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.

Recent work highlights how gradient-level access can lead to successful inference and reconstruction attacks against federated learning (FL). In such settings, differentially private (DP) learning is known to provide resilience. However, approaches used in the status quo (i.e., central and local DP) introduce disparate utility vs. privacy trade-offs. In this work, we mitigate such trade-offs through hierarchical FL (HFL). For the first time, we demonstrate that by the introduction of a new intermediary level where calibrated noise can be added, better trade-offs can be obtained; we term this hierarchical DP (HDP). Our experiments with 3 different datasets (commonly used as benchmarks for FL in prior works) suggest that HDP produces models as accurate as those obtained using central DP, where noise is added at a central aggregator at a lower privacy budget.

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.

Internet access is a special resource of which needs has become universal across the public whereas the service is operated in the private sector. Mobile Network Operators (MNOs) put efforts for management, planning, and optimization; however, they do not link such activities to socioeconomic fairness. In this paper, we make a first step towards understanding the relation between socioeconomic status of customers and network performance, and investigate potential discrimination in network deployment and management. The scope of our study spans various aspects, including urban geography, network resource deployment, data consumption, and device distribution. A novel methodology that enables a geo-socioeconomic perspective to mobile network is developed for the study. The results are based on an actual infrastructure in multiple cities, covering millions of users densely covering the socioeconomic scale. We report a thorough examination of the fairness status, its relationship with various structural factors, and potential class specific solutions.

A machine learning model can often produce biased outputs for a familiar group or similar sets of classes during inference over an unknown dataset. The generalization of neural networks have been studied to resolve biases, which has also shown improvement in accuracy and performance metrics, such as precision and recall, and refining the dataset's validation set. Data distribution and instances included in test and validation-set play a significant role in improving the generalization of neural networks. For producing an unbiased AI model, it should not only be trained to achieve high accuracy and minimize false positives. The goal should be to prevent the dominance of one class/feature over the other class/feature while calculating weights. This paper investigates state-of-art object detection/classification on AI models using metrics such as selectivity score and cosine similarity. We focus on perception tasks for vehicular edge scenarios, which generally include collaborative tasks and model updates based on weights. The analysis is performed using cases that include the difference in data diversity, the viewpoint of the input class and combinations. Our results show the potential of using cosine similarity, selectivity score and invariance for measuring the training bias, which sheds light on developing unbiased AI models for future vehicular edge services.

LEO satellite "mega-constellations" such as SpaceX's Starlink, Amazon's Kuiper, OneWeb are launching thousands of satellites annually, promising high-bandwidth low-latency connectivity. To quantify the achievable performance of such providers, we carry out a measurement study of the spatial and temporal characteristics as well as the geographic variability of the connectivity provided by Starlink, the current leader in this space. We do this by building and deploying a browser extension that provides data about web performance seen by 28 users from 10 cities across the world. We complement this with performance tests run from three measurement nodes hosted by volunteer enthusiasts in the UK, EU and USA. Our findings suggest that although Starlink offers some of the best web performance figures among the ISPs observed, there are important sources of variability in performance such as weather conditions. The bent-pipe connection to a satellite and back to earth also forms a significant component of the observed latency. We also observe frequent and significant packet losses of up to 50% of packets, which appear to be correlated with handovers between satellites. This has an effect on achievable throughput even when using modern congestion control protocols such as BBR or CUBIC.

Federated Learning (FL) [2] has emerged as a popular solution of Confidential Computing [3] to distributedly train a model on user devices, improving privacy and system scalability. Such privacy-preserving models can be used in wide range of applications, and especially in Telco networks [4]. However, there are no practical systems to easily enable FL training on mobile apps, and especially in an as-a-service fashion. In this demo, we implement and test FLaaS, our recently proposed end-to-end FL service [1]. FLaaS includes a client-side framework with app library and service, and a back-end server, to enable secure and easy to deploy intra- and inter-app FL model training on mobile environments.

