208 research outputs found
Dynamic VNF Placement, Resource Allocation and Traffic Routing in 5G  
5G networks are going to support a variety of vertical services, with a
diverse set of key performance indicators (KPIs), by using enabling
technologies such as software-defined networking and network function
virtualization. It is the responsibility of the network operator to efficiently
allocate the available resources to the service requests in such a way to honor
KPI requirements, while accounting for the limited quantity of available
resources and their cost. A critical challenge is that requests may be highly
varying over time, requiring a solution that accounts for their dynamic
generation and termination. With this motivation, we seek to make joint
decisions for request admission, resource activation, VNF placement, resource
allocation, and traffic routing. We do so by considering real-world aspects
such as the setup times of virtual machines, with the goal of maximizing the
mobile network operator profit. To this end, first, we formulate a one-shot
optimization problem which can attain the optimum solution for small size
problems given the complete knowledge of arrival and departure times of
requests over the entire system lifespan. We then propose an efficient and
practical heuristic solution that only requires this knowledge for the next
time period and works for realistically-sized scenarios. Finally, we evaluate
the performance of these solutions using real-world services and large-scale
network topologies. {Results demonstrate that our heuristic solution performs
better than a state-of-the-art online approach and close to the optimum
View on 5G Architecture: Version 2.0
The 5G Architecture Working Group as part of the 5GPPP Initiative is looking at capturing novel trends and key technological enablers for the realization of the 5G architecture. It also targets at presenting in a harmonized way the architectural concepts developed in various projects and initiatives (not limited to 5GPPP projects only) so as to provide a consolidated view on the technical directions for the architecture design in the 5G era. The first version of the white paper was released in July 2016, which captured novel trends and key technological enablers for the realization of the 5G architecture vision along with harmonized architectural concepts from 5GPPP Phase 1 projects and initiatives. Capitalizing on the architectural vision and framework set by the first version of the white paper, this Version 2.0 of the white paper presents the latest findings and analyses with a particular focus on the concept evaluations, and accordingly it presents the consolidated overall architecture design
A Survey of Deep Learning for Data Caching in Edge Network
The concept of edge caching provision in emerging 5G and beyond mobile
networks is a promising method to deal both with the traffic congestion problem
in the core network as well as reducing latency to access popular content. In
that respect end user demand for popular content can be satisfied by
proactively caching it at the network edge, i.e, at close proximity to the
users. In addition to model based caching schemes learning-based edge caching
optimizations has recently attracted significant attention and the aim
hereafter is to capture these recent advances for both model based and data
driven techniques in the area of proactive caching. This paper summarizes the
utilization of deep learning for data caching in edge network. We first outline
the typical research topics in content caching and formulate a taxonomy based
on network hierarchical structure. Then, a number of key types of deep learning
algorithms are presented, ranging from supervised learning to unsupervised
learning as well as reinforcement learning. Furthermore, a comparison of
state-of-the-art literature is provided from the aspects of caching topics and
deep learning methods. Finally, we discuss research challenges and future
directions of applying deep learning for cachin
Virtualisation and resource allocation in MECEnabled metro optical networks
The appearance of new network services and the ever-increasing network traffic and number
of connected devices will push the evolution of current communication networks towards the
Future Internet.
In the area of optical networks, wavelength routed optical networks (WRONs) are evolving
to elastic optical networks (EONs) in which, thanks to the use of OFDM or Nyquist WDM,
it is possible to create super-channels with custom-size bandwidth. The basic element in
these networks is the lightpath, i.e., all-optical circuits between two network nodes. The
establishment of lightpaths requires the selection of the route that they will follow and the
portion of the spectrum to be used in order to carry the requested traffic from the source to
the destination node. That problem is known as the routing and spectrum assignment (RSA)
problem, and new algorithms must be proposed to address this design problem.
Some early studies on elastic optical networks studied gridless scenarios, in which a slice
of spectrum of variable size is assigned to a request. However, the most common approach to
the spectrum allocation is to divide the spectrum into slots of fixed width and allocate multiple,
consecutive spectrum slots to each lightpath, depending on the requested bandwidth. Moreover,
EONs also allow the proposal of more flexible routing and spectrum assignment techniques,
like the split-spectrum approach in which the request is divided into multiple "sub-lightpaths".
In this thesis, four RSA algorithms are proposed combining two different levels of
flexibility with the well-known k-shortest paths and first fit heuristics. After comparing the
performance of those methods, a novel spectrum assignment technique, Best Gap, is proposed
to overcome the inefficiencies emerged when combining the first fit heuristic with highly
flexible networks. A simulation study is presented to demonstrate that, thanks to the use of
Best Gap, EONs can exploit the network flexibility and reduce the blocking ratio.
