The transition from LTE to 5G, and therefore from a monolithic network architecture to a service based architecture, introduces a myriad of new challenges. Most significantly, the decomposition of core components into individual services introduces an extensive signalling traffic overhead within the 5G core network. Additionally, there is a general lack of observability for the 5G core, hindering advancements in modelling and optimization for the system as a whole as well as the individual network functions. To address this, we present a measurement methodology to increase observability within the 5G core system and extract crucial information for the signalling traffic for specific user equipments within the individual network functions. Furthermore, we use our proposed methodology to conduct a case study on the performance of the attachment procedure in Open5GS, a commonly used open source implementation, under different loads. Lastly, we investigate the stability of the Access and Mobility Management Function under heavy load.

Integrated Access and Backhaul (IAB) is a cost-effective and adaptable solution for deploying ultra-dense next-generation (5G and 6G) cellular networks to increase the likelihood of Line-of-Sight (LOS) coverage. This technology allows wireless backhaul connections to be established using the same technology and specifications as available in the access links. However, the absence of a physical testbed or a dataset that can be used for simulation in the millimeter wave (mmWave) band prevents researchers’ validation of the proposed algorithms in the IAB scenario. In this paper, we propose a novel data generator based on a Generative Adversarial Network (GAN), trained on a real dataset from a mobile network that operates in Europe, and maintains a significant market share that returns accurate traffic data for an IAB network. Also, we introduce IAB-CNPos, an intelligent IAB node positioning framework using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) that indicates IAB node positions to increase the coverage network with minimal deployment cost. Furthermore, we integrate this data generator with the SeBaSi simulator (an IAB simulator based on Sionna), which obtains accurate, data-consistent, and realistic end-to-end IAB simulation results. The performance results indicate that the data generator successfully passes the Kolmogorov–Smirnov (KS) criterion, so, it could operate as a verified data generator. Furthermore, we use the SeBaSi simulator, integrated with the data generator, to evaluate the performance of an IAB network in the London City scenario.

While traditional handovers (THOs) have served as a backbone for mobile connectivity, they increasingly suffer from failures and delays, especially in dense deployments and high-frequency bands. To address these limitations, 3GPP introduced Conditional Handovers (CHOs) that enable proactive cell reservations and user-driven execution. However, both handover (HO) types present intricate trade-offs in signaling, resource usage, and reliability. This paper presents unique, countrywide mobility management datasets from a top-tier mobile network operator (MNO) that offer fresh insights into these issues and call for adaptive and robust HO control in next-generation networks. Motivated by these findings, we propose CONTRA, a framework that, for the first time, jointly optimizes THOs and CHOs within the O-RAN architecture. We study two variants of CONTRA: one where users are a priori assigned to one of the HO types, reflecting distinct service or user-specific requirements, as well as a more dynamic formulation where the controller decides on-the-fly the HO type, based on system conditions and needs. To this end, it relies on a practical meta-learning algorithm that adapts to runtime observations and guarantees performance comparable to an oracle with perfect future information (universal no-regret). CONTRA is specifically designed for near-real-time deployment as an O-RAN xApp and aligns with the 6G goals of flexible and intelligent control. Extensive evaluations leveraging crowdsourced datasets show that CONTRA improves user throughput and reduces both THO and CHO switching costs, outperforming 3GPP-compliant and Reinforcement Learning (RL) baselines in dynamic and real-world scenarios.

Internet of Things (IoT) application providers rely on Mobile Network Operators (MNOs) and roaming infrastructures to deliver their services globally. In this complex ecosystem, where the end-to-end communication path traverses multiple entities, it became increasingly challenging to guarantee communication availability and reliability. Further, most platform operators use areactiveapproach to communication issues, responding to user complaints only after incidents have become severe, compromising service quality. This paper presents our experience in the design and deployment of ANCHOR – anunsupervisedanomaly detection solution for the IoT connectivity service of a large global roaming platform. ANCHOR assists engineers by filtering vast amounts of data to identify potential problematic clients (i.e., those with connectivity issues affecting several of their IoT devices), enabling proactive issue resolution before the service is critically impacted. We first describe the IoT service, infrastructure, and network visibility of the IoT connectivity provider we operate. Second, we describe the main challenges and operational requirements for designing an unsupervised anomaly detection solution on this platform. Following these guidelines, we propose different statistical rules, and machine- and deep-learning models for IoT verticals anomaly detection based on passive signaling traffic.We describe the steps we followed working with the operational teams on the design and evaluation of our solution on the operational platform, and report an evaluation on operational IoT customers.