We propose and implement a Privacy-preserving Federated Learning ( PPFL ) framework for mobile systems to limit privacy leakages in federated learning. Leveraging the widespread presence of Trusted Execution Environments (TEEs) in high-end and mobile devices, we utilize TEEs on clients for local training, and on servers for secure aggregation, so that model/gradient updates are hidden from adversaries. Challenged by the limited memory size of current TEEs, we leverage greedy layer-wise training to train each model's layer inside the trusted area until its convergence. The performance evaluation of our implementation shows that PPFL can significantly improve privacy while incurring small system overheads at the client-side. In particular, PPFL can successfully defend the trained model against data reconstruction, property inference, and membership inference attacks. Furthermore, it can achieve comparable model utility with fewer communication rounds (0.54×) and a similar amount of network traffic (1.002×) compared to the standard federated learning of a complete model. This is achieved while only introducing up to ~15% CPU time, ~18% memory usage, and ~21% energy consumption overhead in PPFL's client-side.

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.

The prevalence of smart wearable devices is increasing exponentially and we are witnessing a wide variety of fascinating new services that leverage the capabilities of these wearables. Wearables are truly changing the way mobile computing is deployed and mobile apps are being developed. It is possible to leverage the capabilities such as connectivity, processing, and sensing of wearable devices in an adaptive manner for efficient resource usage and information accuracy within the personal area network. We show that app developers are not yet taking advantage of these cross-device capabilities, however, instead using wearables as passive sensors or simple end displays to provide notifications to the user. We thus design Application Function Virtualization (AFV), an architecture enabling automated dynamic function virtualization and scheduling across devices in a personal area network, simplifying the development of the apps that are adaptive to context changes. AFV provides a simple set of APIs hiding complex architectural tasks from app developers whilst continuously monitoring the user, device, and network context, to enable the adaptive invocation of functions across devices. We show the feasibility of our design by implementing AFV on Android, and the benefits for the user in terms of resource efficiency, especially in saving energy consumption, and quality of experience with multiple use cases.

Free web proxies promise anonymity and censorship circumvention at no cost. Several websites publish lists of free proxies organized by country, anonymity level, and performance. These lists index hundreds of thousands of hosts discovered via automated tools and crowd-sourcing. A complex free proxy ecosystem has been forming over the years, of which very little is known. In this article, we shed light on this ecosystem via a distributed measurement platform that leverages both active and passive measurements. Active measurements are carried out by an infrastructure we name ProxyTorrent, which discovers free proxies, assesses their performance, and detects potential malicious activities. Passive measurements focus on proxy performance and usage in the wild, and are accomplished by means of a Chrome extension named Ciao. ProxyTorrent has been running since January 2017, monitoring up to 230K free proxies. Ciao was launched in March 2017 and has thus far served roughly 9.7K users and generated 14TB of traffic. Our analysis shows that less than 2% of the proxies announced on the Web indeed proxy traffic on behalf of users; further, only half of these proxies have decent performance and can be used reliably. Every day, around 5%--10% of the active proxies exhibit malicious behaviors, e.g., advertisement injection, TLS interception, and cryptojacking, and these proxies are also the ones providing the best performance. Through the analysis of more than 14TB of proxied traffic, we show that web browsing is the primary user activity. Geo-blocking avoidance—allegedly a popular use case for free web proxies—accounts for 30% or less of the traffic, and it mostly involves countries hosting popular geo-blocked content.

Free web proxies promise anonymity and censorship circumvention at no cost. Several websites publish lists of free proxies organized by country, anonymity level, and performance. These lists index hundreds of thousand of hosts discovered via automated tools and crowd-sourcing. A complex free proxy ecosystem has been forming over the years, of which very little is known. In this paper we shed light on this ecosystem via ProxyTorrent, a distributed measurement platform that leverages both active and passive measurements. Active measurements discover free proxies, assess their performance, and detect potential malicious activities. Passive measurements relate to proxy performance and usage in the wild, and are collected by free proxies users via a Chrome plugin we developed. ProxyTorrent has been running since January 2017, monitoring up to 180,000 free proxies and totaling more than 1,500 users over a 10 months period. Our analysis shows that less than 2% of the proxies announced on the Web indeed proxy traffic on behalf of users; further, only half of these proxies have decent performance and can be used reliably. Around 10% of the working proxies exhibit malicious behaviors, e.g., ads injection and TLS interception, and these proxies are also the ones providing the best performance. Through the analysis of more than 2 Terabytes of proxied traffic, we show that web browsing is the primary user activity. Geo-blocking avoidance is not a prominent use-case, with the exception of proxies located in countries hosting popular geo-blocked content.

