Simulation and experimental evaluation of a flexible time triggered ethernet architecture applied in satellite nano/micro launchers

The success of small satellites has led to the study of new technologies for the realization of Nano and Micro Launch Vehicle (NMLV) in order to make competitive launch costs. The paper has the objective to define and experimentally investigate the performance of a communication system for NMLV interconnecting the End Systems as On-Board Computer (OBC), telemetry apparatus, Navigation Unit...we propose a low cost Ethernet-based solution able to provide the devices with high interconnection bandwidth. To guarantee hard delays to the Guide, Navigation and Control applications we propose some architectural changes of the traditional Ethernet network with the introduction of a layer implemented in the End Systems and allow for the lack of any contention on the network links. We show how the proposed solution has comparable performance to the one of TTEthernet standard that is a very expensive solution. An experimental test-bed equipped with Ethernet switches and Hercules boards by Texas Instruments is also provided to prove the feasibility of the proposed solution

Performance evaluation of 5G access technologies and SDN transport network on an NS3 simulator

In this article, we deal with the enhanced Mobile Broadband (eMBB) service class, defined within the new 5G communication paradigm, to evaluate the impact of the transition from 4G to 5G access technology on the Radio Access Network and on the Transport Network. Simulation results are obtained with ns3 and performance analyses are focused on 6 GHz radio scenarios for the Radio Access Network, where an Non-Standalone 5G configuration has been assumed, and on SDN-based scenarios for the Transport Network. Inspired by the 5G Transformer model, we describe and simulate each single element of the three main functional plains of the proposed architecture to aim a preliminary evaluation of the end-to-end system performances

Dynamic in-network classification for service function chaining ready SDN networks

Service Function Chaining (SFC) paradigm consists in steering traffic flows through an ordered set of Service Functions (SFs) so that to realize complex end to end services. SFC architecture introduces all the logical functions that need to be developed in order to provide the required service. The SFC overlay infrastructure can be built on top of many different underlay network technologies. The high flexibility and centrally controlled feature of Software Defined Networking (SDN), make SDN networks to be a perfect underlay to build the SFC architecture. Due to Ternary Content Address Memory (TCAM) limited size, SDN switches have a limitation in the number of flow rules that can be hosted. This constraint is particularly penalizing in case of the SFC classifier function, since it requires to manage a high number of different flows. The limitation imposed by the TCAM size on the SFC classifier can be a bottleneck for the number of SFC requests that the SDN-based SFC architecture can handle. In this paper we define the Dynamic Chain Request Classification Offloading (D-CRCO) problem, as the one of maximizing the number of accepted SFC requests, having the possibility of: i) implement the SFC classifier also in a node that is internal to the SDN-based SFC domain, and ii) install classification rules in a reactive fashion. Furthermore, we propose the Dynamic Nearest Node (DNN) heuristic to solve the D-CRCO problem. Performance evaluation shows that by using DNN heuristic it is possible to triple the number of accepted requests, with respect to existing solutions

Cross-species analysis of viral nucleic acid interacting proteins identifies TAOKs as innate immune regulators

The cell intrinsic antiviral response of multicellular organisms developed over millions of years and critically relies on the ability to sense and eliminate viral nucleic acids. Here we use an affinity proteomics approach in evolutionary distant species (human, mouse and fly) to identify proteins that are conserved in their ability to associate with diverse viral nucleic acids. This approach shows a core of orthologous proteins targeting viral genetic material and species-specific interactions. Functional characterization of the influence of 181 candidates on replication of 6 distinct viruses in human cells and flies identifies 128 nucleic acid binding proteins with an impact on virus growth. We identify the family of TAO kinases (TAOK1, -2 and -3) as dsRNA-interacting antiviral proteins and show their requirement for type-I interferon induction. Depletion of TAO kinases in mammals or flies leads to an impaired response to virus infection characterized by a reduced induction of interferon stimulated genes in mammals and impaired expression of srg1 and diedel in flies. Overall, our study shows a larger set of proteins able to mediate the interaction between viral genetic material and host factors than anticipated so far, attesting to the ancestral roots of innate immunity and to the lineage-specific pressures exerted by viruses. Whether there are conserved nucleic acid (NA) binding proteins across species is not fully known. Using data from human, mouse and fly, the authors identify common binders, implicate TAOKs and show that these kinases bind NAs across species and promote virus defence in mammalian cells.We further thank Korbinian Mayr, Igor Paron, and Gaby Sowa for maintaining mass spectrometers and the MPI-B core facility, especially Judith Scholz, Leopold Urich, Sabine Suppmann, and Stephan Uebel, for support..

