1,272 research outputs found
An experimental study on latency-aware and self-adaptive service chaining orchestration in distributed NFV and SDN infrastructures
Network Function Virtualization (NFV) and Software Defined Networking (SDN) changed radically the way 5G networks will be deployed and services will be delivered to vertical applications (i.e., through dynamic chaining of virtualized functions deployed in distributed clouds to best address latency requirements). In this work, we present a service chaining orchestration system, namely LASH-5G, running on top of an experimental set-up that reproduces a typical 5G network deployment with virtualized functions in geographically distributed edge clouds. LASH-5G is built upon a joint integration effort among different orchestration solutions and cloud deployments and aims at providing latency-aware, adaptive and reliable service chaining orchestration across clouds and network resource domains interconnected through SDN. In this paper, we provide details on how this orchestration system has been deployed and it is operated on top of the experimentation infrastructure provided within the Fed4FIRE+ facility and we present performance results assessing the effectiveness of the proposed orchestration approach
A Case for Time Slotted Channel Hopping for ICN in the IoT
Recent proposals to simplify the operation of the IoT include the use of
Information Centric Networking (ICN) paradigms. While this is promising,
several challenges remain. In this paper, our core contributions (a) leverage
ICN communication patterns to dynamically optimize the use of TSCH (Time
Slotted Channel Hopping), a wireless link layer technology increasingly popular
in the IoT, and (b) make IoT-style routing adaptive to names, resources, and
traffic patterns throughout the network--both without cross-layering. Through a
series of experiments on the FIT IoT-LAB interconnecting typical IoT hardware,
we find that our approach is fully robust against wireless interference, and
almost halves the energy consumed for transmission when compared to CSMA. Most
importantly, our adaptive scheduling prevents the time-slotted MAC layer from
sacrificing throughput and delay
Software Defined Applications in Cellular and Optical Networks
abstract: Small wireless cells have the potential to overcome bottlenecks in wireless access through the sharing of spectrum resources. A novel access backhaul network architecture based on a Smart Gateway (Sm-GW) between the small cell base stations, e.g., LTE eNBs, and the conventional backhaul gateways, e.g., LTE Servicing/Packet Gateways (S/P-GWs) has been introduced to address the bottleneck. The Sm-GW flexibly schedules uplink transmissions for the eNBs. Based on software defined networking (SDN) a management mechanism that allows multiple operator to flexibly inter-operate via multiple Sm-GWs with a multitude of small cells has been proposed. This dissertation also comprehensively survey the studies that examine the SDN paradigm in optical networks. Along with the PHY functional split improvements, the performance of Distributed Converged Cable Access Platform (DCCAP) in the cable architectures especially for the Remote-PHY and Remote-MACPHY nodes has been evaluated. In the PHY functional split, in addition to the re-use of infrastructure with a common FFT module for multiple technologies, a novel cross functional split interaction to cache the repetitive QAM symbols across time at the remote node to reduce the transmission rate requirement of the fronthaul link has been proposed.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
MeDICINE: Rapid Prototyping of Production-Ready Network Services in Multi-PoP Environments
Virtualized network services consisting of multiple individual network
functions are already today deployed across multiple sites, so called multi-PoP
(points of presence) environ- ments. This allows to improve service performance
by optimizing its placement in the network. But prototyping and testing of
these complex distributed software systems becomes extremely challenging. The
reason is that not only the network service as such has to be tested but also
its integration with management and orchestration systems. Existing solutions,
like simulators, basic network emulators, or local cloud testbeds, do not
support all aspects of these tasks. To this end, we introduce MeDICINE, a novel
NFV prototyping platform that is able to execute production-ready network func-
tions, provided as software containers, in an emulated multi-PoP environment.
These network functions can be controlled by any third-party management and
orchestration system that connects to our platform through standard interfaces.
Based on this, a developer can use our platform to prototype and test complex
network services in a realistic environment running on his laptop.Comment: 6 pages, pre-prin
QoE on media deliveriy in 5G environments
231 p.5G expandirá las redes móviles con un mayor ancho de banda, menor latencia y la capacidad de proveer conectividad de forma masiva y sin fallos. Los usuarios de servicios multimedia esperan una experiencia de reproducción multimedia fluida que se adapte de forma dinámica a los intereses del usuario y a su contexto de movilidad. Sin embargo, la red, adoptando una posición neutral, no ayuda a fortalecer los parámetros que inciden en la calidad de experiencia. En consecuencia, las soluciones diseñadas para realizar un envío de tráfico multimedia de forma dinámica y eficiente cobran un especial interés. Para mejorar la calidad de la experiencia de servicios multimedia en entornos 5G la investigación llevada a cabo en esta tesis ha diseñado un sistema múltiple, basado en cuatro contribuciones.El primer mecanismo, SaW, crea una granja elástica de recursos de computación que ejecutan tareas de análisis multimedia. Los resultados confirman la competitividad de este enfoque respecto a granjas de servidores. El segundo mecanismo, LAMB-DASH, elige la calidad en el reproductor multimedia con un diseño que requiere una baja complejidad de procesamiento. Las pruebas concluyen su habilidad para mejorar la estabilidad, consistencia y uniformidad de la calidad de experiencia entre los clientes que comparten una celda de red. El tercer mecanismo, MEC4FAIR, explota las capacidades 5G de analizar métricas del envío de los diferentes flujos. Los resultados muestran cómo habilita al servicio a coordinar a los diferentes clientes en la celda para mejorar la calidad del servicio. El cuarto mecanismo, CogNet, sirve para provisionar recursos de red y configurar una topología capaz de conmutar una demanda estimada y garantizar unas cotas de calidad del servicio. En este caso, los resultados arrojan una mayor precisión cuando la demanda de un servicio es mayor
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