19 research outputs found
Optimization of Flow Allocation in Asynchronous Deterministic 5G Transport Networks by Leveraging Data Analytics
This research work was supported in part by the Euro-
pean Union’s Horizon 2020 Research and Innovation Program
under the “Cloud for Holography and Augmented Reality
(CHARITY)” Project under Agreement 101016509, and 5G-
CLARITY Project under Agreement 871428. It is also partially
supported by the Spanish national research project TRUE5G:
PID2019-108713RB-C53.Time-Sensitive Networking (TSN) and Deterministic
Networking (DetNet) technologies are increasingly recognized as
key levers of the future 5G transport networks (TNs) due to their
capabilities for providing deterministic Quality-of-Service and
enabling the coexistence of critical and best-effort services. Addi-
tionally, they rely on programmable and cost-effective Ethernet-
based forwarding planes. This article addresses the flow alloca-
tion problem in 5G backhaul networks realized as asynchronous
TSN networks, whose building block is the Asynchronous Traffic
Shaper. We propose an offline solution, dubbed “Next Generation
Transport Network Optimizer” (NEPTUNO), that combines ex-
act optimization methods and heuristic techniques and leverages
data analytics to solve the flow allocation problem. NEPTUNO
aims to maximize the flow acceptance ratio while guaranteeing
the deterministic Quality-of-Service requirements of the critical
flows. We carried out a performance evaluation of NEPTUNO
regarding the degree of optimality, execution time, and flow
rejection ratio. Furthermore, we compare NEPTUNO with a
novel online baseline solution for two different optimization goals.
Online methods compute the flow’s allocation configuration right
after the flow arrives at the network, whereas offline solutions
like NEPTUNO compute a long-term configuration allocation for
the whole network. Our results highlight the potential of data
analytics for the self-optimization of the future 5G TNs.Union’s Horizon 2020, 1010165095G-CLARITY 871428TRUE5G: PID2019-108713RB-C53
Deep Reinforcement Learning based Collision Avoidance in UAV Environment
Unmanned Aerial Vehicles (UAVs) have recently
attracted both academia and industry representatives due to
their utilization in tremendous emerging applications. Most
UAV applications adopt Visual Line of Sight (VLOS) due to
ongoing regulations. There is a consensus between industry for
extending UAVs’ commercial operations to cover the urban and
populated area controlled airspace Beyond VLOS (BVLOS).
There is ongoing regulation for enabling BVLOS UAV management. Regrettably, this comes with unavoidable challenges
related to UAVs’ autonomy for detecting and avoiding static
and mobile objects. An intelligent component should either
be deployed onboard the UAV or at a Multi-Access Edge
Computing (MEC) that can read the gathered data from
different UAV’s sensors, process them, and then make the
right decision to detect and avoid the physical collision. The
sensing data should be collected using various sensors but
not limited to Lidar, depth camera, video, or ultrasonic. This
paper proposes probabilistic and Deep Reinforcement Learning
(DRL)-based algorithms for avoiding collisions while saving
energy consumption. The proposed algorithms can be either run
on top of the UAV or at the MEC according to the UAV capacity
and the task overhead. We have designed and developed
our algorithms to work for any environment without a need
for any prior knowledge. The proposed solutions have been
evaluated in a harsh environment that consists of many UAVs
moving randomly in a small area without any correlation. The
obtained results demonstrated the efficiency of these solutions
for avoiding the collision while saving energy consumption in
familiar and unfamiliar environments.This work has been partially funded by the Spanish national project TRUE-5G (PID2019-108713RB-C53)
Dynamic Resource Provisioning of a Scalable E2E Network Slicing Orchestration System
Network slicing allows different applications and
network services to be deployed on virtualized resources running
on a common underlying physical infrastructure. Developing
a scalable system for the orchestration of end-to-end (E2E)
mobile network slices requires careful planning and very reliable
algorithms. In this paper, we propose a novel E2E Network
Slicing Orchestration System (NSOS) and a Dynamic Auto-
Scaling Algorithm (DASA) for it. Our NSOS relies strongly on
the foundation of a hierarchical architecture that incorporates
dedicated entities per domain to manage every segment of the
mobile network from the access, to the transport and core
network part for a scalable orchestration of federated network
slices. The DASA enables the NSOS to autonomously adapt
its resources to changes in the demand for slice orchestration
requests (SORs) while enforcing a given mean overall time taken
by the NSOS to process any SOR. The proposed DASA includes
both proactive and reactive resource provisioning techniques).
