15 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)
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)
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
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)
Performance Modeling of Softwarized Network Services Based on Queuing Theory with Experimental Validation
Network Functions Virtualization facilitates the automation of the scaling of softwarized network services (SNSs).
However, the realization of such a scenario requires a way to
determine the needed amount of resources so that the SNSs performance requisites are met for a given workload. This problem is
known as resource dimensioning, and it can be efficiently tackled
by performance modeling. In this vein, this paper describes an
analytical model based on an open queuing network of G/G/m
queues to evaluate the response time of SNSs. We validate our
model experimentally for a virtualized Mobility Management
Entity (vMME) with a three-tiered architecture running on
a testbed that resembles a typical data center virtualization
environment. We detail the description of our experimental
setup and procedures. We solve our resulting queueing network
by using the Queueing Networks Analyzer (QNA), Jackson’s
networks, and Mean Value Analysis methodologies, and compare
them in terms of estimation error. Results show that, for medium
and high workloads, the QNA method achieves less than half of
error compared to the standard techniques. For low workloads,
the three methods produce an error lower than 10%. Finally,
we show the usefulness of the model for performing the dynamic
provisioning of the vMME experimentally.This work has been partially funded by the H2020 research
and innovation project 5G-CLARITY (Grant No. 871428)National research
project 5G-City: TEC2016-76795-C6-4-RSpanish Ministry of
Education, Culture and Sport (FPU Grant 13/04833). We would also like to
thank the reviewers for their valuable feedback to enhance the quality
and contribution of this wor