Networking is critical to a holistic metaverse system given the high-throughput and low-latency requirements and often distributed nature of metaverses. This chapter discusses several important aspects and directions that can enhance the networking performance in the metaverse, including user experienced delay, edge computing, multimodal networking, semantic and goal/deadline-aware networking, multipath networking, CDN, etc.

Starlink has deployed over 7,800 satellites serving millions of subscribers, yet predicting its performance remains an open challenge. Rapid orbital dynamics, frequent handovers, and weather-induced signal attenuation create variability that existing models, built on a handful of instrumented terminals in limited regions, cannot capture at global scale. We present Horizon, the first global-scale machine learning system for predicting LEO satellite Internet performance. Our key insight is that crowdsourced measurement platforms, while noisier than controlled experiments, provide the geographic diversity necessary to build globally generalizable models. Horizon integrates 11 months of measurements from M-Lab and Cloudflare spanning 90+ countries with meteorological data and satellite orbital propagation features. On a fully held-out one-week temporal window, Horizon achieves mean absolute errors of 17.76 ms for latency and 25.63 Mbps for throughput; on a standard 80/20 split it outperforms all baselines, including adaptations of state-of-the-art architectures. Feature importance analysis reveals that geographic position dominates prediction, with latitude alone contributing 42-46%, while weather features account for 14-15%, quantifying the impact of atmospheric conditions on Ku/Ka-band links. Leave-one-location-out experiments confirm that Horizon generalizes to regions absent from training, enabling performance estimation where measurement infrastructure does not yet exist. Our dataset and pipeline are publicly available, providing a foundation for global LEO network performance visibility.

Microservice architectures allow developers to decompose their applications into independently deployable functional blocks, each with its own requirements. In order to support a wide range of constraints, service virtualization can be customized across microservices but is typically homogeneous within a cluster. As there is no clear one size fit all approach, we can improve resource utilization and performance by using virtualization as a new dimension in orchestration, especially in edge computing environments. For instance, Unikernels represent a lightweight virtualization technology that offers a performant alternative to traditional containers. While we find different studies analyzing and comparing these virtualization technologies, (a) the performance results might vary when including the overhead of the orchestration platform, and (b) it's not trivial to select the perfect virtualization technology for an entire cluster. In this paper, we explore the benefits of hybrid container-unikernel deployments by extending an orchestration framework for edge computing to allow for seamless mixing and matching of both technologies. Our evaluation shows how hybrid deployments can lead up to 44% CPU reduction cluster-wide while there are scenarios where containers are still preferable.

Emerging Low Earth Orbit (LEO) satellite constellations have been considered for uses beyond plain Internet access, including content caching and edge computing. Assuming satellites are equipped with inter-satellite links, we propose using these links and thus the space in-between satellites, paired with a dedicated satellite queuing system, to "store"data and provide access by keeping data in constant flux around the globe. We describe the properties and explore the capabilities of such a system and discuss some potential uses.

Low Earth Orbit (LEO) satellite constellations have highly dynamic network topologies, making conventional routing protocols inefficient. This paper presents Geographic Checkpoint Routing (GCR), a routing protocol that combines Geographic Routing and Segment Routing (SR) principles. Utilizing the structure of Walker Delta constellations, GCR eliminates the reliance on network topologies. It routes traffic through predefined geographic segments, offloads route computation to network edges, and allows traffic engineering through customizable policies without modifying satellite infrastructure. Simulations using the Starlink constellation show that GCR can match the performance of traditional source-based routing protocols without depending on network topologies.

Low-Earth-Orbit satellite networks (LSNs) are enabling low-latency high-bandwidth internet connectivity at a global scale. However, majority of the traffic on the Internet is currently handled by Content Delivery Networks (CDNs), which rely on geographical proximity to deliver content. In this work, we examine CDN performance for the commercial largest LSN, i.e. Starlink, by performing active measurements through our web browser plugin and passive analysis of Cloudflare speed tests globally. Comparing this to terrestrial networks, we highlight significant performance degradation for Starlink users due to the asymmetries between satellite and terrestrial infrastructure.

Recent industrial advancements introduce novel safety-critical applications for commercial networks. Remote Piloting (RP) Aerial Vehicles (AVs) is an example application, where reliable wireless connectivity is key to ensure safe operations in the sky. Jointly utilizing cellular and satellite networks can enable robust Multipath (MP) communications; however, their usage must be orchestrated efficiently toward application requirements. In this work, we investigate the MP communications performance of cellular and Low-Earth-Orbit (LEO) satellite links with respect to the Quality-of-Service (QoS) requirements of RP operations. Using MP-Transmission Control Protocol (MPTCP) and MP-Datagram Congestion Control Protocol (MP-DCCP), we evaluate various transport layer configurations to efficiently orchestrate both links and to support the application requirements. For this purpose, we develop an end-to-end MP emulation testbed that can provide means to realistically emulate cellular and LEO links with MPTCP and MP-DCCP. We run bi-direction al RP traffic over our testbed and measure the MP performance using different schedulers and Congestion Control (CC) algorithms. The results show that the flow size largely influences the individual path utilization due to high LEO link-layer losses. Moreover, excessive retransmissions occur on the MPTCP layer due to Head-of-Line (HoL) blocking from asymmetric link conditions. Using MP-DCCP without retransmissions helps avoid late arrivals and can meet the 99.999% communication reliability demand.

Researchers have already begun experimenting with next-generation cellular technologies and algorithms to enable use cases that lie beyond the scope of the current 5G standard, e.g. XR, smart factories, AI networks ops, etc. The common denominator requirement of such scenarios is the joint (coupled) operation of radio channel and edge computing resources within the core network. While there are numerous tools that allow experimenting with various aspects of radio resource management and computing resource management individually, there is a lack of solutions that enable researchers to prototype and evaluate applications and technologies dependent on both aspects simultaneously. In this work, we present nextGSIM, a 5G and beyond network simulator that realistically models the radio access network and edge network jointly to provide an end-to-end service to various user devices running microservice-based application workloads. We detail our design decisions and modular architecture of nextGSIM which resembles real-world setup of cellular networks, enabling effective and detailed simulations of resource management algorithms. We demonstrate the effectiveness and capabilities of nextGSIM through indoor factory case study wherein we evaluate widely regarded radio and edge resource management algorithms. We compare these against a joint radio-compute scheduler which emphasizes the need and benefits of joint resource allocation decision making, which is only possible through tools such as nextGSIM.