The Starlink network from SpaceX stands out as the only commercial LEO network with over 2M+ customers and more than 4000 operational satellites. In this paper, we conduct a first-of-its-kind extensive multi-faceted analysis of Starlink performance leveraging several measurement sources. First, based on 19.2M crowdsourced M-Lab speed tests from 34 countries since 2021, we analyze Starlink global performance relative to terrestrial cellular networks. Second, we examine Starlink's ability to support real-time latency and bandwidth-critical applications by analyzing the performance of (i) Zoom conferencing, and (ii) Luna cloud gaming, comparing it to 5G and fiber. Third, we perform measurements from Starlink-enabled RIPE Atlas probes to shed light on the last-mile access and other factors affecting its performance.Finally, we conduct controlled experiments from Starlink dishes in two countries and analyze the impact of globally synchronized "15-second reconfiguration intervals'' of the satellite links that cause substantial latency and throughput variations. Our unique analysis paints the most comprehensive picture of Starlink's global and last-mile performance to date.

The InterPlanetary File System (IPFS) is a novel decentralised storage architecture, which provides decentralised cloud storage by building on founding principles of P2P networking and content addressing. IPFS is used by more than 230 k peers per week and serves tens of millions of requests per day, which makes it an interesting large-scale operational network to study. While it is used as a building block in several projects and studies, its inner workings, properties, and implications have only been marginally explored in research. Thus, we provide an overview of the IPFS design and its core features, along with the opportunities that it opens, as well as the challenges that it faces because of its properties. Overall, IPFS presents an interesting set of characteristics and offers lessons which can help building decentralised systems of the future.

With increasing connectivity, more and more advanced forms of cooperation among vehicles have become possible. Research has shown that vehicle-to-vehicle communication can improve road safety. However, more recent research advances regarding complex interactions such as cooperative maneuver negotiation have not yet gained much attention. Thus, in this survey, we analyze proposals for maneuver coordination protocols among automated vehicles. We include the communication, computation, and decision-making architectures commonly employed and a categorization scheme for different application-layer protocols enabling cooperative maneuvers. Next, we summarize related standardization, industry alliances, and research projects. As this field of research is still emerging, we also identify the diverse challenges that lie ahead before cooperative maneuver negotiations among automated vehicles can become a reality.

Widespread adoption of mobile augmented reality (AR) and virtual reality (VR) applications depends on their smoothness and immersiveness. Modern AR applications applying computationally intensive computer vision algorithms can burden today's mobile devices, and cause high energy consumption and/or poor performance. To tackle this challenge, it is possible to offload part of the computation to nearby devices at the edge. However, this calls for smart task placement strategies in order to efficiently use the resources of the edge infrastructure. In this paper, we introduce Nimbus --- a task placement and offloading solution for a multi-tier, edge-cloud infrastructure where deep learning tasks are extracted from the AR application pipeline and offloaded to nearby GPU-powered edge devices. Our aim is to minimize the latency experienced by end-users and the energy costs on mobile devices. Our multifaceted evaluation, based on benchmarked performance of AR tasks, shows the efficacy of our solution. Overall, Nimbus reduces the task latency by ~4x and the energy consumption by ~77% for real-time object detection in AR applications. We also benchmark three variants of our offloading algorithm, disclosing the trade-off of centralized versus distributed execution.

Time-Sensitive Networking (TSN) is a set of standards offering bounded latency and jitter, low packet loss, and reliability for Ethernet-based systems and allowing best-effort and real-time traffic to coexist. Domains that use TSN include intra-vehicular networks (IVNs), aerospace, professional audio-video solutions, and smart manufacturing. All these areas shift towards Ethernet due to its scalability, throughput, easy to develop applications, and affordability to produce in a large scale. In this work, we devise a methodology that introduces a workflow comprising several steps to assess TSN in various domains. The first step defines requirements and assesses which real-time traffic is present within a given domain. The second step focuses on configuration of a representative TSN-based network. The third step then evaluates the performance of different TSN standards in the chosen configuration(s). The final - optional - step supports optimizing the system to fulfill the identified requirements. The methodology is generalized by assessing the various TSN domains, finding their commonalities. As a result, we see the methodology can be applied to other TSN solutions. We provide a detailed case study for the domain of IVNs, from which the methodology is derived. We summarize the key requirements, systematically analyze IVNs traffic patterns for real-time and best effort traffic, and evaluate the performance of crucial TSN standards recommended by the 802.1DG Automotive Profile. The methodology builds on top of infrastructure framework, EnGINE, that offers an environment for reproducible and scalable TSN experiments and relies on commercial off the shelf hardware and open-source solutions. The framework allows to evaluate various standards and identify suitable topologies with focus on Layer 2 solutions. Using EnGINE, we evaluated the various traffic patterns and their corresponding TSN configurations and identified if and how the IVN requirements can be fulfilled.

Self-driving and multimedia systems have common implications: increased demand on network bandwidth and computation nodes. To cope with the current and future challenges, intra-vehicular networks (IVNs) change their layout. They are built around powerful central nodes connected to the rest of the vehicle via Ethernet. The usage of Ethernet presents a challenge, as it by design lacks support for deterministic behavior, which is crucial for real-time systems. Therefore, the IEEE Time-Sensitive Networking (TSN) task group offers standards introducing low-latency and deterministic communication into Ethernet based networks allowing coexistence of best-effort and real-time traffic. To understand the coexistence challenges, these new networked systems need to be thoroughly evaluated with IVN requirements in mind. To assess various topologies, configurations, and data traffic types in IVN setups, we introduceEnvironment forGenericIn-vehicularNetworkingExperiments—EnGINE. It allows, among many others, repeatable, reproducible, and replicable TSN experiments with high precision and flexibility.EnGINEis based on commercial off-the-shelf hardware and uses the flexible Ansible framework for experiment orchestration. This allows us to configure various topologies emulating realistic behavior of IVNs or other time sensitive systems used, e.g., in industrial automation. Obtaining such realism is challenging using simulations. Based on available related work, we further address the challenges found in those networks, especially IVNs. We derive TSN domain framework requirements, provide details on design decisions for theEnGINE, and present results to show its capabilities. The results present relevant network metrics based on collected data. A key focus is on the experiment campaigns realism achieved by real IVNs’ data footage and the OS optimizations to offer real-time behavior. We believe thatEnGINEprovides the ideal environment for TSN experiments from different domains.