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

Reconfigurable network processing: the FPGA case

As communication networks evolve towards 100 gigabits per second rates to address increasing demand of data trafic, network processing solutions must be revised and upgraded to support this need. Meanwhile, Field Programmable Gate Array (FPGA) technology is becoming a much more interesting platform were to integrate network processing capabilities and compete with current available solutions. In this paper, we argue that FPGAs can play a signi cant role in this area. To this end, a general discussion on the technology is first introduced to later focus on the speci c requirements to implement network processing architectures. Finally, based on our previous experience on building network devices on FPGAs, we discuss a case study to illustrate some of the main drivers to consider FPGA as an interesting solution for network processing.Sociedad Argentina de Informática e Investigación Operativ

COVID‑19 mitigation by digital contact tracing and contact prevention (app‑based social exposure warnings)

A plethora of measures are being combined in the attempt to reduce SARS-CoV-2 spread. Due to its sustainability, contact tracing is one of the most frequently applied interventions worldwide, albeit with mixed results. We evaluate the performance of digital contact tracing for different infection detection rates and response time delays. We also introduce and analyze a novel strategy we call contact prevention, which emits high exposure warnings to smartphone users according to Bluetooth-based contact counting. We model the effect of both strategies on transmission dynamics in SERIA, an agent-based simulation platform that implements population-dependent statistical distributions. Results show that contact prevention remains effective in scenarios with high diagnostic/response time delays and low infection detection rates, which greatly impair the effect of traditional contact tracing strategies. Contact prevention could play a significant role in pandemic mitigation, especially in developing countries where diagnostic and tracing capabilities are inadequate. Contact prevention could thus sustainably reduce the propagation of respiratory viruses while relying on available technology, respecting data privacy, and most importantly, promoting community-based awareness and social responsibility. Depending on infection detection and app adoption rates, applying a combination of digital contact tracing and contact prevention could reduce pandemic-related mortality by 20–56%.publishedVersionFil: Soldano, Germán J. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas; Argentina.Fil: Soldano, Germán J. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Físico-química de Córdoba; Argentina.Fil: Fraire Juan A. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina.Fil: Fraire Juan A. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Estudios Avanzados en ingeniería y Tecnología; Argentina.Fil: Fraire Juan A. Saarland University. Saarland Informatics Campus; Saarbrücken, Germany.Fil: Finochietto, Jorge M. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina.Fil: Finochietto, Jorge M. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Estudios Avanzados en ingeniería y Tecnología; Argentina.Fil: Quiroga; Rodrigo. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas; Argentina.Fil: Quiroga; Rodrigo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Físico-química de Córdoba; Argentina

A probabilistic query routing scheme for wireless sensor networks

The use of wireless sensor networks for information discovery and monitoring of continuous physical fields has emerged as a novel and efficient solution. To this end, a query message is routed through the network to fetch data from sensor nodes and report it back to a sink node. As several applications only require a limited subset of the available data in the network, this query could be ideally routed to fetch only relevant data. In this way, much energy due to message exchange among nodes could be saved. In this paper, we consider the application of computational intelligence on nodes to implement a parallel adaptive simulated annealing (PASA) mechanism able to direct queries to relevant nodes. Besides, a reinforcement learning algorithm is proposed to adapt progressively the query process to the characteristics of the network, limiting the routing space to areas with useful data. Finally, the relevant data collection mechanism is also discussed to illustrate the complete process. We show by extensive simulations that the routing cost can be reduced by approximately 60% over flooding with an error less than 5%.Sociedad Argentina de Informática e Investigación Operativ

Multistage Switching Architectures for Software Routers

Software routers based on personal computer (PC) architectures are becoming an important alternative to proprietary and expensive network devices. However, software routers suffer from many limitations of the PC architecture, including, among others, limited bus and central processing unit (CPU) bandwidth, high memory access latency, limited scalability in terms of number of network interface cards, and lack of resilience mechanisms. Multistage PC-based architectures can be an interesting alternative since they permit us to i) increase the performance of single software routers, ii) scale router size, iii) distribute packet manipulation and control functionality, iv) recover from single-component failures, and v) incrementally upgrade router performance. We propose a specific multistage architecture, exploiting PC-based routers as switching elements, to build a high-speed, largesize,scalable, and reliable software router. A small-scale prototype of the multistage router is currently up and running in our labs, and performance evaluation is under wa