The proposed resource dimensioning heuristic algorithm of the
DASA is based on a queuing model for the NSOS, which consists
of an open network of G/G/m queues. Finally, we validate the
proper operation and evaluate the performance of our DASA
solution for the NSOS by means of system-level simulations.This research work is partially supported by the European
Union’s Horizon 2020 research and innovation program under
the 5G!Pagoda project, the MATILDA project and the
Academy of Finland 6Genesis project with grant agreement
No. 723172, No. 761898 and No. 318927, respectively. It was
also partially funded by the Academy of Finland Project CSN
- under Grant Agreement 311654 and the Spanish Ministry of
Education, Culture and Sport (FPU Grant 13/04833), and the
Spanish Ministry of Economy and Competitiveness and the
European Regional Development Fund (TEC2016-76795-C6-
4-R)
NarrowBand IoT Data Transmission Procedures for Massive Machine Type Communications
Large-scale deployments of massive Machine Type Communications (mMTC)
involve several challenges on cellular networks. To address the challenges of mMTC, or
more generally, Internet of Things (IoT), the 3rd Generation Partnership Project has
developed NarrowBand IoT (NB-IoT) as part of Release 13. NB-IoT is designed to
provide better indoor coverage, support of a massive number of low-throughput devices,
with relaxed delay requirements, and lower-energy consumption. NB-IoT reuses Long
Term Evolution functionality with simplifications and optimizations. Particularly for small
data transmissions, NB-IoT specifies two procedures to reduce the required signaling:
one of them based on the Control Plane (CP), and the other on the User Plane (UP). In
this work, we provide an overview of these procedures as well as an evaluation of their
performance. The results of the energy consumption show both optimizations achieve
a battery lifetime extension of more than 2 years for a large range in the considered
cases, and up to 8 years for CP with good coverage. In terms of cell capacity relative to
SR, CP achieves gains from 26% to 224%, and UP ranges from 36% to 165%. The
comparison of CP and UP optimizations yields similar results, except for some specific
configurations.This work is partially supported by the Spanish
Ministry of Economy and Competitiveness and
the European Regional Development Fund (Projects TIN2013-46223-P, and TEC2016-76795-
C6-4-R), and the Spanish Ministry of Education,
Culture and Sport (FPU Grant 13/04833)
Asynchronous Time-Sensitive Networking for Industrial Networks
Time-Sensitive Networking (TSN) is expected to be a
cornerstone in tomorrow’s industrial networks. That is because of
its ability to provide deterministic quality-of-service in terms of
delay, jitter, and scalability. Moreover, it enables more scalable,
more affordable, and easier to manage and operate networks
compared to current industrial networks, which are based on
Industrial Ethernet. In this article, we evaluate the maximum
capacity of the asynchronous TSN networks to accommodate
industrial traffic flows. To that end, we formally formulate the
flow allocation problem in the mentioned networks as a convex
mixed-integer non-linear program. To the best of the authors’
knowledge, neither the maximum utilization of the asynchronous
TSN networks nor the formulation of the flow allocation problem
in those networks have been previously addressed in the literature.
The results show that the network topology and the traffic matrix
highly impact on the link utilization.This work has been partially funded by the H2020 research
and innovation project 5G-CLARITY (Grant No. 871428), national
research project TRUE5G: PID2019-108713RB-C5
5G Infrastructure Network Slicing: E2E Mean Delay Model and Effectiveness Assessment to Reduce Downtimes in Industry 4.0
This work has been partially funded by the H2020 project 5G-CLARITY (Grant No. 871428) and the Spanish national project TRUE-5G (PID2019-108713RB-C53).Fifth Generation (5G) is expected to meet stringent performance network requisites of
the Industry 4.0. Moreover, its built-in network slicing capabilities allow for the support of the
traffic heterogeneity in Industry 4.0 over the same physical network infrastructure. However, 5G
network slicing capabilities might not be enough in terms of degree of isolation for many private
5G networks use cases, such as multi-tenancy in Industry 4.0. In this vein, infrastructure network
slicing, which refers to the use of dedicated and well isolated resources for each network slice at every
network domain, fits the necessities of those use cases. In this article, we evaluate the effectiveness of
infrastructure slicing to provide isolation among production lines (PLs) in an industrial private 5G
network. To that end, we develop a queuing theory-based model to estimate the end-to-end (E2E)
mean packet delay of the infrastructure slices. Then, we use this model to compare the E2E mean
delay for two configurations, i.e., dedicated infrastructure slices with segregated resources for each
PL against the use of a single shared infrastructure slice to serve the performance-sensitive traffic
from PLs. Also we evaluate the use of Time-Sensitive Networking (TSN) against bare Ethernet to
provide layer 2 connectivity among the 5G system components. We use a complete and realistic
setup based on experimental and simulation data of the scenario considered. Our results support the
effectiveness of infrastructure slicing to provide isolation in performance among the different slices.
