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.

Networking research, especially focusing on human mobility, has evolved significantly in the last two decades and now relies on collection and analyzing larger datasets. The increasing sizes of datasets are enabled by larger automated efforts to collect data as well as by scalable methods to analyze and unveil insights, which was not possible many years ago. However, this fast expansion and innovation in human-centric research often comes at a cost of privacy or ethics. In this work, we review a vast corpus of scientific work on human mobility and how ethics and privacy were considered. We reviewed a total of 118 papers, including 149 datasets on individual mobility. We demonstrate that these ever growing collections, while enabling new and insightful studies, have not all consistently followed a pre-defined set of guidelines regarding acceptable practices in data governance as well as how their research was communicated. We conclude with a series of discussions on how data, privacy and ethics could be dealt within our community.

Content Delivery Networks (CDNs) have been pivotal in the dramatic evolution of the Internet, handling the majority of data traffic for billions of connected users. Low-Earth-Orbit (LEO) satellite networks, such as Starlink, aim to revolutionize global connectivity by providing high-speed, low-latency Internet to remote regions. However, LEO satellite networks (LSNs) face challenges integrating with traditional CDNs, which rely on geographical proximity for efficient content delivery - a method that clashes with the operational dynamics of LSNs. In this paper, we scrutinize the operation of CDNs in the context of LSNs, using Starlink as a case study. We develop a browser extension NetMet that performs extensive web browsing experiments from controlled nodes using both Starlink and terrestrial Internet access. Additionally, we analyse crowdsourced speed tests from Starlink users to Cloudflare CDN servers globally. Our results indicate significant performance issues for Starlink users, stemming from the misalignment between terrestrial and satellite infrastructures. We then investigate the potential for SpaceCDNs which integrate CDN infrastructure directly within the LSNs, and show that this approach offers a promising alternative that decreases latencies by over 50%, making them comparable with the CDN experience of users behind terrestrial ISPs. Our aim is to stimulate further research and discussion on overcoming the challenges of effective content delivery with growing LSN offerings.

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.

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.

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.

Application domains such as automotive and the Internet of Things may benefit from in-network computing to reduce the distance data travels through the network and the response time. Information Centric Networking (ICN) based compute frameworks such as Named Function Networking (NFN) are promising options due to their location independence and loosely-coupled communication model. However, unlike current operations, such solutions may benefit from orchestration across the compute nodes to use the available resources in the network better. In this paper, we adopt the State Vector Synchronization (SVS), an application dataset synchronization protocol in ICN, to enhance the neighborhood knowledge of in-network compute nodes in a distributed fashion. As such, we design distributed coordination for in-network computation (DICer) that assists the service deployments by improving the resolution of compute requests. We evaluate the performance of DICer against NFN and observe an increase in the resource utilization at the edge and a reduction in the request completion time.

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.

Edge computing enables developers to deploy their services on compute resources deployed closer to the users. The abstraction requires powerful orchestration capabilities and the resolution of complex optimization problems. While edge computing is a consistently growing trend, the community (research and industry) still largely embraces adaptations and extensions of existing cloud technologies that have been proven ineffective on edge (e.g. Kubernetes). In this work, we present Oakestra, a novel hierarchical orchestration framework specifically designed for supporting service operation over heterogeneous edge infrastructures. In this demonstration, we showcase the various features and operations of Oakestra using our latency-critical augmented reality (AR) application.

Bluetooth trackers, or tags, have quickly become ubiquitous and widely supported by multiple vendors. Beyond their original design of finding lost objects, these devices have the ability to extend the capabilities of current wireless smart devices. Since its launch in 2019, Apple’s FindMy enables any devices from their brand to be easily tracked by more than 1 billion active iPhones and iPads on the market. While convenient, these systems may even serve further uses, including as a result of this work, crowd sensing and a side channel for mobile communication. But they also raise privacy concerns for their users. In this paper, we demonstrate how Apple FindMy can be used as a privacy-friendly tool for crowd monitoring, and how it may inadvertently leak information on a person’s location in case of deliberate tracking. Additionally, we design and evaluate a proof of concept protocol, using the Apple FindMy and a crafted tag using a simple microcontroller. We show how such system could be used to transmit information at very low bit rates, while the devices transporting the information remain unaware of this covert channel, yielding an out of band communication channel.

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.

