juno therefore utilizes existing providers in a backwards compatible way, supporting immediate deployment. This article evaluates Juno using Emulab to validate its ability to adapt to its environment.

BitTorrent has become the de-facto standard for peer-to-peer content delivery, however, it has been found that it suffers from one fundamental problem: the long-term availability of content. Previous work has attributed this to what is termed the seeder promotion problem in which peers refuse to continue serving content after their own download has completed. As of yet, no deployed solution exists to this problem. In this paper, we objectively investigate the solution space for dealing with the seeder promotion problem. Specifically, both single-torrent and cross-torrent approaches are investigated to ascertain which is superior based on three key metrics: availability, performance, and fairness. To achieve this, two large-scale BitTorrent measurement studies have been performed which include 46K torrents and 29M users. Through these, we first quantify the seriousness of the seeder promotion problem before exploiting the data logs to execute accurate trace-based simulations for the different solutions considered. Using the results, we ascertain and describe the different trade-offs between the four general solutions: extending seeding times, cross-torrent bartering, local persistent histories, and global shared histories. We find that single-torrent solutions are profoundly impractical when considering the user behaviour observed in our studies. In contrast, we discover that the different cross-torrent approaches can offer a far more effective solution for satisfying (to varying degrees) the need for high availability, good performance, and fairness between users.

BitTorrent suffers from one fundamental problem: the long-term availability of content. This occurs on a massive-scale with 38% of torrents becoming unavailable within the first month. In this paper we explore this problem by performing two large-scale measurement studies including 46K torrents and 29M users. The studies go significantly beyond any previous work by combining per-node, per-torrent and system-wide observations to ascertain the causes, characteristics and repercussions of file unavailability. The study confirms the conclusion from previous works that seeders have a significant impact on both performance and availability. However, we also present some crucial new findings: (i) the presence of seeders is not the sole factor involved in file availability, (ii) 23.5% of nodes that operate in seedless torrents can finish their downloads, and (iii) BitTorrent availability is discontinuous, operating in cycles of temporary unavailability.

Grids offer the potential to carry out difficult computing tasks and achieve superior aggregate performance. However, grids are highly complex systems. They consist of heterogeneous resources on disparate hosts from various virtual organizations interconnected via a mixture of communication standards. Monitoring grid resources allows grid schedulers to adapt to changes in the status of these remote resources and the network paths between them. This is crucial to ensuring optimum performance. In this paper we introduce a distributed solution, called GridMAP, to collect network and end-host resource measurements, analyze their performance and feed these statistics and predictions back to schedulers. At this stage, we present our implementation of a passive TCP-SYN-based technique to provide GridMAP with round trip time and throughput measurements and we evaluate our approach against ping and iperf.

Existing approaches for modelling the Internet delay space predict end-to-end delays between two arbitrary hosts as static values. Further, they do not capture the characteristics caused by geographical constraints. Peer-to-peer (P2P) systems are, however, often very sensitive to the underlying delay characteristics of the Internet, since these characteristics directly influence system performance. This work proposes a model to predict lifelike delays between a given pair of end hosts. In addition to its low delay computation time, it has only linear memory costs which allows large scale P2P simulations to be performed. The model includes realistic delay jitter, subject to the geographical position of the sender and the receiver. Our analysis, using existing Internet measurement studies reveals that our approach seems to be an optimal tradeoff between a number of conflicting properties of existing approaches.