Network Neutrality, Consumers, and Innovation

In this Article, Professor Christopher Yoo directly engages claims that mandating network neutrality is essential to protect consumers and to promote innovation on the Internet. It begins by analyzing the forces that are placing pressure on the basic network architecture to evolve, such as the emergence of Internet video and peer-to-peer architectures and the increasing heterogeneity in business relationships and transmission technologies. It then draws on the insights of demand-side price discrimination (such as Ramsey pricing) and the two-sided markets, as well as the economics of product differentiation and congestion, to show how deviating from network neutrality can benefit consumers, a conclusion bolstered by the empirical literature showing that vertical restraints tend to increase rather than reduce consumer welfare. In fact, limiting network providers’ ability to vary the prices charged to content and applications providers may actually force consumers to bear a greater proportion of the costs to upgrade the network. Restricting network providers’ ability to experiment with different protocols may also reduce innovation by foreclosing applications and content that depend on a different network architecture and by dampening the price signals needed to stimulate investment in new applications and content. In the process, Professor Yoo draws on the distinction between generalizing and exemplifying theory to address some of the arguments advanced by his critics. While the exemplifying theories on which these critics rely are useful for rebutting calls for broad, categorical, ex ante rules, their restrictive nature leaves them ill suited to serve as the foundation for broad, categorical ex ante mandates pointing in the other direction. Thus, in the absence of some empirical showing that the factual preconditions of any particular exemplifying theory have been satisfied, the existence of exemplifying theories pointing in both directions actually supports an ex post, case-by-case approach that allows network providers to experiment with different pricing regimes unless and until a concrete harm to competition can be shown

Network Neutrality, Consumers, and Innovation

In this Article, Professor Christopher Yoo directly engages claims that mandating network neutrality is essential to protect consumers and to promote innovation on the Internet. It begins by analyzing the forces that are placing pressure on the basic network architecture to evolve, such as the emergence of Internet video and peer-to-peer architectures and the increasing heterogeneity in business relationships and transmission technologies. It then draws on the insights of demand-side price discrimination (such as Ramsey pricing) and the two-sided markets, as well as the economics of product differentiation and congestion, to show how deviating from network neutrality can benefit consumers, a conclusion bolstered by the empirical literature showing that vertical restraints tend to increase rather than reduce consumer welfare. In fact, limiting network providers’ ability to vary the prices charged to content and applications providers may actually force consumers to bear a greater proportion of the costs to upgrade the network. Restricting network providers’ ability to experiment with different protocols may also reduce innovation by foreclosing applications and content that depend on a different network architecture and by dampening the price signals needed to stimulate investment in new applications and content. In the process, Professor Yoo draws on the distinction between generalizing and exemplifying theory to address some of the arguments advanced by his critics. While the exemplifying theories on which these critics rely are useful for rebutting calls for broad, categorical, ex ante rules, their restrictive nature leaves them ill suited to serve as the foundation for broad, categorical ex ante mandates pointing in the other direction. Thus, in the absence of some empirical showing that the factual preconditions of any particular exemplifying theory have been satisfied, the existence of exemplifying theories pointing in both directions actually supports an ex post, case-by-case approach that allows network providers to experiment with different pricing regimes unless and until a concrete harm to competition can be shown

A framework to provide charging for third party composite services

Includes synopsis.Includes bibliographical references (leaves 81-87).Over the past few years the trend in the telecommunications industry has been geared towards offering new and innovative services to end users. A decade ago network operators were content with offering simple services such as voice and text messaging. However, they began to notice that these services were generating lower revenues even while the number of subscribers increased. This was a direct result of the market saturation and network operators were forced to rapidly deploy services with minimum capital investment and while maximising revenue from service usage by end users. Network operators can achieve this by exposing the network to external content and service providers. They would create interfaces that would allow these 3rd party service and content providers to offer their applications and services to users. Composing and bundling of these services will essentially create new services for the user and achieve rapid deployment of enhanced services. The concept of offering a wide range of services that are coordinated in such a way that they deliver a unique experience has sparked interest and numerous research on Service Delivery Platforms (SDP). SDP‟s will enable network operators to be able to develop and offer a wide-variety service set. Given this interest on SDP standardisation bodies such as International Telecommunications Union – Telecommunications (ITU-T), Telecoms and Internet converged Servicers and Protocols for Advanced Networks) (TISPAN), 3rd Generations Partnership Project (3GPP) and Open Mobile Alliance (OMA) are leading efforts into standardising functions and protocols to enhance service delivery by network operators. Obtaining revenue from these services requires effective accounting of service usage and requires mechanisms for billing and charging of these services. The IP Multimedia subsystem(IMS) is a Next Generation Network (NGN) architecture that provides a platform for which multimedia services can be developed and deployed by network operators. The IMS provides network operators, both fixed or mobile, with a control layer that allows them to offer services that will enable them to remain key role players within the industry. Achieving this in an environment where the network operator interacts directly with the 3rd party service providers may become complicated