Then, using dedicated slices with segregated resources for each PL might reduce the number of the
production downtimes and associated costs as the malfunctioning of a PL will not affect the network
performance perceived by the performance-sensitive traffic from other PLs. Last, our results show
that, besides the improvement in performance, TSN technology truly provides full isolation in the
transport network compared to standard Ethernet thanks to traffic prioritization, traffic regulation,
and bandwidth reservation capabilities.H2020 project 5G-CLARITY 871428Spanish Government PID2019-108713RB-C53TRUE-5
Reduced M2M Signaling Communications in 3GPP LTE and Future 5G Cellular Networks
The increase of machine-to-machine (M2M) communications
over cellular networks imposes new requirements
and challenges that current networks have to handle with. Many
M2M UEs (User Equipment) may send small infrequent data,
which suppose a challenge for cellular networks not optimized
for such traffic, where signaling load could increase significantly
and cause congestion over the network. This paper evaluates
current proposals to manage small transmissions over the Long
Term Evolution (LTE) cellular network. We also propose a new
Random Access-based Small IP packet Transmission (RASIPT)
procedure for M2M UEs small data transmissions. Its main
feature is data transfer without establishment of Radio Resource
Control (RRC) connection to reduce signaling overhead. In
our design, we assume a Software Defined Networking-based
architecture for 5G system. When compared with current LTE
scheme, our procedure reduces significantly the signaling load
generated by M2M UEs small transmissions.This work is partially supported by the Spanish Ministry
of Economy and Competitiveness (project TIN2013-
46223-P), FEDER and the Spanish Ministry of Education,
Culture and Sport (FPU grant 13/04833)
Modeling and Dimensioning of a Virtualized MME for 5G Mobile Networks
Network function virtualization is considered one of
the key technologies for developing future mobile networks. In this
paper, we propose a theoretical framework to evaluate the performance of a Long-Term Evolution (LTE) virtualized mobility management entity (vMME) hosted in a data center. This theoretical
framework consists of 1) a queuing network to model the vMME
in a data center and 2) analytic expressions to estimate the overall
mean system delay and the signaling workload to be processed by
the vMME. We validate our mathematical model by simulation.
One direct use of the proposed model is vMME dimensioning, i.e.,
to compute the number of vMME processing instances to provide
a target system delay given the number of users in the system.
Additionally, the paper includes a scalability analysis of the system. In our study, we consider the billing model and a data center
setup of Amazon Elastic Compute Cloud service and estimate the
processing time of MME processing instances for different LTE
control procedures experimentally. For the considered setup, our
results show that the vMME is scalable for signaling workloads
up to 37 000 LTE control procedures per second for a target mean
system delay of 1 ms. The system design and database performance
assumed imposes this limit in the system scalability.This work was supported in part by the Spanish Ministry of Economy
and Competitiveness and the European Regional Development Fund (project
TIN2013-46223-P) and in part by the Spanish Ministry of Education, Culture,
and Sport under FPU Grant 13/04833
Implementación de mecanismos de mitigación de tormentas de broadcast en redes de área local mediante Redes Definidas por Software
[ES] El uso de Ethernet como tecnologı́a de red pararedes corporativas se justifica por su bajo coste y facilidadde configuración y mantenimiento. Sin embargo, estas redesno son muy escalables, debido en parte a las inundacioneso tormentas de broadcasts, que afectan al rendimiento tantode los dispositivos de red como finales. Para mitigar elimpacto de las inundaciones por broadcast, se ha previstoutilizar técnicas de filtrado y caché en distintos nodos dela red. Sin embargo, el paradigma de Redes Definidas porSoftware permite definir nuevas aproximaciones, gracias ala capacidad de reprogramar la red de forma centralizaday flexible que proporciona. En este trabajo se aborda laimplementación de una red de área local con soporte parafiltrar algunos paquetes broadcast mediante la utilizaciónde Redes Definidas por Software. Esta solución permitirı́adesplegar redes de área local más amplias, adecuadas paralos requisitos de redes corporativas. Para ello, se describe eldesarrollo de filtros para varios protocolos de red, su imple-mentación en el controlador OpendayLight, y la evaluacióndel rendimiento obtenido.Este trabajo esta parcialmente financiado por el Ministerio de Economía, Industria y Competitividad y el
Fondo Europeo de Desarrollo Regional FEDER (proyectos TEC2016-76795-C6-4-R y TIN2013-46223-P).Valera Muros, B.; Prados Garzón, J.; Ramos-Munoz, JJ.; Navarro-Ortiz, J. (2017). Implementación de mecanismos de mitigación de tormentas de broadcast en redes de área local mediante Redes Definidas por Software. En XIII Jornadas de Ingeniería telemática (JITEL 2017). Libro de actas. Editorial Universitat Politècnica de València. 216-223. https://doi.org/10.4995/JITEL2017.2017.6585OCS21622
Link-Level Access Cloud Architecture Design Based on SDN for 5G Networks
The exponential growth of data traffic and connected devices, and the reduction of latency and costs, are considered major challenges for future mobile communication networks. The satisfaction of these challenges motivates revisiting the architecture of these networks. We propose an SDN-based design of a hierarchical architecture for the 5G packet core. In this article we focus on the design of its access cloud with the goal of providing low latency and scalable Ethernet-like support to terminals and MTC devices including mobility management. We examine and address its challenges in terms of network scalability and support for link-level mobility. We propose a link-level architecture that forwards frames from and to edge network elements (AP and routers) with a label that identifies the APs through which the terminal is reachable. An SDN local controller tracks and updates the users' location information at the edge network elements. Additionally, we propose to delegate in SDN local controllers the handling of non-scalable operations, such as broadcast and multicast messages, and network management procedures.This work is partially supported by the Spanish Ministry of Economy and
Competitiveness (project TIN2013-46223-P), and the Granada
Excellence Network of Innovation Laboratories (projects
GENIL-PYR-2014-20 and GENIL-PYR-2014-18)