29 research outputs found
Leveraging energy saving capabilities of current EEE interfaces via pre-coalescing
The low power idle mode implemented by Energy Efficient Ethernet (EEE) allows network interfaces to save up to 90% of their nominal energy consumption when idling. There is an ample body of research that recommends the use of frame coalescing algorithms—that enter the low power mode as soon as there is no more traffic waiting to be sent, and delay the exit from this mode until there is an acceptable amount of traffic queued—to minimize energy usage while maintaining an acceptable performance. However, EEE capable hardware from several manufactures delays the entrance to the low power mode for a considerable amount of time (hysteresis). In this paper we augment existing EEE energy models to account for the hysteresis delay and show that, using the configuration ranges provided by manufacturers, most existing EEE networking devices are unable to obtain significant energy savings. To improve their energy efficiency, we propose to implement frame coalescing directly at traffic sources, before reaching the network interface. We also derive the optimum coalescing parameters to obtain a given target energy consumption at the EEE device when its configuration parameters are known in advance.Agencia Estatal de Investigación | Ref. TEC2017-85587-
Optimal Design Strategies for Survivable Carrier Ethernet Networks
Ethernet technologies have evolved through enormous standardization efforts over the past two decades to achieve carrier-grade functionalities, leading to carrier Ethernet. Carrier Ethernet is expected to dominate next generation backbone networks due to its low-cost and simplicity. Ethernet's ability to provide carrier-grade Layer-2 protection switching with SONET/SDH-like fast restoration time is achieved by a new protection switching protocol, Ethernet Ring Protection (ERP). In this thesis, we address two important design aspects of carrier Ethernet networks, namely, survivable design of ERP-based Ethernet transport networks together with energy efficient network design. For the former, we address the problem of optimal resource allocation while designing logical ERP for deployment and model the combinatorially complex problem of joint Ring Protection Link (RPL) placements and ring hierarchies selection as an optimization problem. We develop several Mixed Integer Linear Programming (MILP) model to solve the problem optimally considering both single link failure and concurrent dual link failure scenarios. We also present a traffic engineering based ERP design approach and develop corresponding MILP design models for configuring either single or multiple logical ERP instances over one underlying physical ring. For the latter, we propose two novel architectures of energy efficient Ethernet switches using passive optical correlators for optical bypassing as well as using energy efficient Ethernet (EEE) ports for traffic aggregation and forwarding. We develop an optimal frame scheduling model for EEE ports to ensure minimal energy consumption by using packet coalescing and efficient scheduling
Analysis, characterization and optimization of the energy efficiency on softwarized mobile platforms
MenciĂłn Internacional en el tĂtulo de doctorLa inminente 5ÂŞ generaciĂłn de sistemas mĂłviles (5G) está a punto de revolucionar la industria, trayendo una nueva arquitectura orientada a los nuevos mercados verticales y servicios. Debido a esto, el 5G Infrastructure Public Private Partnership (5G-PPP) ha especificado una lista de Indicadores de Rendimiento Clave (KPI) que todo sistema 5G tiene que soportar, por ejemplo incrementar por 1000 el volumen de datos, de 10 a 100 veces m´as dispositivos conectados o consumos energĂ©ticos 10 veces inferiores. Con el fin de conseguir estos requisitos, se espera expandir los despligues actuales usando mas Puntos de Acceso (PoA) incrementando asĂ su densidad con
mĂşltiples tecnologĂas inalámbricas. Esta estrategia de despliegue masivo tiene una contrapartida en la eficiencia energĂ©tica, generando un conflicto con el KPI de reducir por 10 el consumo energĂ©tico. En este contexto, la comunidad investigadora ha propuesto nuevos paradigmas para alcanzar los requisitos impuestos para los sistemas 5G, siendo materializados en tecnologĂas como Redes Definidas por Software (SDN) y VirtualizaciĂłn de Funciones de Red (NFV). Estos nuevos paradigmas son el primer paso hacia la softwarizaciĂłn de los despliegues mĂłviles, incorporando nuevos grados de flexibilidad y reconfigurabilidad de la Red de Acceso Radio (RAN). En esta tesis, presentamos primero un análisis detallado y caracterizaciĂłn de las redes mĂłviles softwarizadas. Consideramos el software como la base de la nueva generaciĂłn de redes celulares y, por lo tanto, analizaremos y caracterizaremos el impacto en la eficiencia energĂ©tica de estos
sistemas. La primera meta de este trabajo es caracterizar las plataformas software disponibles para Radios Definidas por Software (SDR), centrándonos en las dos soluciones principales de cĂłdigo abierto: OpenAirInterface (OAI) y srsLTE. Como resultado, proveemos una metodologĂa para analizar y caracterizar el rendimiento de estas soluciones en funciĂłn del uso de la CPU, rendimiento de red, compatibilidad y extensibilidad de dicho software. Una vez hemos entendido
quĂ© rendimiento podemos esperar de este tipo de soluciones, estudiamos un prototipo SDR construido con aceleraciĂłn hardware, que emplea una plataformas basada en FPGA. Este prototipo está diseñado para incluir capacidad de ser consciente de la energĂa, permiento al sistema ser reconfigurado para minimizar la huella energĂ©tica cuando sea posible. Con el fin de validar el diseño de nuestro sistema, más tarde presentamos una plataforma para caracterizar la energĂa que será empleada para medir experimentalmente el consumo energĂ©tico de dispositivos reales. En nuestro enfoque, realizamos dos tipos de análisis: a pequeña escala de tiempo y a gran escala de tiempo. Por lo tanto, para validar nuestro entorno de medidas, caracterizamos a travĂ©s de análisis numĂ©rico los algoritmos para la AdaptaciĂłn de la Tasa (RA) en IEEE 802.11, para entonces comparar
nuestros resultados teĂłricos con los experimentales. A continuaciĂłn extendemos nuestro
análisis a la plataforma SDR acelerada por hardware previamente mencionada. Nuestros resultados experimentales muestran que nuestra sistema puede en efecto reducir la huella energética reconfigurando el despligue del sistema.
