Quantum Computers (QCs) differ radically from traditional computers and can efficiently solve mathematical problems fundamental to our current cryptographic algorithms. Although existing QCs need to accommodate more qubits to break cryptographic algorithms, the concern of ''Store-Now-Decrypt-Later'' (i.e., adversaries store encrypted data today and decrypt them once powerful QCs become available) highlights the necessity to adopt quantum-safe approaches as soon as possible. In this work, we investigate the performance impact of Post-Quantum Cryptography (PQC) on TLS 1.3. Different signature algorithms and key agreements (as proposed by the National Institute of Standards and Technology (NIST)) are examined through black- and white-box measurements to get precise handshake latencies and computational costs per participating library. We emulated loss, bandwidth, and delay to analyze constrained environments. Our results reveal that HQC and Kyber are on par with our current state-of-the-art, while Dilithium and Falcon are even faster. We observed no performance drawback from using hybrid algorithms; moreover, on higher NIST security levels, PQC outperformed any algorithm in use today. Hence, we conclude that post-quantum TLS is suitable for adoption in today's systems.

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.

Today's businesses and consumer applications are becoming increasingly dependent on cloud solutions, making them vulnerable to service outages that can result in a loss of communication or access to business-critical services and data. Are we really prepared for such failure scenarios? Given that failures can occur on both the network and data center sides, is it possible to have efficient end-to-end recovery? The answer is mostly negative due to the separate operation of these domains. This article offers a solution to this problem based on network virtualization, and discusses the necessary architecture and algorithm details. It also answers the question of whether it is better to provide resilience in the virtual or physical layer from a cost effectiveness and failure coverage perspective.

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.