The functioning of mobile apps involves a large number of protocols and entities, with the Domain Name System (DNS) acting as a predominant one. Despite being one of the oldest Internet systems, DNS still operates with semi-obscure interactions among its stakeholders: domain owners, network operators, operating systems, and app developers. The goal of this work is to holistically understand the dynamics of DNS in mobile traffic along with the role of each of its stakeholders. We use two complementary (anonymized) datasets: traffic logs provided by a European mobile network operator (MNO) with 19M customers, and traffic logs from 5,000 users of Lumen, a traffic monitoring app for Android. We complement such passive traffic analysis with active measurements at four European MNOs. Our study reveals that 10k domains (out of 198M) account for 87% of total network flows. The time to live (TTL) values for such domains are mostly short (< 1min), despite domain-to-IPs mapping tends to change on a longer time-scale. Further, depending on the operators recursive resolver architecture, end-user devices receive even smaller TTL values leading to suboptimal effectiveness of the on-device DNS cache. Despite a number of on-device and in-network optimizations available to minimize DNS overhead, which we find corresponding to 10% of page load time (PLT) on average, we have not found wide evidence of their adoption in the wild.

As researchers, we are aware of how hard it is to obtain access to vantage points in the Internet. Experimentation platforms are useful tools, but they are also: 1) paid, either via a membership fee or by resource sharing, 2) unreliable, nodes come and go, 3) outdated, often still run on their original hardware and OS. While one could build yet-another platform with up-to-date and reliable hardware and software, it is hard to imagine one which is free. This is the goal of this paper: we set out to build FreeLab, a free experimentation platform which also aims to be reliable and up-to-date. The key idea behind FreeLab is that experiments run directly at its user machines, while traffic is relayed by free vantage points in the Internet (web and SOCKS proxies, and DNS resolvers). FreeLab is thus free of access by design and up-to-date as far as its users maintain their experimenting machines. Reliability is a key challenge due to the volatile nature of free resources, and the introduction of errors (path inflation, header manipulation, bandwidth shrinkage) caused by traffic relays.

The majority of available wearable computing devices require communication with Internet servers for data analysis and storage, and rely on a paired smartphone to enable secure communication. However, many wearables are equipped with WiFi network interfaces, enabling direct communication with the Internet. Secure communication protocols could then run on these wearables themselves, yet it is not clear if they can be efficiently supported.,,,,In this paper, we show that wearables are ready for direct and secure Internet communication by means of experiments with both controlled local web servers and Internet servers. We observe that the overall energy consumption and communication delay can be reduced with direct Internet connection via WiFi from wearables compared to using smartphones as relays via Bluetooth. We also show that the additional HTTPS cost caused by TLS handshake and encryption is closely related to the number of parallel connections, and has the same relative impact on wearables and smartphones.

Smart wearable devices are widely available today and changing the way mobile applications are being developed. Applications can dynamically leverage the capabilities of wearable devices worn by the user for optimal resource usage and information accuracy, depending on the user/device context and application requirements. However, application developers are not yet taking advantage of these cross-device capabilities.

The upcoming 5G architecture is expected to heavily rely on network functions implemented by software deployed on commodity hardware architectures. Multiple standardization efforts are underway to specify interfaces between virtualized and real infrastructure, and procedures for interoperability among functions. However, the practical feasibility of function implementation in such abstract and disembodied conditions is scarcely covered in the latest literature. In this paper, we argue for a Network Function Virtualization (NFV) framework that provides 5G network functions built around a modular software router model, rather than following the traditional VM-container approaches. We illustrate its advantages in enabling support for efficient processing on heterogeneous hardware and in ensuring consistency of flow/session semantics across distributed 5G data planes. Finally, we report on the state of Programmable Data Plane, our architecture to implement 5G network functions as modular pipelines orchestrated across multiple devices.

The rapid traffic growth fueled by mobile devices spread and high speed network access calls for substantial innovation at network layer. The content-centric nature of Internet usage highlights the limitations of the host-centric model in coping with dynamic content-to-location binding, mobility, multicast, multi-homing, etc. If transmission capacity speedups in the backhaul may hide inefficiencies in the short term, the hostcentric communication model needs to be revisited to sustain future mobile demand. In this paper, we first identify and quantify the opportunities for backhaul evolution by analyzing a large set of traffic measurements collected between mobile core and backhaul of Orange France. The analysis reveals that 50% of HTTP requests are cacheable and traffic can be reduced from 60% to 95% during the peak hour by using 350GBs to 1TB of memory overall. Motivated by such significant opportunities for latency reduction and network cost savings, we present a solution based on Information-Centric Networking (ICN). First results of a large scale experimentation with 100 Linux servers and customized software, in a realistic network setting, provide a glimpse into ICN gains even under naive caching: a factor three reduction in delivery time and almost 40% bandwidth savings, when compared to existing alternatives.

