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 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.

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.

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.

Emerging Remote Piloting (RP) operations of electrified Unmanned Aerial Vehicles (UAVs) demand low-latency and high-quality video delivery to conduct safe operations in the low-altitude airspace. Although cellular networks are one of the prominent candidates to provide connectivity for such operations, their ground-centric nature limits their capabilities in achieving seamless and reliable aerial connectivity. In this paper, we study the feasibility of supporting RP operations with low latency and high-quality video delivery over commercial cellular networks. By setting up an adaptive bitrate video transmission pipeline with the Google Congestion Control (GCC) and Self-Clocked Rate Adaptation for Multimedia (SCReAM) Congestion Control (CC) algorithms, we analyze the video delivery performance for the RP application requirements and compare the performance of GCC and SCReAM against constant bitrate video delivery. Our results show that low-latency video delivery with < 300 ms playback latency between full-HD and 4K resolution can be maintained up to about 95% of the time in the air. While static bitrate video delivery outperforms adaptive streaming in urban location with abundant link capacity, the latter becomes advantageous in rural locations, where the link capacity is affected by fluctuations. Although the study’s findings highlight the capabilities of cellular networks in delivering low-latency video for a safety-critical aerial service, we also discuss the potential improvements and future research challenges for enabling safe operations and meeting the service requirements using cellular networks. We release our collected traces and the video transmission pipeline as open-source to facilitate research in this field.

Mobility is a fundamental characteristic of human society that shapes various aspects of our everyday interactions. This pervasiveness of mobility makes it paramount to understand factors that govern human movement and how it varies across individuals. Currently, factors governing variations in personal mobility are understudied with existing research focusing on explaining the aggregate behaviour of individuals. Indeed, empirical studies have shown that the aggregate behaviour of individuals follows a truncated Lévy-flight model, but little understanding exists of the laws that govern intra-individual variations in mobility resulting from transportation choices, social interactions, and exogenous factors such as location-based mobile applications. Understanding these variations is essential for improving our collective understanding of human mobility, and the factors governing it. In this article, we study the mobility laws of location-based gaming—an emerging and increasingly popular exogenous factor influencing personal mobility. We analyse the mobility changes considering the popular PokémonGO application as a representative example of location-based games and study two datasets with different reporting granularity, one captured through location-based social media, and the other through smartphone application logging. Our analysis shows that location-based games, such as PokémonGO, increase mobility—in line with previous findings—but the characteristics governing mobility remain consistent with a truncated Lévy-flight model and that the increase can be explained by a larger number of short-hops, i.e., individuals explore their local neighborhoods more thoroughly instead of actively visiting new areas. Our results thus suggest that intra-individual variations resulting from location-based gaming can be captured by re-parameterization of existing mobility models.

Multipath TCP (MPTCP) extends traditional TCP to enable simultaneous use of multiple connection endpoints at the source and destination. MPTCP has been under active development since its standardization in 2013, and more recently in February 2020, MPTCP was upstreamed to the Linux kernel. In this paper, we provide the first broad analysis of MPTCPv0 in the Internet. We probe the entire IPv4 address space and an IPv6 hitlist to detect MPTCP-enabled systems operational on port 80 and 443. Our scans reveal a steady increase in MPTCP-capable IPs, reaching 9k+ on IPv4 and a few dozen on IPv6. We also discover a significant share of seemingly MPTCP-capable hosts, an artifact of middleboxes mirroring TCP options. We conduct targeted HTTP(S) measurements towards select hosts and find that middleboxes can aggressively impact the perceived quality of applications utilizing MPTCP. Finally, we analyze two complementary traffic traces from CAIDA and MAWI to shed light on the real-world usage of MPTCP. We find that while MPTCP usage has increased by a factor of 20 over the past few years, its traffic share is still quite low.

Telecommunication (Telco) outdoor position recovery aims to localize outdoor mobile devices by leveraging measurement report (MR) data. Unfortunately, Telco position recovery requires sufficient amount of MR samples across different areas and suffers from high data collection cost. For an area with scarce MR samples, it is hard to achieve good accuracy. In this paper, by leveraging the recently developed transfer learning techniques, we design a novel Telco position recovery framework, called sf TLoc, to transfer good models in the carefully selected source domains (those fine-grained small subareas) to a target one which originally suffers from poor localization accuracy. Specifically, sf TLoc introduces three dedicated components: 1) a new coordinate space to divide an area of interest into smaller domains, 2) a similarity measurement to select best source domains, and 3) an adaptation of an existing transfer learning approach. To the best of our knowledge, sf TLoc is the first framework that demonstrates the efficacy of applying transfer learning in the Telco outdoor position recovery. To exemplify, on the 2G GSM and 4G LTE MR datasets in Shanghai, sf TLoc outperforms a non-transfer approach by 27.58 and 26.12 percent less median errors, and further leads to 47.77 and 49.22 percent less median errors than a recent fingerprinting approach NBL.