Systems-compatible Incentives

Originally, the Internet was a technological playground, a collaborative endeavor among researchers who shared the common goal of achieving communication. Self-interest used not to be a concern, but the motivations of the Internet's participants have broadened. Today, the Internet consists of millions of commercial entities and nearly 2 billion users, who often have conflicting goals. For example, while Facebook gives users the illusion of access control, users do not have the ability to control how the personal data they upload is shared or sold by Facebook. Even in BitTorrent, where all users seemingly have the same motivation of downloading a file as quickly as possible, users can subvert the protocol to download more quickly without giving their fair share. These examples demonstrate that protocols that are merely technologically proficient are not enough. Successful networked systems must account for potentially competing interests. In this dissertation, I demonstrate how to build systems that give users incentives to follow the systems' protocols. To achieve incentive-compatible systems, I apply mechanisms from game theory and auction theory to protocol design. This approach has been considered in prior literature, but unfortunately has resulted in few real, deployed systems with incentives to cooperate. I identify the primary challenge in applying mechanism design and game theory to large-scale systems: the goals and assumptions of economic mechanisms often do not match those of networked systems. For example, while auction theory may assume a centralized clearing house, there is no analog in a decentralized system seeking to avoid single points of failure or centralized policies. Similarly, game theory often assumes that each player is able to observe everyone else's actions, or at the very least know how many other players there are, but maintaining perfect system-wide information is impossible in most systems. In other words, not all incentive mechanisms are systems-compatible. The main contribution of this dissertation is the design, implementation, and evaluation of various systems-compatible incentive mechanisms and their application to a wide range of deployable systems. These systems include BitTorrent, which is used to distribute a large file to a large number of downloaders, PeerWise, which leverages user cooperation to achieve lower latencies in Internet routing, and Hoodnets, a new system I present that allows users to share their cellular data access to obtain greater bandwidth on their mobile devices. Each of these systems represents a different point in the design space of systems-compatible incentives. Taken together, along with their implementations and evaluations, these systems demonstrate that systems-compatibility is crucial in achieving practical incentives in real systems. I present design principles outlining how to achieve systems-compatible incentives, which may serve an even broader range of systems than considered herein. I conclude this dissertation with what I consider to be the most important open problems in aligning the competing interests of the Internet's participants

Economic issues in distributed computing

textOn the Internet, one of the essential characteristics of electronic commerce is the integration of large-scale computer networks and business practices. Commercial servers are connected through open and complex communication technologies, and online consumers access the services with virtually unpredictable behavior. Both of them as well as the e-Commerce infrastructure are vulnerable to cyber attacks. Among the various network security problems, the Distributed Denial-of-Service (DDoS) attack is a unique example to illustrate the risk of commercial network applications. Using a massive junk traffic, literally anyone on the Internet can launch a DDoS attack to flood and shutdown an eCommerce website. Cooperative technological solutions for Distributed Denial-of-Service (DDoS) attacks are already available, yet organizations in the best position to implement them lack incentive to do so, and the victims of DDoS attacks cannot find effective methods to motivate the organizations. Chapter 1 discusses two components of the technological solutions to DDoS attacks: cooperative filtering and cooperative traffic smoothing by caching, and then analyzes the broken incentive chain in each of these technological solutions. As a remedy, I propose usage-based pricing and Capacity Provision Networks, which enable victims to disseminate enough incentive along attack paths to stimulate cooperation against DDoS attacks. Chapter 2 addresses possible Distributed Denial-of-Service (DDoS) attacks toward the wireless Internet including the Wireless Extended Internet, the Wireless Portal Network, and the Wireless Ad Hoc network. I propose a conceptual model for defending against DDoS attacks on the wireless Internet, which incorporates both cooperative technological solutions and economic incentive mechanisms built on usage-based fees. Cost-effectiveness is also addressed through an illustrative implementation scheme using Policy Based Networking (PBN). By investigating both technological and economic difficulties in defense of DDoS attacks which have plagued the wired Internet, our aim here is to foster further development of wireless Internet infrastructure as a more secure and efficient platform for mobile commerce. To avoid centralized resources and performance bottlenecks, online peer-to-peer communities and online social network have become increasingly popular. In particular, the recent boost of online peer-to-peer communities has led to exponential growth in sharing of user-contributed content which has brought profound changes to business and economic practices. Understanding the dynamics and sustainability of such peer-to-peer communities has important implications for business managers. In Chapter 3, I explore the structure of online sharing communities from a dynamic process perspective. I build an evolutionary game model to capture the dynamics of online peer-to-peer communities. Using online music sharing data collected from one of the IRC Channels for over five years, I empirically investigate the model which underlies the dynamics of the music sharing community. Our empirical results show strong support for the evolutionary process of the community. I find that the two major parties in the community, namely sharers and downloaders, are influencing each other in their dynamics of evolvement in the community. These dynamics reveal the mechanism through which peer-to-peer communities sustain and thrive in a constant changing environment.Information, Risk, and Operations Management (IROM