Consider a network of prosumers of media content in which users dynamically create and request content objects. The request process is governed by the objects' popularity, which may vary across network regions and over time. In order to meet user requests, content objects can be stored and transported over the network, characterized by the capacity and efficiency of its storage and transport resources. The energy-efficient dynamic content distribution problem aims at finding the evolution of the network configuration, in terms of the placement and routing of content objects over time, that meets user requests, satisfies network resource capacities and minimizes overall energy use. We present 1) an information-centric linear programming formulation for the energy efficient dynamic content distribution problem that captures multicasting and caching over the network, per-object system dynamics, and delivery deadlines; 2) an offline solution that characterizes the minimum energy use achievable with global knowledge of user requests and network resources; and 3) an efficient distributed online solution that allows network nodes to make caching decisions based on their local estimate of the global energy benefit. Using a custom-built content distribution network simulator as well as a real prototype implementation in an information-centric networking testbed, we show the significant energy savings that can be obtained via the efficient and lightweight cache cooperation induced by our service and energy aware distributed online solution with respect to state of the art approaches.

Internet users are interested in content regardless of its location; however, the current client/server architecture still requires requests to be directed to a specific server. Information-centric networking (ICN) is a recent vein that relaxes this requirement through the use of name-based forwarding, where forwarding decisions are based on content names instead of IP addresses. Despite previous name-based forwarding strategies have been proposed, almost none have actually built a content router. To fill this gap, in this paper we design and prototype a content router called Caesar for high-speed forwarding on content names. Caesar introduces several innovative features, including (i) a longest-prefix matching algorithm based on a novel data structure called prefix Bloom filter; (ii) an incremental design which allows for easy integration with existing protocols and network equipment;(iii) a forwarding scheme where multiple line cards collaborate in a distributed fashion; and (iv) support for offloading packet processing to graphics processing units (GPUs). We build Caesar as an enterprise router, and show that every line card sustains up to 10 Gbps using a forwarding table with more than 10 million content prefixes. Distributed forwarding allows the forwarding table to grow even further, and to scale linearly with the number of line cards at the cost of only a few microseconds in the packet processing latency. GPU offloading, in turn, trades off a few milliseconds of latency for a large speedup in the forwarding rate.

Content-Centric Networking is a new communication architecture that rethinks the Internet communication model, designed for point-to-point connections between hosts, and centers it around content dissemination and retrieval. Most of the issues faced by the current IP infrastructure in terms of mobility management, security, scalability, which are accrued by today's Internet trends, find a natural solution in CCN shift from IP addresses to named data. In this paper we explore the impact of storage management on the performance of multiple applications sharing the same CCN infrastructure and we quantify the effectiveness of static storage partitioning and dynamic management techniques in providing service differentiation. To this purpose, we implement a set of storage management techniques in the open source CCNx prototype and perform extensive experiments in a real testbed under fairly realistic network conditions. Our experimental results allow to clarify the relation between CCN chunk-level caching and Quality of Experience (QoE) perceived by end users.

Optimal dissemination schemes have previously been studied for peer-to-peer live streaming applications. Live streaming being a delay-sensitive application, fine tuning of dissemination parameters is crucial. In this paper, we investigate optimal sizing of chunks, the units of data exchange, and probe sets, the number peers a given node probes before transmitting chunks. Chunk size can have significant impact on diffusion rate (chunk miss ratio), diffusion delay, and overhead. The size of the probe set can also affect these metrics, primarily through the choices available for chunk dissemination. We perform extensive simulations on the so-called random-peer, latest-useful dissemination scheme. Our results show that size does matter, with the optimal size being not too small in both cases.

We have currently reached a phase where big shifts in the network traffic might impose to rethink the design of current architectures, and where new technologies, being pushed into market, will act as enabler of such changes. Taking into account the current scenario and its likely evolution as well, in this paper we examine the case for multi-path routing within the metropolitan access network. Through an optimization framework, we undertake the analysis of several interesting aspects of the problem, such as (i) the user access technology, (ii) the topology of the access network and (iii) the traffic locality ratio within the access. By numerical solution of the problem we quantify the potential gain given by path-diversity: our results confirm the appeal of multi-path routing strategies both from the user and the network perspectives.

