In the past few years, accelerated by Covid-19 pandemic, real-time services based on RTC (Real-Time Communications) have become pervasive, and their performance has become critical for a wide range of business and entertainment applications. The diffusion of RTC services beyond controlled enterprise network perimeters poses the challenge of guaranteeing high-quality, reliable, in-time delivery over the Internet and at global, worldwide scale. In this paper, we address such a problem by proposing ROBUST, a Reliable and Flexible Media Transport based on Pub/Sub Information-Centric Networking (ICN) principles. ROBUST brings enhanced reliability and flexibility to RTC applications over varying network environments and conditions. ROBUST initial design is experimentally assessed on three popular Video Conferencing applications (Jitsi Meet, Cisco Webex, and Zoom) with encouraging improvements in terms of objective Quality of Experience (QoE) metrics: e.g., we observe at least 91% loss recovery in the most challenging experimental settings, an improved video smoothness for Zoom up to 160%, a higher resolution for Webex up to 16%, while Jitsi with ROBUST works in conditions where the original application does not.

In this article, we consider security aspects of online meeting applications based on protocols such as WebRTC that leverage the Information-centric Networking (ICN) architecture to make the system fundamentally more scalable. If the scalability properties provided by ICN have been proved in recent literature, the security challenges and implications for real-time applications have not been reviewed. We show that this class of applications can benefit from strong security and scalability jointly without any major tradeoff and with significant performance improvements over traditional WebRTC systems. To achieve this goal, some modifications to the current ICN architecture must be implemented in the way integrity and authentication are verified. Extensive performance analysis of the architecture based on the open source implementation of Hybrid-ICN proves that real-time applications can greatly benefit from this novel network architecture in terms of strong security and scalable communications.

WebRTC is increasingly adopted by real-time streaming applications beyond traditional multiparty collaboration, in virtue of its low-latency capabilities. To support large scale deployments, the WebRTC architecture has evolved from the original peer-to-peer design to a centralized MCU-based (Multipoint Control Unit) system first, then to a lighter, SFU-based (Selective Forwarding Unit) system where media encoding/decoding and rate selection is delegated to the clients.In this paper we present ICN-RTC, a real time communication architecture build on the latest SFU-based proposal that exploits the scalability benefits provided by Information-Centric Networking (ICN). We also propose the ICN-RTC synchronization protocol, a protocol that allows to use the pull-based transport of ICN without introducing additional latency to media distribution.We implemented ICN-RTC using Hybrid ICN, an incrementally deployable ICN implementation using IP. Preliminary results are encouraging: ICN-RTC scales with the number of active speakers rather than the total number of users in the conference. This allows for calls with ten times more participants with respect to the standard WebRTC, reducing the resources needed for the call by more than a hundred times.

Large-scale information processing often relies on subset matching for data classification and routing. Examples are publish/subscribe and stream processing systems, database systems, social media, and information-centric networking. For instance, an advanced Twitter-like messaging service where users might follow specific publishers as well as specific topics encoded as tag sets must join a stream of published messages with the users and their preferred tag sets so that the user tag set is a subset of the message tags.

Streaming of video content over the Internet is experiencing an unprecedented growth. While video permeates every application, it also puts tremendous pressure in the network-to support users having heterogeneous accesses and expecting a high quality of experience, in a furthermore cost-effective manner. In this context, future internet paradigms, such as information centric networking (ICN), are particularly well suited to not only enhance video delivery at the client (as in the dynamic adaptive streaming over HTTP (DASH) approach), but to also naturally and seamlessly extend video support deeper in the network functions. In this paper, we contrast ICN and transmission control protocol/internet protocol (TCP/IP) with an experimental approach, where we employ several state-of-the-art DASH controllers (PANDA, AdapTech, and BOLA) on an ICN versus TCP/IP network stack. Our campaign, based on tools that we developed and made available as open-source software, includes multiple clients (homogeneous vesrus heterogeneous mixture and synchronous vesrus asynchronous arrivals), videos (up to 4k resolution), channels (e.g., DASH profiles, emulated WiFi and LTE, and real 3G/4G traces), and levels of integration with an ICN network (i.e., vanilla named data networking (NDN), wireless loss detection and recovery at the access point, and load balancing). Our results clearly illustrate, as well as quantitatively assess, the benefits of ICN-based streaming, warning about potential pitfalls that are however easy to avoid.

Application-defined and location-independent addressing is a founding principle of information centric networking (ICN) that is inherently difficult to realize if one also wants scalable routing and forwarding. We propose an ICN architecture, called TagNet, intended to combine expressive application-defined addressing with scalable routing and forwarding. TagNet features two independent delivery services: one with application-defined and possibly location-independent content descriptors, and one with network-defined host locators. In this paper we develop and evaluate specialized forwarding algorithms for TagNet. We then implement and combine these algorithms in a forwarding engine built on a general-purpose commodity CPU, and show experimentally that, thanks to the dual addressing, by descriptor or by locator, this engine can achieve a throughput of over 20Gbps with large forwarding tables corresponding to hundreds of millions of users.

The evolution of the Internet into a distributed Information access system calls for a paradigm shift to enable an evolvable future network architecture. Information-Centric Networking (ICN) proposals rethink the communication model around named data, in contrast with the host-centric transport view of TCP/IP. Information retrieval is natively pull-based, driven by user requests, point-to-multipoint and intrinsically coupled with in-network caching. In this paper, we tackle the problem of joint multipath congestion control and request forwarding in ICN for the first time. We formulate it as a global optimization problem with the twofold objective of maximizing user throughput and minimizing overall network cost. We solve it via decomposition and derive a family of optimal congestion control strategies at the receiver and of distributed algorithms for dynamic request forwarding at network nodes. An experimental evaluation of our proposal is carried out in different network scenarios to assess the performance of our design and to highlight the benefits of an ICN approach.

It is often beneficial to architect networks and overlays as fully decentralized systems, in the sense that any computation (e.g., routing or search) would only use local information, and no single node would have a complete view or control over the whole network. Yet sometimes it also important to compute global properties of the network. In this paper we propose a fully decentralized algorithm to compute some global properties that can be derived from the spectrum of the network. More specifically, we compute the most significant eigenvalues of a descriptive matrix closely related to the adjacency matrix of the network graph. Such spectral properties can then lead to, for example, the “mixing time” of a network, which can be used to parametrize random walks and related search algorithms typical of peer-to-peer networks. Our key insight is to view the network as a linear dynamic system whose impulse response can be computed efficiently and locally by each node. We then use this impulse response to identify the spectral properties of the network. This algorithm is completely decentralized and requires only minimal local state and local communication. We show experimentally that the algorithm works well on different kinds of networks and in the presence of network instability.