Human mobility shapes our daily lives, our urban environment and even the trajectory of a global pandemic. While various aspects of human mobility and inter-personal contact duration have already been studied separately, little is known about how these two key aspects of our daily lives are fundamentally connected. Better understanding of such interconnected human behaviors is crucial for studying infectious diseases, as well as opportunistic content forwarding. To address these deficiencies, we conducted a study on a mobile social network of human mobility and contact duration, using data from 71 persons based on GPS and Bluetooth logs for 2 months in 2018. We augment these data with location APIs, enabling a finer granular characterization of the users’ mobility in addition to contact patterns. We model stops durations to reveal how time-unbounded-stops (e.g., bars or restaurants) follow a log-normal distribution while time-bounded-stops (e.g., offices, hotels) follow a power-law distribution. Furthermore, our analysis reveals contact duration adheres to a log-normal distribution, which we use to model the duration of contacts as a function of the duration of stays. We further extend our understanding of contact duration during trips by modeling these times as a Weibull distribution whose parameters are a function of trip length. These results could better inform models for information or epidemic spreading, helping guide the future design of network protocols as well as policy decisions.

Cloud computing has seen continuous growth over the last decade. The recent rise in popularity of next-generation applications brings forth the question: "Can current cloud infrastructure support the low latency requirements of such apps?" Specifically, the interplay of wireless last-mile and investments of cloud operators in setting up direct peering agreements with ISPs globally to current cloud reachability and latency has remained largely unexplored.This paper investigates the state of end-user to cloud connectivity over wireless media through extensive measurements over six months. We leverage 115,000 wireless probes on the Speed-checker platform and 195 cloud regions from 9 well-established cloud providers. We evaluate the suitability of current cloud infrastructure to meet the needs of emerging applications and highlight various hindering pressure points. We also compare our results to a previous study over RIPE Atlas. Our key findings are: (i) the most impact on latency comes from the geographical distance to the datacenter; (ii) the choice of a measurement platform can significantly influence the results; (iii) wireless last-mile access contributes significantly to the overall latency, almost surpassing the impact of the geographical distance in many cases. We also observe that cloud providers with their own private network backbone and direct peering agreements with serving ISPs offer noticeable improvements in latency, especially in its consistency over longer distances.

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.

In the early days of cloud computing, datacenters were sparsely deployed at distant locations far from end-users with high end-to-end communication latency. However, today’s cloud datacenters have become more geographically spread, the bandwidth of the networks keeps increasing, pushing the end-users latency down. In this paper, we provide a comprehensive cloud reachability study as we perform extensive global client-to-cloud latency measurements towards 189 datacenters from all major cloud providers. We leverage the well-known measurement platform RIPE Atlas, involving up to 8500 probes deployed in heterogeneous environments, e.g., home and offices. Our goal is to evaluate the suitability of modern cloud environments for various current and predicted applications. We achieve this by comparing our latency measurements against known human perception thresholds and are able to draw inferences on the suitability of current clouds for novel applications, such as augmented reality. Our results indicate that the current cloud coverage can easily support several latency-critical applications, like cloud gaming, for the majority of the world’s population.

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.

For road safety, detecting and reacting efficiently to road hazards is crucial and yet challenging due to practical restrictions such as limited data availability, which relies on network support. Moreover, from a system perspective we lack a computational model capable of providing to vehicles reliable and real-time assessment of the road context. As autonomous vehicles become widespread, the safety issues are further aggravated by the gap between cloud, roadside infrastructure and road users in terms of communication latency, software-hardware compatibility and data interoperability. To tackle this, we present ECCO: an orchestration framework that enables edge-cloud collaborative computing for road context assessment. ECCO can create on-demand task execution pipelines spanning multiple, potentially resource-constrained edge-nodes with the smart IoT infrastructure support. Our prototype lays the groundwork to support new services, which can use more efficiently the road infrastructure and deliver safety-critical applications for road users.

Unikernels are a new lightweight virtualization technology born as an alternative to virtual machines and containers. Geared towards service provisioning for the Internet of Things (IoT) and edge computing, they offer extremely small memory footprint and strong isolation properties. However, the unikernels ecosystem is still in its infancy and lacks quintessential functionalities found in more well-established virtualization technologies. For example, stateful migration is a highly desired feature for mobile edge services in distributed environments which is not yet supported by unikernels. This is one of the shortcomings preventing us from reaping the full benefits of unikernels outside of stateless applications. In this work, we aim bridging this gap with MirageManager: A ready-to-deploy unikernel migration system enabling lossless migration supported by a function-level, application logic check-pointing library of our design. Our evaluation results show that MirageManager is able to lower the service downtime by ∼80%, and drastically reduce the state transfer data by almost ∼100% when comparing against Podman. Additionally, MirageManager also beats Podman, a container-based engine, in parallel service migration across constrained edge networks reducing the overall migration time by up to ∼6x.