Recently, various Mobile Network Aggregators (MNAs) have emerged, leveraging the coverage of a few base operators to offer global connectivity. These MNAs reap the benefits of network softwarization and virtualization, such as eSIM technology or control/data-plane separation. This paper explores a new type of MNA — a thick MNA — that uses multiple base operators from different regions to provide eSIM profiles and employs public internet gateways outside the base operators' home countries. Specifically, we analyze Airalo, a thick MNA operating in 219 countries. Unlike other MNAs that our community has scrutinized, we show that Airalo often decouples the geographical location of the public internet gateway from the native country of the base operator via IPX Hub Breakout (IHBO). To map Airalo's underlying infrastructure, we ran web-based measurements that 14 volunteers performed while traveling and using an Airalo eSIM on their personal devices. We further dive into Airalo's performance by running device-based measurements (speedtest, traceroute, etc.) in 10 countries. We find that IHBO, despite offering some latency benefits over traditional Home-Routed Roaming, still results in inflated latency and degraded bandwidth compared to non-roaming traffic, introducing new challenges for user experience, content access, and privacy.

Multi-Access Edge Computing (MEC) is a promising solution for providing the computational resources and low latency required by vehicular services, such as autonomous driving. It enables cars to offload computationally intensive tasks to nearby servers. Effective offloading involves determining when to offload tasks, selecting the appropriate MEC site, and efficiently allocating resources to ensure optimal performance. While car mobility poses significant challenges to guaranteeing reliable task completion, today we lack energy-efficient solutions to solve this problem, especially when considering real-world car mobility traces. In this paper, we begin by examining the mobility patterns of cars using data obtained from a leading mobile network operator in Europe. Based on the insights from this analysis, we design an optimization problem for task computation/offloading, considering both static and mobility scenarios. Our objective is to minimize the total energy consumption—both at the cars and the MEC nodes—while satisfying the latency requirements of various tasks. We evaluate our solution, based on multi-agent reinforcement learning, both in simulations as well as in a realistic setup that relies on datasets from the operator. Our solution shows a significant reduction of user dissatisfaction and task interruptions in both static and mobile scenarios, while achieving energy savings of 47% (static) and 14% (mobile) compared to state-of-the-art schemes.

The mobile telecommunication industry is undergoing continuous evolution to cope with ever increasing service requirements and expectations of end users. This has recently led to the rise of Mobile Network Aggregators (MNAs), a new type of global virtual operators that deliver mobile communication services by utilizing multiple Mobile Network Operators (MNOs), dynamically connecting to the one that best meets their customers’ needs based on location and time. MNAs can then offer optimized global coverage by connecting to local MNOs that have limited (e.g., national) geographic service. In this paper, we provide a first in-depth analysis of the operations of three major MNAs: Google Fi, Twilio, and Truphone. We conduct performance measurements across these MNAs for critical applications spanning DNS, web browsing, and video streaming, and compare their performance against that of a traditional MNO from two very diverse geographical locations, US and Spain. We find that MNAs may introduce some delay compared to local MNOs in the region where the user is roaming, yet they offer significant performance improvements over the traditional MNOs roaming model, such as home-routed roaming. To fully assess the potential benefits of the MNA model, we also carry out emulation studies assessing the potential performance gains that MNAs could achieve by deploying both control and user plane functions of open-source 5G implementations across different Amazon Web Services locations.

This study examines Airalo, a "thick" Mobile Network Aggregator. We analyze its unique use of multiple base operators for eSIM provisioning and its decoupling of internet gateways from operators' home countries. Our methodology combines measurements from volunteers in 24 countries using a mix of personal and instrumented Android devices, providing a comprehensive analysis of Airalo's operational model and impact on global mobile connectivity.