Video Caching, Analytics and Delivery at the Wireless Edge: A Survey and Future Directions

Future wireless networks will provide high bandwidth, low-latency, and ultra-reliable Internet connectivity to meet the requirements of different applications, ranging from mobile broadband to the Internet of Things. To this aim, mobile edge caching, computing, and communication (edge-C3) have emerged to bring network resources (i.e., bandwidth, storage, and computing) closer to end users. Edge-C3 allows improving the network resource utilization as well as the quality of experience (QoE) of end users. Recently, several video-oriented mobile applications (e.g., live content sharing, gaming, and augmented reality) have leveraged edge-C3 in diverse scenarios involving video streaming in both the downlink and the uplink. Hence, a large number of recent works have studied the implications of video analysis and streaming through edge-C3. This article presents an in-depth survey on video edge-C3 challenges and state-of-the-art solutions in next-generation wireless and mobile networks. Specifically, it includes: a tutorial on video streaming in mobile networks (e.g., video encoding and adaptive bitrate streaming); an overview of mobile network architectures, enabling technologies, and applications for video edge-C3; video edge computing and analytics in uplink scenarios (e.g., architectures, analytics, and applications); and video edge caching, computing and communication methods in downlink scenarios (e.g., collaborative, popularity-based, and context-aware). A new taxonomy for video edge-C3 is proposed and the major contributions of recent studies are first highlighted and then systematically compared. Finally, several open problems and key challenges for future research are outlined

Intelligence artificielle à la périphérie du réseau mobile avec efficacité de communication