On the other hand, operators must face profound architectural changes to increase the
adaptability and flexibility of networks and ease their management. Thanks to the use of
network function virtualisation (NFV), the necessary network functions that must be applied
to offer a service can be deployed as virtual appliances hosted by commodity servers, which
can be located in data centres, network nodes or even end-user premises. The appearance of
new computation and networking paradigms, like multi-access edge computing (MEC), may
facilitate the adaptation of communication networks to the new demands. Furthermore, the
use of MEC technology will enable the possibility of installing those virtual network functions
(VNFs) not only at data centres (DCs) and central offices (COs), traditional hosts of VFNs, but
also at the edge nodes of the network. Since data processing is performed closer to the enduser,
the latency associated to each service connection request can be reduced. MEC nodes
will be usually connected between them and with the DCs and COs by optical networks.
In such a scenario, deploying a network service requires completing two phases: the
VNF-placement, i.e., deciding the number and location of VNFs, and the VNF-chaining,
i.e., connecting the VNFs that the traffic associated to a service must transverse in order to
establish the connection. In the chaining process, not only the existence of VNFs with available
processing capacity, but the availability of network resources must be taken into account to
avoid the rejection of the connection request. Taking into consideration that the backhaul of
this scenario will be usually based on WRONs or EONs, it is necessary to design the virtual
topology (i.e., the set of lightpaths established in the networks) in order to transport the tra c
from one node to another. The process of designing the virtual topology includes deciding the
number of connections or lightpaths, allocating them a route and spectral resources, and finally
grooming the traffic into the created lightpaths.
Lastly, a failure in the equipment of a node in an NFV environment can cause the
disruption of the SCs traversing the node. This can cause the loss of huge amounts of data
and affect thousands of end-users. In consequence, it is key to provide the network with faultmanagement
techniques able to guarantee the resilience of the established connections when a
node fails.
For the mentioned reasons, it is necessary to design orchestration algorithms which solve
the VNF-placement, chaining and network resource allocation problems in 5G networks
with optical backhaul. Moreover, some versions of those algorithms must also implements
protection techniques to guarantee the resilience system in case of failure.
This thesis makes contribution in that line. Firstly, a genetic algorithm is proposed to solve
the VNF-placement and VNF-chaining problems in a 5G network with optical backhaul based
on star topology: GASM (genetic algorithm for effective service mapping). Then, we propose
a modification of that algorithm in order to be applied to dynamic scenarios in which the
reconfiguration of the planning is allowed. Furthermore, we enhanced the modified algorithm
to include a learning step, with the objective of improving the performance of the algorithm.
In this thesis, we also propose an algorithm to solve not only the VNF-placement and
VNF-chaining problems but also the design of the virtual topology, considering that a WRON
is deployed as the backhaul network connecting MEC nodes and CO. Moreover, a version
including individual VNF protection against node failure has been also proposed and the
effect of using shared/dedicated and end-to-end SC/individual VNF protection schemes are
also analysed.
Finally, a new algorithm that solves the VNF-placement and chaining problems and
the virtual topology design implementing a new chaining technique is also proposed.
Its corresponding versions implementing individual VNF protection are also presented.
Furthermore, since the method works with any type of WDM mesh topologies, a technoeconomic
study is presented to compare the effect of using different network topologies in
both the network performance and cost.Departamento de TeorĂa de la Señal y Comunicaciones e IngenierĂa TelemáticaDoctorado en TecnologĂas de la InformaciĂłn y las Telecomunicacione
VNF placement optimization at the edge and cloud
Network Function Virtualization (NFV) has revolutionized the way network services are offered to end users. Individual network functions are decoupled from expensive and dedicated middleboxes and are now provided as software-based virtualized entities called Virtualized Network Functions (VNFs). NFV is often complemented with the Cloud Computing paradigm to provide networking functions t
Study and application of machine learning techniques to the deployment of services on 5G optical networks
The vision of the future 5G corresponds to a highly heterogeneous network at different levels; the increment in the number of services requests for the 5G networks imposes several technical challenges. In the 5G context, in the recent years, several machine learning-based approaches have been demonstrated as useful tools for making easier the networks’ management, by considering that different unexpected events could make that the services cannot be satisfied at the moment they are requested. Such approaches are usually referred as cognitive network management. There are too many parameters inside the 5G network affecting each layer of the network; the virtualization and abstraction of the services is a crucial part for a satisfactory service deployment, being the monitoring and control of the different planes the two keys inside the cognitive network management. In this project it has been addressed the implementation of a simulated data collector as well as the study of several machine learning-based approaches. This way, possible future performance can be predicted, giving to the system the ability to change the initial parameters and to adapt the network to future demands
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