Cellular networks rely on handovers (HOs) as a fundamental element to enable seamless connectivity for mobile users. A comprehensive analysis of HOs can be achieved through data from Mobile Network Operators (MNOs); however, the vast majority of studies employ data from measurement campaigns within confined areas and with limited end-user devices, thereby providing only a partial view of HOs. This paper presents the first countrywide analysis of HO performance, from the perspective of a top-tier MNO in a European country. We collect traffic from approximately 40M users for 4 weeks and study the impact of the radio access technologies (RATs), device types, and manufacturers on HOs across the country. We characterize the geo-temporal dynamics of horizontal (intra-RAT) and vertical (inter-RATs) HOs, at the district level and at millisecond granularity, and leverage open datasets from the country's official census office to associate our findings with the population. We further delve into the frequency, duration, and causes of HO failures, and model them using statistical tools. Our study offers unique insights into mobility management, highlighting the heterogeneity of the network and devices, and their effect on HOs.

The growing connected car market requires mobile network operators (MNOs) to rethink their network architecture to deliver ultra-reliable low-latency communications. In response, Multi-Access Edge Computing (MEC) has emerged as a solution, enabling the deployment of computing resources at the network edge. For MNOs to tap into the potential benefits of MEC, they need to transform their networks accordingly. Consequently, the primary objective of this study is to design a realistic MEC architecture and corresponding optimal deployment strategy – deciding on the placement and configuration of computing resources – as opposed to prior studies focusing on MEC run-time management and orchestration (e.g., service placement, computation offloading, and user allocation). To cater to the heterogeneous demands of vehicular services, we propose a multi-tier MEC architecture aligned with 5G and Beyond-5G radio access network deployments. Therefore, we frame MEC deployment as an optimization problem within this architecture, assuming 3 MEC tiers. Our data-driven evaluation, grounded in realistic assumptions about network architecture, usage, latency, and cost models, relies on datasets from a major MNO in the UK. We show the benefits of adopting a 3-tier MEC architecture over single-tier (centralized or distributed) architectures for heterogeneous vehicular services, in terms of deployment cost, energy consumption, and robustness.

IoT plays an important role in cellular networks, and its need for global connectivity is driving the rise of Global IoT Providers. These provide service by aggregating multiple mobile providers through roaming, complicating the understanding of the overall mobile ecosystem. This calls for lightweight monitoring solutions, which are crucial to meet the quality demanded by IoT services, and of automatic means to analyze the data, with the final goal to carry out economic and management activities. This paper provides insights from the study of two commercial, widespread IoT providers. We show how monitoring signaling traffic between mobile networks offers a unique opportunity to understand both the IoT customers’ characteristics and the network functioning. Leveraging clustering, we offer the first data-driven methodology to examine large IoT signaling datasets. By analyzing over 1.3 billion signaling dialogues across two providers, we identify common signaling profiles that depend on the specific IoT vertical, likely misconfigured devices, and sudden changes that indicate potential problems. This provides actionable insights for network management decisions and service improvements, and lays the groundwork for future research on IoT traffic modeling.

Fifth Generation (5G) has been acknowledged as a significant shift in cellular networks, expected to run significantly different classes of services and do so with outstanding performance in terms of low latency, high capacity, and extreme reliability. Managing the resulting complexity of mobile network architectures will depend on making efficient decisions at all network levels based on end-user requirements. However, to achieve this, it is critical to first understand the current mobile ecosystem and capture the device heterogeneity, which is one of the major challenges for ensuring the successful exploitation of 5G technologies.In this paper, we conduct a large-scale measurement study of a commercial mobile operator in the UK, focusing on bringing forward a real-world view on the available network resources, as well as how more than 30M end-user devices utilize the mobile network. We focus on the current status of the 5G Non-Standalone (NSA) deployment and the network-level performance and show how it caters to the prominent use cases that 5G promises to support. Finally, we demonstrate that a fine-granular set of requirements is, in fact, necessary to orchestrate the service to the diverse groups of 5G devices, some of which operate in permanent roaming.