L'intelligence artificielle (AI) et l'informatique à la périphérie du réseau (EC) ont permis de mettre en place diverses applications intelligentes incluant les maisons intelligentes, la fabrication intelligente, et les villes intelligentes. Ces progrès ont été alimentés principalement par la disponibilité d'un plus grand nombre de données, l'abondance de la puissance de calcul et les progrès de plusieurs techniques de compression. Toutefois, les principales avancées concernent le déploiement de modèles dans les dispositifs connectés. Ces modèles sont préalablement entraînés de manière centralisée. Cette prémisse exige que toutes les données générées par les dispositifs soient envoyées à un serveur centralisé, ce qui pose plusieurs problèmes de confidentialité et crée une surcharge de communication importante. Par conséquent, pour les derniers pas vers l'AI dans EC, il faut également propulser l'apprentissage des modèles ML à la périphérie du réseau. L'apprentissage fédéré (FL) est apparu comme une technique prometteuse pour l'apprentissage collaboratif de modèles ML sur des dispositifs connectés. Les dispositifs entraînent un modèle partagé sur leurs données stockées localement et ne partagent que les paramètres résultants avec une entité centralisée. Cependant, pour permettre l' utilisation de FL dans les réseaux périphériques sans fil, plusieurs défis hérités de l'AI et de EC doivent être relevés. En particulier, les défis liés à l'hétérogénéité statistique des données à travers les dispositifs ainsi que la rareté et l'hétérogénéité des ressources nécessitent une attention particulière. L'objectif de cette thèse est de proposer des moyens de relever ces défis et d'évaluer le potentiel de la FL dans de futures applications de villes intelligentes. Dans la première partie de cette thèse, l'accent est mis sur l'incorporation des propriétés des données dans la gestion de la participation des dispositifs dans FL et de l'allocation des ressources. Nous commençons par identifier les mesures de diversité des données qui peuvent être utilisées dans différentes applications. Ensuite, nous concevons un indicateur de diversité permettant de donner plus de priorité aux clients ayant des données plus informatives. Un algorithme itératif est ensuite proposé pour sélectionner conjointement les clients et allouer les ressources de communication. Cet algorithme accélère l'apprentissage et réduit le temps et l'énergie nécessaires. De plus, l'indicateur de diversité proposé est renforcé par un système de réputation pour éviter les clients malveillants, ce qui améliore sa robustesse contre les attaques par empoisonnement des données. Dans une deuxième partie de cette thèse, nous explorons les moyens de relever d'autres défis liés à la mobilité des clients et au changement de concept dans les distributions de données. De tels défis nécessitent de nouvelles mesures pour être traités. En conséquence, nous concevons un processus basé sur les clusters pour le FL dans les réseaux véhiculaires. Le processus proposé est basé sur la formation minutieuse de clusters pour contourner la congestion de la communication et est capable de traiter différents modèles en parallèle. Dans la dernière partie de cette thèse, nous démontrons le potentiel de FL dans un cas d'utilisation réel impliquant la prévision à court terme de la puissance électrique dans un réseau intelligent. Nous proposons une architecture permettant l'utilisation de FL pour encourager la collaboration entre les membres de la communauté et nous montrons son importance pour l'entraînement des modèles et la réduction du coût de communication à travers des résultats numériques.Abstract : Artificial intelligence (AI) and Edge computing (EC) have enabled various applications ranging from smart home, to intelligent manufacturing, and smart cities. This progress was fueled mainly by the availability of more data, abundance of computing power, and the progress of several compression techniques. However, the main advances are in relation to deploying cloud-trained machine learning (ML) models on edge devices. This premise requires that all data generated by end devices be sent to a centralized server, thus raising several privacy concerns and creating significant communication overhead. Accordingly, paving the last mile of AI on EC requires pushing the training of ML models to the edge of the network. Federated learning (FL) has emerged as a promising technique for the collaborative training of ML models on edge devices. The devices train a globally shared model on their locally stored data and only share the resulting parameters with a centralized entity. However, to enable FL in wireless edge networks, several challenges inherited from both AI and EC need to be addressed. In particular, challenges related to the statistical heterogeneity of the data across the devices alongside the scarcity and the heterogeneity of the resources require particular attention. The goal of this thesis is to propose ways to address these challenges and to evaluate the potential of FL in future applications. In the first part of this thesis, the focus is on incorporating the data properties of FL in handling the participation and resource allocation of devices in FL. We start by identifying data diversity measures allowing us to evaluate the richness of local datasets in different applications. Then, we design a diversity indicator allowing us to give more priority to clients with more informative data. An iterative algorithm is then proposed to jointly select clients and allocate communication resources. This algorithm accelerates the training and reduces the overall needed time and energy. Furthermore, the proposed diversity indicator is reinforced with a reputation system to avoid malicious clients, thus enhancing its robustness against poisoning attacks. In the second part of this thesis, we explore ways to tackle other challenges related to the mobility of the clients and concept-shift in data distributions. Such challenges require new measures to be handled. Accordingly, we design a cluster-based process for FL for the particular case of vehicular networks. The proposed process is based on careful clusterformation to bypass the communication bottleneck and is able to handle different models in parallel. In the last part of this thesis, we demonstrate the potential of FL in a real use-case involving short-term forecasting of electrical power in smart grid. We propose an architecture empowered with FL to encourage the collaboration among community members and show its importance for both training and judicious use of communication resources through numerical results

Trustworthy Federated Learning: A Survey

Federated Learning (FL) has emerged as a significant advancement in the field of Artificial Intelligence (AI), enabling collaborative model training across distributed devices while maintaining data privacy. As the importance of FL increases, addressing trustworthiness issues in its various aspects becomes crucial. In this survey, we provide an extensive overview of the current state of Trustworthy FL, exploring existing solutions and well-defined pillars relevant to Trustworthy . Despite the growth in literature on trustworthy centralized Machine Learning (ML)/Deep Learning (DL), further efforts are necessary to identify trustworthiness pillars and evaluation metrics specific to FL models, as well as to develop solutions for computing trustworthiness levels. We propose a taxonomy that encompasses three main pillars: Interpretability, Fairness, and Security & Privacy. Each pillar represents a dimension of trust, further broken down into different notions. Our survey covers trustworthiness challenges at every level in FL settings. We present a comprehensive architecture of Trustworthy FL, addressing the fundamental principles underlying the concept, and offer an in-depth analysis of trust assessment mechanisms. In conclusion, we identify key research challenges related to every aspect of Trustworthy FL and suggest future research directions. This comprehensive survey serves as a valuable resource for researchers and practitioners working on the development and implementation of Trustworthy FL systems, contributing to a more secure and reliable AI landscape.Comment: 45 Pages, 8 Figures, 9 Table