Multiclass scheduling algorithms for the DAVID metro network

Abstract—The data and voice integration over dense wavelength-division-multiplexing (DAVID) project proposes a metro network architecture based on several wavelength-division-multiplexing (WDM) rings interconnected via a bufferless optical switch called Hub. The Hub provides a programmable interconnection among rings on the basis of the outcome of a scheduling algorithm. Nodes connected to rings groom traffic from Internet protocol routers and Ethernet switches and share ring resources. In this paper, we address the problem of designing efficient centralized scheduling algorithms for supporting multiclass traffic services in the DAVID metro network. Two traffic classes are considered: a best-effort class, and a high-priority class with bandwidth guarantees. We define the multiclass scheduling problem at the Hub considering two different node architectures: a simpler one that relies on a complete separation between transmission and reception resources (i.e., WDM channels) and a more complex one in which nodes fully share transmission and reception channels using an erasure stage to drop received packets, thereby allowing wavelength reuse. We propose both optimum and heuristic solutions, and evaluate their performance by simulation, showing that heuristic solutions exhibit a behavior very close to the optimum solution. Index Terms—Data and voice integration over dense wavelength-division multiplexing (DAVID), metropolitan area network, multiclass scheduling, optical ring, wavelength-division multiplexing (WDM). I

Migration strategies toward all optical metropolitan access rings

This paper was published in Journal of Lightwave Technology and is made available as an electronic reprint with the permission of OSA. The paper can be found at the following URL on the IEEE website: http://dx.doi.org/10.1109/JLT.2007.901325. Systematic or multiple reproduction or distribution to multiple locations via electronic or other means is prohibited and is subject to penalties under law.Nowadays, network operators are steadily deploying optical circuit switching (OCS) equipment in their metropolitan networks in order to cope with traffic increase and, most importantly, in order to reduce capital expenditures and operational expenditures of existing active technologies. On the other hand, optical burst switching (OBS) technology is expected to become mature in the medium term, and it may be used as an alternative to current OCS networks due to its potential advantages in terms of bandwidth allocation granularity. While OBS is being extensively studied in the literature, little attention has been paid in conducting a comparative analysis of OBS versus OCS, especially concerning cost analysis. In this paper, we provide a comparative analysis of OBS versus OCS as an evolutionary technology for all-optical rings in the metropolitan-access network. This paper is specifically targeted toward optimizing the number of optoelectronic receivers and wavelengths with real traffic matrices from the metropolitan rings in Madrid, Spain. Such matrices also include traffic projections of foreseeable broadband services, which are based on a market analysis from the largest operator in Spain. Our findings show that OCS might be more efficient than OBS in the metro-access segment, which is characterized by a highly centralized traffic pattern. However, the more distributed the traffic is, the more efficient the OBS is as well. Consequently, OBS might be better suited to metro-core networks, which show a more distributed and dynamic traffic pattern.The authors would like to thank the e-Photon/ONe+ network of excellenc

Multi-MetaRing fairness control in a WDM folded-bus architecture

The paper deals with fairness issues in a slotted, single-hop, WDM (Wavelength Division Multiplexing) optical architecture, based on a folded bus topology, previously proposed as a broadband access system or as a metro network. The peculiar fairness problem arising in this folded bus based architecture is addressed and an extension of the MetaRing protocol to the WDM scenario, named Multi-MetaRing, is proposed. Feasible Multi-MetaRing strategies are defined and analyzed. Both fair access and high aggregate network throughput can be achieved with a low complexity distributed access protocol by properly handling node access through all WDM channel

Flexible prototyping for ad hoc wireless sensor network protocols

The development of sophisticated energy-efficient protocols and the increasing complexity of applications in wireless sensor networks (WSNs) imposes the use of open and flexible programming architectures that enable access to the communication stack in a simple way. Nowadays, researchers in WSNs focus on not only the development of efficient mechanisms in the application level but also the interaction with the communication stack, in order to improve the performance. Because radio communication is the most energy-consuming component of a sensor node, the main challenge in WSNs is to reduce the communication cost by means of efficient in-network distributed processing. In this work, a probabilistic query routing mechanism is designed and implemented in a WSN, and a study of different operating systems (OS) and communication stacks available for WSNs is analyzed. This implementation enables flexible prototyping of novel mechanisms by using light communication protocols over a versatile operating system.Sociedad Argentina de Informática e Investigación Operativ

Risk Estimation in COVID-19 Contact Tracing Apps

In the context of COVID-19, contact tracing has shown its value as a tool for contention of the pandemic. In addition to its paper based form, contact tracing can be carried out in a more scalable and faster way by using digital apps. Mobile phones can record digital signals emitted by communication and sensing technologies, enabling the identification of risky contacts between users. Factors such as proximity, encounter duration, environment, ventilation, and the use (or not) of protective measures contribute to the probability of contagion. Estimation of these factors from the data collected by phones remains a challenge. In this work in progress we describe some of the challenges of digital contact tracing, the type of data that can be collected with mobile phones and focus particularly on the problem of proximity estimation using Bluetooth Low Energy (BLE) signals. Specifically, we use machine learning models fed with different combinations of statistical features derived from the BLE signal and study how improvements in accuracy can be obtained with respect to reference models currently in use.Sociedad Argentina de Informática e Investigación Operativ