In this paper we propose a method for using mobile network data to detect potential COVID-19 hospitalizations and derive corresponding epidemic risk maps. We apply our methods to a dataset from more than 2 million cellphones, collected by a mobile network provider located in London, UK. The approach yields a 98.6% agreement with released public records of patients admitted to NHS hospitals. Analyzing the mobility pattern of these individuals prior to their potential hospitalization, we present a series of risk maps. Compared with census-based maps, our risk maps indicate that the areas of highest risk are not necessarily the most densely populated ones and may change from day to day. Finally, we observe that hospitalized individuals tended to have a higher average mobility than non-hospitalized ones.

A new model of global virtual Mobile Network Operator (MNO) - the Mobile Network Aggregator (MNA) - has recently been gaining significant traction. MNAs provide mobile communications services to their customers by leveraging multiple MNOs, and connecting through the one that best match their customers' needs at any point in time (and space). MNAs naturally provide optimized global coverage by connecting through local MNOs across the different geographic regions they provide service. In this paper, we dissect the operations of three MNAs, namely, Google Fi, Twilio and Truphone. We perform measurements using the three selected MNAs to assess their performance for three major applications, namely, DNS, web browsing and video streaming. We benchmark their performance comparing it to the one of a traditional MNO. We find that even MNAs provide some delay penalty compared to the service accessed through the local MNOs in the geographic area where the user is roaming, they can significantly improve performance compared to traditional roaming model of the MNOs (e.g. home routed roaming). Finally, in order to fully quantify the potential benefits that can be realized using the MNA model, we perform a set of emulations by deploying both control and user plane functions of open-source 5G implementations in different locations of AWS, and measure the potential gains.

Route leak events have historically caused many wide-scale disruptions on the Internet. Leaks are particularly hard to detect because they most frequently involve routes with legitimate origin announced through legitimate paths that are propagated beyond their legitimate scope. In this paper we present ASIRIA, a mechanism for detecting and avoiding leaked routes and protecting against leakage events that uses AS relationship information inferred from the Internet Routing Registries. By relying on existing information, ASIRIA provides immediate benefits to early adopters. In particular, we consider the deployment of ASIRIA to detect leaks caused by over 300 ASes and we show that it can detect over 99% of the leakage events generated by a customer or a peer solely using currently available information in 90% of the cases.

In this paper we present a large measurement study of the impact on the performance of the adoption of HTTPS as a transport for the DNS protocol (DoH) with public resolvers compared to the existent approach of using non-encrypted transport of DNS queries with the resolver services locally provided by ISPs. Using on web-ads as the mean to execute our tests, we perform over 42 million measurements from more than 4 million vantage points distributed in 32 countries and served by over 2,500 ISPs. We find that, the median resolution time increased 17 ms when using DoH with Cloudflare, 41 ms when using DoH with Quad9, 68 ms when using DoH with Google and 170 ms when using DoH with DNS.SB, compared to using Do53 with the local resolver for a non-cached name. We find similar increases even when using caching. The results presented in the paper contribute to the ongoing discussion of the tradeoffs involved in the combined adoption of public resolvers and DoH.

“Roam like Home” is the initiative of the European Commission (EC) to end the levy of extra charges when roaming within the European region. As a result, people can use data services more freely across Europe. However, the implications of roaming solutions on network performance have not been carefully examined yet. This paper provides an in-depth characterization of the implications of international data roaming within Europe. We build a unique roaming measurement platform using 16 different mobile networks deployed in six countries across Europe. Using this platform, we measure different aspects of international roaming in 4G networks in Europe, including mobile network configuration, performance characteristics, and quality of experience. We find that operators adopt a common approach to implement roaming called Home-routed roaming (HR). This results in additional latency penalties of 60 ms or more, depending on geographical distance. This leads to worse browsing performance, with an increase in the metrics related to Quality of Experience (QoE) of users (Page Load time and Speed Index) in the order of 15-20 percent. We further analyze in isolation the impact of latency on QoE metrics and find that the penalty imposed by HR leads to a degradation on QoE metrics up to 150 percent in case of intercontinental roaming.