Entonces, la escala de tiempos es elevada y presentamos los esquemas para Recursos bajo Demanda (RoD) en despliegues de red ultra-densos. Esta estrategia está basada en apagar/encender
dinámicamente los elementos que forman la red con el fin de reducir el total del consumo
energĂ©tico. Por lo tanto, presentamos un modelo analĂtico en dos sabores, un modelo exacto que predice el comportamiento del sistema con precisiĂłn pero con un alto coste computacional y uno simplificado que es más ligero en complejidad mientras que mantiene la precisiĂłn. Nuestros resultados muestran que estos esquemas pueden efectivamente mejorar la eficiencia energĂ©tica de
los despliegues y mantener la Calidad de Servicio (QoS). Con el fin de probar la plausibilidad
de los esquemas RoD, presentamos un plataforma softwarizada que sigue el paradigma SDN,
OFTEN (OpenFlow framework for Traffic Engineering in mobile Network with energy awareness).
Nuestro diseño está basado en OpenFlow con funcionalidades para hacerlo consciente de
la energĂa. Finalmente, un prototipo real con esta plataforma es presentando, probando asĂ la plausibilidad de los RoD en despligues reales.The upcoming 5th Generation of mobile systems (5G) is about to revolutionize the industry,
bringing a new architecture oriented to new vertical markets and services. Due to this, the 5G-PPP
has specified a list of Key Performance Indicator (KPI) that 5G systems need to support e.g. increasing
the 1000 times higher data volume, 10 to 100 times more connected devices or 10 times
lower power consumption. In order to achieve these requirements, it is expected to expand the
current deployments using more Points of Attachment (PoA) by increasing their density and by
using multiple wireless technologies. This massive deployment strategy triggers a side effect in
the energy efficiency though, generating a conflict with the “10 times lower power consumption”
KPI. In this context, the research community has proposed novel paradigms to achieve the imposed
requirements for 5G systems, being materialized in technologies such as Software Defined
Networking (SDN) and Network Function Virtualization (NFV). These new paradigms are the
first step to softwarize the mobile network deployments, enabling new degrees of flexibility and
reconfigurability of the Radio Access Network (RAN).
In this thesis, we first present a detailed analysis and characterization of softwarized mobile
networking. We consider software as a basis for the next generation of cellular networks and
hence, we analyze and characterize the impact on the energy efficiency of these systems. The
first goal of this work is to characterize the available software platforms for Software Defined
Radio (SDR), focusing on the two main open source solutions: OAI and srsLTE. As result, we
provide a methodology to analyze and characterize the performance of these solutions in terms
of CPU usage, network performance, compatibility and extensibility of the software. Once we
have understood the expected performance for such platformsc, we study an SDR prototype built
with hardware acceleration, that employs a FPGA based platform. This prototype is designed
to include energy-awareness capabilites, allowing the system to be reconfigured to minimize the
energy footprint when possible. In order to validate our system design, we later present an energy
characterization platform that we will employ to experimentally measure the energy consumption
of real devices. In our approach, we perform two kind of analysis: at short time scale and large
time scale. Thus, to validate our approach in short time scale and the energy framework, we have
characterized though numerical analysis the Rate Adaptation (RA) algorithms in IEEE 802.11,
and then compare our theoretical results to the obtained ones through experimentation. Next
we extend our analysis to the hardware accelerated SDR prototype previously mentioned. Our experimental results show that our system can indeed reduce the energy footprint reconfiguring
the system deployment.