In a distributed Video-on-Demand system, customers are in charge of storing the video catalog, and they actively participate in serving video requests generated by other customers. The design of such systems is driven by key constraints like customer upload and storage capacities, video popularity distribution, and so on. In this paper, we analyze by simulations the impact of: i) the video allocation technique (used for distributed storage) ii) the use of a cache that allows nodes to redistribute the video they are using iii) the use of static/dynamic algorithms for video distribution. Based on these results, we provide some guidelines for setting the system parameters: the use of cache strongly improves system performance; popularity based allocation techniques can be sensitive and bring little improvement; dynamic distribution algorithms are needed only in extreme scenarios while static ones are generally sufficient.

We consider the fully distributed video-on-demand problem, where n nodes called boxes store a large set of videos and collaborate to serve simultaneously n videos or less between them. It is said to be scalable when Omega (n) videos can be distributively stored under the condition that any sequence of demands for these videos can always be satisfied. Our main result consists in establishing a threshold on the average upload bandwidth of a box, above which the system becomes scalable. We are thus interested in the normalized upload capacity u = upload bandwidth/video bitrate of a box. The number m of distinct videos stored in the system is called its catalog size. We show an upload capacity threshold of 1 for scalability in a homogeneous system, where all boxes have the same upload capacity. More precisely, a system with u1, an homogeneous system where all boxes have same upload capacity at least u admits a static allocation of m = Omega (n) videos into the boxes such that any adversarial sequence of video demands can be satisfied. Moreover, such an allocation can be obtained randomly with high probability. This result is generalized to a system of boxes that have heterogeneous upload capacities under some balancing conditions.

Multi-path routing is a valuable on-line technique to deal with unpredictable and variable traffic patters, mostly for intra-domain TE, multi-homing, wireless mesh networks, metropolitan access networks, and has been shown efficient for a large spectrum of future traffic scenarios. In this paper we analyze the performance of MIRTO, TEXCP and TRUMP, three recently proposed multi-path routing algorithms. Modeling of such algorithms is performed through fluid models, based on ordinary differential equations (ODEs). On a US-like backbone network, with and without in-network fair queuing schedulers, TEXCP and TRUMP show faster convergence times while MIRTO, that relies on simpler feedbacks, consumes less network resources.

We investigate performance bounds of P2P systems by application of the law of bandwidth conservation. This approach is quite general and allows us to consider various sharing systems such as fixed-rate streaming, VoD-type streaming, and elastic file sharing. Starting from a general law of bandwidth conservation, we consider several specific cases that apply to various P2P systems. For dynamic systems with a stationary arrival process, we show that simple seeding policies result in regimes where the download rates are arbitrarily fast. We consider a case with equal download rate among all peers as well as cases where the download rate is a function of upload rates, inspired by BitTorrent's tit-for-tat policy. In particular, we show that the sustainable proportion of free-riders is closely related to the tit-for-tat parameter.

Several peer-to-peer systems for live streaming have been recently deployed (e.g. CoolStreaming, PPLive, SopCast). These all rely on distributed, epidemic-style dissemination mechanisms. Despite their popularity, the fundamental performance trade-offs of such mechanisms are still poorly understood. In this paper we propose several results that contribute to the understanding of such trade-offs.

Large-scale live media streaming is a challenge for traditional server-based approaches. To appropriately support big audiences, broadcasters must be able to allocate huge bandwidth and computational resources. The costs involved with such an infrastructure exclude all but the established content producers from exploiting the Internet as a distribution medium. Publishers of not-yet-popular content, unless they manage to properly predict their maximum audience size, will likely fail to dimension correctly their broadcast infrastructure. Peer-to-peer systems for live streaming allow the users to support content distribution by contributing their unused resources: this increases the scalability of the content distribution while reducing at the same time the economical burden on the streaming provider. This paper presents and evaluates PULSE, an unstructured mesh-based peer-to-peer system designed to support live streaming to large audiences under the arbitrary resource availability as is typically the case for the Internet. PULSE is a highly dynamic system: it constantly optimizes its mesh of data connections using a feedback-driven peer selection strategy that is based on pairwise incentives. We evaluate the behavior of PULSE under realistic scenarios via simulation and emulation, and present the advantages of our approach, namely a best-effort response to system-wide resource scarcity, high resilience to node churn, and good hop-count properties of the average data distribution paths.