Streaming 360° video demands high bandwidth and low latency, and poses significant challenges to Internet Service Providers (ISPs) and Mobile Network Operators (MNOs). The identification of 360° video traffic can therefore benefits fixed and mobile carriers to optimize their network and provide better Quality of Experience (QoE) to the user. However, end-to-end encryption of network traffic has obstructed identifying those 360° videos from regular videos. As a solution this paper presents 360NorVic, a near-realtime and offline Machine Learning (ML) classification engine to distinguish 360° videos from regular videos when streamed from mobile devices. We collect packet and flow level data for over 800 video traces from YouTube & Facebook accounting for 200 unique videos under varying streaming conditions. Our results show that for near-realtime and offline classification at packet level, average accuracy exceeds 95%, and that for flow level, 360NorVic achieves more than 92% average accuracy. Finally, we pilot our solution in the commercial network of a large MNO showing the feasibility and effectiveness of 360NorVic in production settings.

IP Exchange Providers (IPX-Ps) offer to their customers (e.g., mobile or IoT service providers) global data roaming and support for a variety of emerging services. They peer to other IPX-Ps and form the IPX network, which interconnects 800 MNOs worldwide offering their customers access to mobile services in any other country. Despite the importance of IPX-Ps, little is known about their operations and performance. In this paper, we shed light on these opaque providers by analyzing a large IPX-P with more than 100 PoPs in 40+ countries, with a particularly strong presence in America and Europe. Specifically, we characterize the traffic and performance of the main infrastructures of the IPX-P (i.e., 2-3-4G signaling and GTP tunneling), and provide implications for its operation, as well as for the IPX-P's customers. Our analysis is based on statistics we collected during two time periods (i.e., prior and during COVID-19 pandemic) and includes insights on the main service the platform supports (i.e., IoT and data roaming), traffic breakdown and geographical/temporal distribution, communication performance (e.g., tunnel setup time, RTTs). Our results constitute a step towards advancing the understanding of IPX-Ps at their core, and provide guidelines for their operations and customer satisfaction.

Support for "things" roaming internationally has become critical for Internet of Things (IoT) verticals, from connected cars to smart meters and wearables, and explains the commercial success of Machine-to-Machine (M2M) platforms. We analyze IoT verticals operating with connectivity via IoT SIMs, and present the first large-scale study of commercially deployed IoT SIMs for energy meters. We also present the first characterization of an operational M2M platform and the first analysis of the rather opaque associated ecosystem.

During early 2020, the SARS-CoV-2 virus rapidly spread worldwide, forcing many governments to impose strict lock-down measures to tackle the pandemic. This significantly changed peoples mobility and habits, subsequently impacting how they use telecommunication networks. In this paper, we investigate the effects of the COVID-19 emergency on a UK Mobile Network Operator (MNO). We quantify the changes in users mobility and investigate how this impacted the cellular network usage and performance. Our analysis spans from the entire country to specific regions, and geodemographic area clusters. We also provide a detailed analysis for London. Our findings bring insights at different geotemporal granularity on the status of the cellular network, from the decrease in data traffic volume in the cellular network and lower load on the radio network, counterposed to a surge in the conversational voice traffic volume.

The Internet Para Todos program is working to provide sustainable mobile broadband to 100 M unconnected people in Latin America. In this paper we present our commercial deployment in thousands remote small communities and describe the unique experience of maintaining this infrastructure. We describe the challenges related to managing operations containing the cost in these extreme geographical conditions. We also analyze operational data to understand outage patterns and present typical operational issues in this unique remote community environment. Finally, we present an extension of the operations support system (OSS) leveraging advanced analytics and machine learning with the goal of optimizing network maintenance while reducing costs.

BGP reconvergence events involving a large number of prefixes may result in the loss of large amounts of traffic. Based on the observation that a very small number of prefixes carries the vast majority of traffic, we propose Power Prefixes Prioritization (PPP) to ensure the routes of these popular BGP prefixes converge first. By doing so, we significantly reduce the amount of traffic lost during reconvergence events. To achieve this, PPP obtains an ordered list of popular prefixes through traffic inspection, and configures the resulting prefix rank in the BGP routers to prioritize the processing and advertisement of BGP routes. We model the benefits of PPP over traditional BGP processing in terms of traffic loss for both generic and a Zipf traffic distribution, and we consider the impact of sampling in the process of obtaining the prefix rank. Applying the mechanism to real traffic traces obtained from WIDE, we show that PPP reduces the amount of traffic lost by an order of magnitude, even when we configure it to use conservative sampling rates. We prototype our proposal in Quagga to show the feasibility of its implementation, and we observe similar traffic loss reduction. PPP can be deployed incrementally, as it is implemented purely as a change in the router-internal BGP processing behavior.