Then, the time scale of our analysis is elevated and we present Resource-on-Demand (RoD)
schemes for ultradense network deployments. This strategy is based on dynamically switch on/off
the elements that form the network to reduce the overall energy consumption. Hence, we present
a analytic model in two flavors, an exact model that accurately predicts the system behaviour
but high computational cost and a simplified one that is lighter in complexity while keeping the
accuracy. Our results show that these schemes can effectively enhance the energy efficiency of
the deployments and mantaining the Quality of Service (QoS). In order to prove the feasibility of
RoD, we present a softwarized platform that follows the SDN paradigm, the OFTEN (Open Flow
framework for Traffic Engineering in mobile Networks with energy awareness) framework. Our
design is based on OpenFlow with energy-awareness functionalities. Finally, a real prototype of
this framework is presented, proving the feasibility of the RoD in real deployments.FP7-CROWD (2013-2015) CROWD (Connectivity management for eneRgy Optimised Wireless Dense networks).-- H2020-Flex5GWare (2015-2017) Flex5GWare (Flexible and efficient hardware/software platforms for 5G network elements and devices).Programa de Doctorado en IngenierĂa Telemática por la Universidad Carlos III de MadridPresidente: Gramaglia , Marco.- Secretario: JosĂ© Nuñez.- Vocal: Fabrizio Giulian
Fast algorithm for real-time rings reconstruction
The GAP project is dedicated to study the application of GPU in several contexts in which
real-time response is important to take decisions. The definition of real-time depends on
the application under study, ranging from answer time of ÎĽs up to several hours in case
of very computing intensive task. During this conference we presented our work in low
level triggers [1] [2] and high level triggers [3] in high energy physics experiments, and
specific application for nuclear magnetic resonance (NMR) [4] [5] and cone-beam CT [6].
Apart from the study of dedicated solution to decrease the latency due to data transport
and preparation, the computing algorithms play an essential role in any GPU application.
In this contribution, we show an original algorithm developed for triggers application, to
accelerate the ring reconstruction in RICH detector when it is not possible to have seeds
for reconstruction from external trackers
Standardization Roadmap for Unmanned Aircraft Systems, Version 2.0
This Standardization Roadmap for Unmanned Aircraft Systems, Version 2.0 (“roadmap”) is an update to version 1.0 of this document published in December 2018. It identifies existing standards and standards in development, assesses gaps, and makes recommendations for priority areas where there is a perceived need for additional standardization and/or pre-standardization R&D.
The roadmap has examined 78 issue areas, identified a total of 71 open gaps and corresponding recommendations across the topical areas of airworthiness; flight operations (both general concerns and application-specific ones including critical infrastructure inspections, commercial services, and public safety operations); and personnel training, qualifications, and certification. Of that total, 47 gaps/recommendations have been identified as high priority, 21 as medium priority, and 3 as low priority. A “gap” means no published standard or specification exists that covers the particular issue in question. In 53 cases, additional R&D is needed.
As with the earlier version of this document, the hope is that the roadmap will be broadly adopted by the standards community and that it will facilitate a more coherent and coordinated approach to the future development of standards for UAS. To that end, it is envisioned that the roadmap will continue to be promoted in the coming year. It is also envisioned that a mechanism may be established to assess progress on its implementation
Development of a real-time business intelligence (BI) framework based on hex-elementization of data points for accurate business decision-making
The desire to use business intelligence (BI) to enhance efficiency and effectiveness of business decisions is neither new nor revolutionary. The promise of BI is to provide the ability to capture interrelationship from data and information to guide action towards a business goal. Although BI has been around since the 1960s, businesses still cannot get competitive information in the form they want, when they want and how they want. Business decisions are already full of challenges. The challenges in business decision-making include the use of a vast amount of data, adopting new technologies, and making decisions on a real-time basis. To address these challenges, businesses spend valuable time and resources on data, technologies and business processes. Integration of data in decision-making is crucial for modern businesses. This research aims to propose and validate a framework for organic integration of data into business decision-making. This proposed framework enables efficient business decisions in real-time. The core of this research is to understand and modularise the pre-established set of data points into intelligent and granular “hex-elements” (stated simply, hex-element is a data point with six properties). These intelligent hex-elements build semi-automatic relationships using their six properties between the large volume and high-velocity data points in a dynamic, automated and integrated manner. The proposed business intelligence framework is called “Hex-Elementization” (or “Hex-E” for short). Evolution of technology presents ongoing challenges to BI. These challenges emanate from the challenging nature of the underlying new-age data characterised by large volume, high velocity and wide variety. Efficient and effective analysis of such data depends on the business context and the corresponding technical capabilities of the organisation. Technologies like Big Data, Internet of Things (IoT), Artificial Intelligence (AI) and Machine Learning (ML), play a key role in capitalising on the variety, volume and veracity of data. Extricating the “value” from data in its various forms, depth and scale require synchronizing technologies with analytics and business processes. Transforming data into useful and actionable intelligence is the discipline of data scientists. Data scientists and data analysts use sophisticated tools to crunch data into information which, in turn, are converted into intelligence. The transformation of data into information and its final consumption as actionable business intelligence is an end-to-end journey. This end-to-end transformation of data to intelligence is complex, time-consuming and resource-intensive. This research explores approaches to ease the challenges the of end-to-end transformation of data into intelligence. This research presents Hex-E as a simplified and semi-automated framework to integrate, unify, correlate and coalesce data (from diverse sources and disparate formats) into intelligence. Furthermore, this framework aims to unify data from diverse sources and disparate formats to help businesses make accurate and timely decisions