IEEE 802.1 Time-sensitive Networking (TSN) enables real-time communication with deterministically bounded network delay and jitter over standard IEEE 802.3 networks ("Ethernet"). In particular, TSN specifies a time-triggered scheduling mechanism in IEEE Std 802.1Qbv implemented by switches to control when outgoing queues get access to switch ports. Besides this time-triggered scheduling mechanism, other scheduling mechanisms can be active in the network at the same time including priority queuing and a credit-based shaper. Moreover, further supporting mechanisms such as the possibility to interrupt frames already in transmission (frame preemption) are specified by the TSN standards. Overall, this leads to a complex network infrastructure transporting both, real-time and non-real-time traffic in one converged network, making it hard to analyze the behavior of converged networks. To facilitate the analysis of TSN networks, we present TSNspecific extensions to the popular OMNeT++/INET framework for network simulations in this paper including, in particular, the time-triggered scheduling mechanism of IEEE Std 802.1Qbv. Besides the design of the TSN simulator, we present a proofof-concept implementation and exemplary evaluation of TSN networks.

Time-sensitive Networking (TSN) is an evolving group of IEEE standards for deterministic real-time communication making standard Ethernet technology applicable to safetycritical application domains such as manufacturing or automotive systems. TSN includes several mechanisms influencing the timely forwarding of traffic, in particular, a time-triggered scheduling mechanism called time-aware shaper (TAS) and frame preemption to reduce the blocking time of high-priority traffic by low-priority traffic. Although these mechanisms have been standardized and products implementing them begin to enter the market, it is still hard for practitioners to select and apply suitable mechanisms fitting the problem at hand. For instance, TAS schedules can be calculated for individual streams or classes of traffic, and frame preemption with strict priority scheduling (w/o TAS) might seem to be an option in networks with extremely high data rates. In this paper, we make a first step towards assisting practitioners in making an informed decision when choosing between stream-based TAS, class-based TAS, and frame preemption by comparing these mechanisms in selected scenarios using our TSN network simulation tool NeSTiNg. Moreover, to facilitate the application of class-based TAS, we derive a formula for calculating class-based TAS configuration.

No abstract

BackgroundWhole genome sequencing has become fast, accurate, and cheap, paving the way towards the large-scale collection and processing of human genome data. Unfortunately, this dawning genome era does not only promise tremendous advances in biomedical research but also causes unprecedented privacy risks for the many. Handling storage and processing of large genome datasets through cloud services greatly aggravates these concerns. Current research efforts thus investigate the use of strong cryptographic methods and protocols to implement privacy-preserving genomic computations.MethodsWe propose Fhe-Bloom and Phe-Bloom, two efficient approaches for genetic disease testing using homomorphically encrypted Bloom filters. Both approaches allow the data owner to securely outsource storage and computation to an untrusted cloud. Fhe-Bloom is fully secure in the semi-honest model while Phe-Bloom slightly relaxes security guarantees in a trade-off for highly improved performance.ResultsWe implement and evaluate both approaches on a large dataset of up to 50 patient genomes each with up to 1000000 variations (single nucleotide polymorphisms). For both implementations, overheads scale linearly in the number of patients and variations, while Phe-Bloom is faster by at least three orders of magnitude. For example, testing disease susceptibility of 50 patients with 100000 variations requires only a total of 308.31 s (σ=8.73 s) with our first approach and a mere 0.07 s (σ=0.00 s) with the second. We additionally discuss security guarantees of both approaches and their limitations as well as possible extensions towards more complex query types, e.g., fuzzy or range queries.ConclusionsBoth approaches handle practical problem sizes efficiently and are easily parallelized to scale with the elastic resources available in the cloud. The fully homomorphic scheme, Fhe-Bloom, realizes a comprehensive outsourcing to the cloud, while the partially homomorphic scheme, Phe-Bloom, trades a slight relaxation of security guarantees against performance improvements by at least three orders of magnitude.

No abstract