In recent years, Downlink (DL) throughput estimation in Mobile Broadband (MBB) networks has gained immense popularity and it is expected to become a vital component of the upcoming fifth generation (5G) systems. Plentiful adaptive video streaming algorithms greatly rely on accurate DL throughput predictions to adapt their mechanisms and ensure high Quality of Service (QoS) to the end-users. Thus far, conventional DL throughput estimation approaches, also known as speed tests, require an extensive exchange of TCP traffic over the network for an allocated time duration. While such tools appear to deliver trustworthy results, they turn out to be inefficient when mobile subscriptions with limited data plans are engaged. In this paper, we propose a supervised Machine Learning (ML) solution for DL throughput estimation that aims at delivering highly accurate predictions while significantly limiting the over-the-air data consumption. We capture the network performance metrics by exploring both crowdsourced and controlled testing methodologies. We leverage RTR-NetTest, a platform of broadband measurements provided by the Austrian Regulatory Authority for Broadcasting and Telecommunications (RTR), and MONROE-NetTest, its counterpart wrapper built as an Experiment as a Service (EaaS) on top of Measuring Mobile Broadband Networks in Europe (MONROE). Results reveal that our solution can achieve a 39.7% reduction in terms of data consumption while delivering a Median Absolute Percentage Error (MdAPE) of 5.55%. We further show that accuracy can be traded-off, for example, a significant data consumption reduction of 95.15% can be achieved for a MdAPE of 20%.

Measuring and characterizing web page performance is a challenging task. When it comes to the mobile world, the highly varying technology characteristics coupled with the opaque network configuration make it even more difficult. Aiming at reproducibility, we present a large scale empirical study of web page performance collected in eleven commercial mobile networks spanning four countries. By digging into measurement from nearly two million web browsing sessions, we shed light on the impact of different web protocols, browsers, and mobile technologies on the web performance. We find that the impact of mobile broadband access is sizeable. For example, the median page load time using mobile broadband increases by a third compared to wired access. Mobility clearly stresses the system, with handover causing the most evident performance penalties. Contrariwise, our measurements show that the adoption of HTTP/2 and QUIC has practically negligible impact. To understand the intertwining of all parameters, we adopt state-of-the-art statistical methods to identify the significance of different factors on the web performance. Our analysis confirms the importance of access technology and mobility context as well as webpage composition and browser. Our work highlights the importance of large-scale measurements. Even with our controlled setup, the complexity of the mobile web ecosystem is challenging to untangle. For this, we are releasing the dataset as open data for validation and further research.

Mobile applications outsource their cloud infrastructure deployment and content delivery to cloud computing services and content delivery networks. Studying how these services, which we collectively denote Cloud Service Providers (CSPs), perform over Mobile Network Operators (MNOs) is crucial to understanding some of the performance limitations of today's mobile apps. To that end, we perform the first empirical study of the complex dynamics between applications, MNOs and CSPs. First, we use real mobile app traffic traces that we gathered through a global crowdsourcing campaign to identify the most prevalent CSPs supporting today's mobile Internet. Then, we investigate how well these services interconnect with major European MNOs at a topological level, and measure their performance over European MNO networks through a month-long measurement campaign on the MONROE mobile broadband testbed. We discover that the top 6 most prevalent CSPs are used by 85 % of apps, and observe significant differences in their performance across different MNOs due to the nature of their services, peering relationships with MNOs, and deployment strategies. We also find that CSP performance in MNOs is affected by inflated path length, roaming, and presence of middleboxes, but not influenced by the choice of DNS resolver.

