Multi-match Packet Classification on Memory-Logic Trade-off FPGA-based Architecture

Packet processing is becoming much more challenging as networks evolve towards a multi-service platform. In particular, packet classification demands smaller processing times as data rates increase. To successfully meet this requirement, hardware-based classification architectures have become an area of extensive research. Even if Field Programmable Logic Arrays (FPGAs) have emerged as an interesting technology for implementing these architectures, existing proposals either exploit maximal concurrency with unbounded resource consumption, or base the architecture on distributed RAM memory-based schemes which strongly undervalues FPGA capabilities. Moreover, most of these proposals target best-match classification and are not suited for high-speed updates of classification rulesets. In this paper, we propose a new approach which exploits rich logic resources available in modern FPGAs while reducing memory consumption. Our architecture is conceived for multi-match classification, and its mapping methodology is naturally suited for high-speed, simple updating of the classification ruleset. Analytical evaluation and implementation results of our architecture are promising, demonstrating that it is suitable for line speed processing with balanced resource consumption. With additional optimizations, our proposal has the potential to be integrated into network processing architectures demanding all aforementioned features.http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6602301Fil: Zerbini, Carlos A. Universidad Tecnológica Nacional. Departamento de Ingeniería Electrónica; Argentina.Fil: Finochietto, Jorge M. Universidad Nacional de Córdoba. Consejo Nacional de Investigaciones Científicas y Técnicas. Laboratorio de Comunicaciones Digitales; Argentina.Ingeniería de Sistemas y Comunicacione

Taxonomy of the order Bunyavirales : second update 2018

In October 2018, the order Bunyavirales was amended by inclusion of the family Arenaviridae, abolishment of three families, creation of three new families, 19 new genera, and 14 new species, and renaming of three genera and 22 species. This article presents the updated taxonomy of the order Bunyavirales as now accepted by the International Committee on Taxonomy of Viruses (ICTV).Non peer reviewe

2021 Taxonomic update of phylum Negarnaviricota (Riboviria: Orthornavirae), including the large orders Bunyavirales and Mononegavirales.

Correction to: 2021 Taxonomic update of phylum Negarnaviricota (Riboviria: Orthornavirae), including the large orders Bunyavirales and Mononegavirales. Archives of Virology (2021) 166:3567–3579. https://doi.org/10.1007/s00705-021-05266-wIn March 2021, following the annual International Committee on Taxonomy of Viruses (ICTV) ratification vote on newly proposed taxa, the phylum Negarnaviricota was amended and emended. The phylum was expanded by four families (Aliusviridae, Crepuscuviridae, Myriaviridae, and Natareviridae), three subfamilies (Alpharhabdovirinae, Betarhabdovirinae, and Gammarhabdovirinae), 42 genera, and 200 species. Thirty-nine species were renamed and/or moved and seven species were abolished. This article presents the updated taxonomy of Negarnaviricota as now accepted by the ICTV.This work was supported in part through Laulima Government Solutions, LLC prime contract with the US National Institute of Allergy and Infectious Diseases (NIAID) under Contract No. HHSN272201800013C. J.H.K. performed this work as an employee of Tunnell Government Services (TGS), a subcontractor of Laulima Government Solutions, LLC under Contract No. HHSN272201800013C. This work was also supported in part with federal funds from the National Cancer Institute (NCI), National Institutes of Health (NIH), under Contract No. 75N91019D00024, Task Order No. 75N91019F00130 to I.C., who was supported by the Clinical Monitoring Research Program Directorate, Frederick National Lab for Cancer Research. This work was also funded in part by Contract No. HSHQDC-15-C-00064 awarded by DHS S&T for the management and operation of The National Biodefense Analysis and Countermeasures Center, a federally funded research and development center operated by the Battelle National Biodefense Institute (V.W.); and NIH contract HHSN272201000040I/HHSN27200004/D04 and grant R24AI120942 (N.V., R.B.T.). S.S. acknowledges partial support from the Special Research Initiative of Mississippi Agricultural and Forestry Experiment Station (MAFES), Mississippi State University, and the National Institute of Food and Agriculture, US Department of Agriculture, Hatch Project 1021494. Part of this work was supported by the Francis Crick Institute which receives its core funding from Cancer Research UK (FC001030), the UK Medical Research Council (FC001030), and the Wellcome Trust (FC001030).S

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

2021 Taxonomic update of phylum Negarnaviricota (Riboviria: Orthornavirae), including the large orders Bunyavirales and Mononegavirales.

In March 2021, following the annual International Committee on Taxonomy of Viruses (ICTV) ratification vote on newly proposed taxa, the phylum Negarnaviricota was amended and emended. The phylum was expanded by four families (Aliusviridae, Crepuscuviridae, Myriaviridae, and Natareviridae), three subfamilies (Alpharhabdovirinae, Betarhabdovirinae, and Gammarhabdovirinae), 42 genera, and 200 species. Thirty-nine species were renamed and/or moved and seven species were abolished. This article presents the updated taxonomy of Negarnaviricota as now accepted by the ICTV

Optimization of Lookup Schemes for Flow-Based Packet Classification on FPGAs

Packet classification has become a key processing function to enable future flow-based networking schemes. As network capacity increases and new services are deployed, both high throughput and reconfigurability are required for packet classification architectures. FPGA technology can provide the best trade-off among them. However, to date, lookup stages have been mostly developed as independent schemes from the classification stage, which makes their efficient integration on FPGAs difficult. In this context, we propose a new interpretation of the lookup problem in the general context of packet classification, which enables comparing existing lookup schemes on a common basis. From this analysis, we recognize new opportunities for optimization of lookup schemes and their associated classification schemes on FPGA. In particular, we focus on the most appropriate candidate for future networking needs and propose optimizations for it. To validate our analysis, we provide estimation and implementation results for typical lookup architectures on FPGA and observe their convenience for different lookup and classification cases, demonstrating the benefits of our proposed optimization