Association of kidney disease measures with risk of renal function worsening in patients with type 1 diabetes

Background: Albuminuria has been classically considered a marker of kidney damage progression in diabetic patients and it is routinely assessed to monitor kidney function. However, the role of a mild GFR reduction on the development of stage 653 CKD has been less explored in type 1 diabetes mellitus (T1DM) patients. Aim of the present study was to evaluate the prognostic role of kidney disease measures, namely albuminuria and reduced GFR, on the development of stage 653 CKD in a large cohort of patients affected by T1DM. Methods: A total of 4284 patients affected by T1DM followed-up at 76 diabetes centers participating to the Italian Association of Clinical Diabetologists (Associazione Medici Diabetologi, AMD) initiative constitutes the study population. Urinary albumin excretion (ACR) and estimated GFR (eGFR) were retrieved and analyzed. The incidence of stage 653 CKD (eGFR < 60 mL/min/1.73 m2) or eGFR reduction > 30% from baseline was evaluated. Results: The mean estimated GFR was 98 \ub1 17 mL/min/1.73m2 and the proportion of patients with albuminuria was 15.3% (n = 654) at baseline. About 8% (n = 337) of patients developed one of the two renal endpoints during the 4-year follow-up period. Age, albuminuria (micro or macro) and baseline eGFR < 90 ml/min/m2 were independent risk factors for stage 653 CKD and renal function worsening. When compared to patients with eGFR > 90 ml/min/1.73m2 and normoalbuminuria, those with albuminuria at baseline had a 1.69 greater risk of reaching stage 3 CKD, while patients with mild eGFR reduction (i.e. eGFR between 90 and 60 mL/min/1.73 m2) show a 3.81 greater risk that rose to 8.24 for those patients with albuminuria and mild eGFR reduction at baseline. Conclusions: Albuminuria and eGFR reduction represent independent risk factors for incident stage 653 CKD in T1DM patients. The simultaneous occurrence of reduced eGFR and albuminuria have a synergistic effect on renal function worsening

Perspectives on AI-based Algorithms Applied to C-RAN Functional Splitting and Advanced Antenna System Problem

The increasing number of mobile devices and the enhanced user experience they require have a strong impact on mobile network development, since they result in an increased channel capacity demand to be obtained with a limited site densification. An interesting approach to face this challenge can be found in the combination of two technology enabling solutions: Cloud or Centralized RAN (C-RAN) and Advanced Antenna Systems (AAS). In this paper, we discuss the advantages given by these solutions, with a special focus on how Artificial Intelligence (AI)-based algorithms can improve their combination in terms of functional split and antenna mapping, stated as an optimization problem. In particular, AI can be beneficial in three main areas, such as the actual solution of the optimization problem, the tuning of parameters used in classical heuristic algorithms aiming at solving the optimization problem, and, finally, the traffic and resource allocation prediction at the base of proactive reconfiguration frameworks

Enhancing the SRv6 Network Programming Model Through the Definition of the Maximize Throughput Behavior

The Network Programming model of SRv6 allows the creation of network programs that can be enforced over traffic flows entering a Segment Routing (SR) domain. A network program is a list of instructions that must be applied on a packet traversing the SR domain. Instructions, also known as behaviors, currently available in SRv6 are divided into two main categories: i) topological (e.g., send the packet over the shortest path), and ii) service based (e.g., duplicate the packet). In this paper we introduce a new behavior for the SRv6 Network Programming model, named maximize Throughput (max T). This function allows to steer an incoming traffic flow toward the egress node over the path that currently guarantees the highest throughput for the flow. The proposed max T behavior has been implemented over programmable switches, and its effectiveness in improving the performance experienced by flows asking for its application is evaluated through experiments performed over an emulated environment. The preliminary result shows that a 23% reduction of the transfer time for a file over the SR domain is achieved when the max T behavior is used