Video streaming is a very popular service among the end-users of Mobile Broadband (MBB) networks. DASH and WebRTC are two key technologies in the delivery of mobile video. In this work, we empirically assess the performance of video streaming with DASH and WebRTC in operational MBB networks, by using a large number of programmable network probes spread over several countries in the context of the MONROE project. We collect a large dataset from more than 300 video streaming experiments. Our dataset consists of network traces, performance indicators captured during the streaming sessions, and experiment metadata. The dataset captures the wide variability in video streaming performance, and unveils how mobile broadband is still not offering consistent quality guarantees across different countries and networks, especially for users on the move. We open source our complete software toolset and provide the video dataset as open data.

"Roam like Home" is the initiative of the European Commission (EC) to end the levy of extra charges when roaming within the European region. As a result, people are able to use data services more freely across Europe. However, the implications roaming solutions have on performance have not been carefully examined. This paper provides an in-depth characterization of the implications of international data roaming within Europe. We build a unique roaming measurement platform using 16 different mobile networks deployed in six countries across Europe. Using this platform, we measure different aspects of international roaming in 3G and 4G networks, including mobile network configuration, performance characteristics, and content discrimination. We find that operators adopt common approaches to implementing roaming, resulting in additional latency penalties of ∼60 ms or more, depending on geographical distance. Considering content accessibility, roaming poses additional constraints that leads to only minimal deviations when accessing content in the original country. However, geographical restrictions in the visited country make the picture more complicated and less intuitive.

The accuracy of measurement-driven mobile coverage maps depends on the quality, density and pattern of the signal strength observations. Thus, identifying an efficient measurement data collection methodology is essential, especially when considering the cost associated with the measurement collection approaches (e.g., drive tests, crowd approaches). We propose ZipWeave, a novel measurement data collection and fusion framework for building efficient and reliable measurement-based mobile coverage maps. ZipWeave incorporates a novel nonuniform sampling strategy to achieve reliable coverage maps with reduced sample size. Assuming prior knowledge of the propagation characteristics of the region of interest, we first examine the potential gains of this non-uniform sampling strategy in different cases via a measurement-based statistical analysis methodology; this involves irregular spatial tessellation of the region of interest into sub-regions with internally similar radio propagation characteristics and sampling based on these sub-regions. We then present a practical form of ZipWeave nonuniform sampling strategy that can be used even without any prior information. In all our evaluations, we show that the ZipWeave non-uniform sampling approach reduces the samples by half compared to the common systematic-random sampling, while maintaining similar accuracy. Moreover, we show that the other key feature of ZipWeave to combine high-quality controlled measurements (that present limited geographic footprint similar to drive tests) with crowdsourced measurements (that cover a wider footprint) leads to more reliable mobile coverage maps overall.

Open experimentation with operational Mobile Broadband (MBB) networks in the wild is currently a fundamental requirement of the research community in its endeavor to address the need of innovative solutions for mobile communications. Even more, there is a strong need for objective data about stability and performance of MBB (e.g., 3G/4G) networks, and for tools that rigorously and scientifically assess their status. In this paper, we introduce the MONROE measurement platform: an open access and flexible hardware-based platform for measurements and custom experimentation on operational MBB networks. The MONROE platform enables accurate, realistic and meaningful assessment of the performance and reliability of 11 MBB networks in Europe. We report on our experience designing, implementing and testing the solution we propose for the platform. We detail the challenges we overcame while building and testing the MONROE testbed and argue our design and implementation choices accordingly. We describe and exemplify the capabilities of the platform and the wide variety of experiments that external users already perform using the system.

Crowdsourcing mobile network performance evaluation is rapidly gaining popularity, with new applications aiming to deliver more accurate and reliable results every day. From the perspective of end-users, these utilities help them estimate the performance of their service provider in terms of throughput, latency and other key performance indicators of the network. In this paper, we build ORCA: Operator Classifier, a Machine Learning (ML) based framework to define and determine the behavior of Mobile Network Operators (MNOs) from crowdsourced datasets. We investigate whether one can differentiate MNOs by using crowdsourced end-to-end network measurements. We consider different performance metrics (e.g. Download (DL)/Upload (UL) data rate, latency, signal strength) and study the impact of them individually but also collectively on differentiating MNOs. We use RTR Open Data, an open dataset of broadband measurements provided by the Austrian Regulatory Authority for Broadcasting and Telecommunications (RTR), to characterize the three major mobile native operators and two virtual operators in Austria. Our results show that ORCA can be used to identify patterns between various mobile systems and disclose their differences from the end-user perspective.

Mobile broadband (MBB) networks underpin numerous vital operations of the society and are arguably becoming the most important piece of the communications infrastructure. In this demo paper, our goal is to showcase the potential of a novel multi-homed MBB platform for measuring, monitoring and assessing the performance of MBB services in an objective manner. Our platform, MONROE, is composed of hundreds of nodes scattered over four European countries and a backend system that collects the measurement results. Through a user-friendly web client, the experimenters can schedule and deploy their experiments. The platform further embeds traffic analysis tools for real-time traffic flow analysis and a powerful visualization tool.

In this paper, we propose the BGP Visibility Toolkit, a system for detecting and analyzing anomalous behavior in the Internet. We show that interdomain prefix visibility can be used to single out cases of erroneous demeanors resulting from misconfiguration or bogus routing policies. The implementation of routing policies with BGP is a complicated process, involving fine-tuning operations and interactions with the policies of the other active ASes. Network operators might end up with faulty configurations or unintended routing policies that prevent the success of their strategies and impact their revenues. As part of the Visibility Toolkit, we propose the BGP Visibility Scanner, a tool which identifies limited visibility prefixes in the Internet. The tool enables operators to provide feedback on the expected visibility status of prefixes. We build a unique set of ground-truth prefixes qualified by their ASes as intended or unintended to have limited visibility. Using a machine learning algorithm, we train on this unique dataset an alarm system that separates with 95% accuracy the prefixes with unintended limited visibility. Hence, we find that visibility features are generally powerful to detect prefixes which are suffering from inadvertent effects of routing policies. Limited visibility could render a whole prefix globally unreachable. This points towards a serious problem, as limited reachability of a non-negligible set of prefixes undermines the global connectivity of the Internet. We thus verify the correlation between global visibility and global connectivity of prefixes.

In this paper, we propose the use of prefix visibility at the interdomain level as an early symptom of anomalous events in the Internet. We focus on detecting anomalies which, despite their significant impact on the routing system, remain concealed from state of the art tools. We design a machine learning system to winnow the prefixes with unintended limited visibility - symptomatic of anomalous events - from the prefixes with intended limited visibility - resulting from legitimate routing operations. We train a winnowing algorithm with ground-truth data on 20,000 operational limited visibility prefixes (LVPs) already classified by the operators of the origin networks. The ground-truth was collected using the BGP Visibility Scanner, a tool we developed to provide operators with a multi-angle view on the efficacy of their routing policies. We build a dataset with the pre-classified prefixes and the features describing their visibility status dynamics. We further use this dataset to derive a boosted decision tree which winnows unintended LVPs with an accuracy of 95%.

By tweaking the BGP configurations, the network operators are able to express their interdomain routing preferences, designed to accommodate a myriad goals. Given the complex interactions between policies in the Internet, the origin AS by itself cannot ensure that only by configuring a routing policy it can also achieve the anticipated results. Moreover, the definition of routing policies is a complicated process, involving a number of subtle tuning operations prone to errors. In this paper, we propose the BGP Visibility Scanner which allows network operators to validate the correct implementation of their routing policies, by corroborating the BGP routing information from approximatively 130 independent observation points in the Internet. We exemplify the use of the proposed methodology and also perform an initial validation for the BGP Visibility Scanner capabilities through various real operational use cases.

Over the last years, the research community has been deeply concerned about the scalability issues that the Internet routing is facing. In this paper we study the economic incentives for the Global Routing Table (GRT) explosive growth by considering a commons model in which the GRT is a public resource. In particular, we analyze the motivations the ASes have for deaggregating their assigned address blocks. We evaluate the efficiency of the global routing system, the properties of the game equilibria and we examine its relation to the social welfare point of the considered game setup. We prove that the GRT, just like any common natural resource, "remorselessly generates tragedy", following Hardin's game theoretic analysis on the tragedy of the commons. Finally, we introduce in the model a pricing mechanism that aims to avoid the tragedy of the Internet routing commons.