295 research outputs found
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 TelemĂĄticaDoctorado en TecnologĂas de la InformaciĂłn y las Telecomunicacione
Genetic algorithm for holistic VNF-mapping and virtual topology design
ProducciĂłn CientĂficaNext generation of Internet of Things (IoT) services imposes stringent requirements to the future networks that current ones cannot fulfill. 5G is a technology born to give response to those requirements. However, the deployment of 5G is also accompanied by profound architectural changes in the network, including the introduction of technologies like multi-access edge computing (MEC), software defined networking (SDN), and network function virtualization (NFV). In particular, NFV poses diverse challenges like virtual network function (VNF) placement and chaining, also called VNF-mapping. In this paper, we present an algorithm that solves VNF-placement and chaining in a metro WDM optical network equipped with MEC resources. Therefore, it solves the VNF-mapping in conjunction with the virtual topology design of the underlying optical backhaul network. Moreover, a version of the method providing protection against node failures is also presented. A simulation study is presented to show the importance of designing the three problems jointly, in contrast to other proposals of the literature that do not take the design of the underlying network into consideration when solving that problem. Furthermore, this paper also shows the advantages of using collaboration between MEC nodes to solve the VNF-mapping problem and the advantage of using shared protection schemes. The new algorithm outperforms other proposals in terms of both service blocking ratio, and number of active CPUs (thus reducing energy consumption). Finally, the impact of deploying different physical topologies for the optical backhaul network is also presented.Ministerio de EconomĂa, Industria y Competitividad (grant TEC2017-84423-C3-1-P)Ministerio de Industria, Comercio y Turismo (grant BES 2015-074514)Spanish Thematic Network (contract RED2018-102585-T)INTERREG V-A España-Portugal (POCTEP) program (project 0677_DISRUPTIVE_2_E
Artificial intelligence (AI) methods in optical networks: A comprehensive survey
ProducciĂłn CientĂficaArtificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks. The use of AI-based techniques is first studied in applications related to optical transmission, ranging from the characterization and operation of network components to performance monitoring, mitigation of nonlinearities, and quality of transmission estimation. Then, applications related to optical network control and management are also reviewed, including topics like optical network planning and operation in both transport and access networks. Finally, the paper also presents a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future.Ministerio de EconomĂa, Industria y Competitividad (Project EC2014-53071-C3-2-P, TEC2015-71932-REDT
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
Joint dimensioning of server and network infrastructure for resilient optical grids/clouds
We address the dimensioning of infrastructure, comprising both network and server resources, for large-scale decentralized distributed systems such as grids or clouds. We design the resulting grid/cloud to be resilient against network link or server failures. To this end, we exploit relocation: Under failure conditions, a grid job or cloud virtual machine may be served at an alternate destination (i.e., different from the one under failure-free conditions). We thus consider grid/cloud requests to have a known origin, but assume a degree of freedom as to where they end up being served, which is the case for grid applications of the bag-of-tasks (BoT) type or hosted virtual machines in the cloud case. We present a generic methodology based on integer linear programming (ILP) that: 1) chooses a given number of sites in a given network topology where to install server infrastructure; and 2) determines the amount of both network and server capacity to cater for both the failure-free scenario and failures of links or nodes. For the latter, we consider either failure-independent (FID) or failure-dependent (FD) recovery. Case studies on European-scale networks show that relocation allows considerable reduction of the total amount of network and server resources, especially in sparse topologies and for higher numbers of server sites. Adopting a failure-dependent backup routing strategy does lead to lower resource dimensions, but only when we adopt relocation (especially for a high number of server sites): Without exploiting relocation, potential savings of FD versus FID are not meaningful
Particle swarm optimization for routing and wavelength assignment in next generation WDM networks.
PhDAll-optical Wave Division Multiplexed (WDM) networking is a promising technology for long-haul backbone and large metropolitan optical networks in order to meet the non-diminishing bandwidth demands of future applications and services. Examples could include archival and recovery of data to/from Storage Area Networks (i.e. for banks), High bandwidth medical imaging (for remote operations), High Definition (HD) digital broadcast and streaming over the Internet, distributed orchestrated computing, and peak-demand short-term connectivity for Access Network providers and wireless network operators for backhaul surges. One desirable feature is fast and automatic provisioning. Connection (lightpath) provisioning in optically switched networks requires both route computation and a single wavelength to be assigned for the lightpath. This is called Routing and Wavelength Assignment (RWA). RWA can be classified as static RWA and dynamic RWA. Static RWA is an NP-hard (non-polynomial time hard) optimisation task. Dynamic RWA is even more challenging as connection requests arrive dynamically, on-the-fly and have random connection holding times. Traditionally, global-optimum mathematical search schemes like integer linear programming and graph colouring are used to find an optimal solution for NP-hard problems. However such schemes become unusable for connection provisioning in a dynamic environment, due to the computational complexity and time required to undertake the search. To perform dynamic provisioning, different heuristic and stochastic techniques are used.
Particle Swarm Optimisation (PSO) is a population-based global optimisation scheme that belongs to the class of evolutionary search algorithms and has successfully been used to solve many NP-hard optimisation problems in both static and dynamic environments. In this thesis, a novel PSO based scheme is proposed to solve the static RWA case, which can achieve optimal/near-optimal solution. In order to reduce the risk of premature convergence of the swarm and to avoid selecting local optima, a search scheme is proposed to solve the static RWA, based on the position of swarmâs global best particle and personal best position of each particle.
To solve dynamic RWA problem, a PSO based scheme is proposed which can provision a connection within a fraction of a second. This feature is crucial to provisioning services like bandwidth on demand connectivity. To improve the convergence speed of the swarm towards an optimal/near-optimal solution, a novel chaotic factor is introduced into the PSO algorithm, i.e. CPSO, which helps the swarm reach a relatively good solution in fewer iterations. Experimental results for PSO/CPSO based dynamic RWA algorithms show that the proposed schemes perform better compared to other evolutionary techniques like genetic algorithms, ant colony optimization. This is both in terms of quality of solution and computation time. The proposed schemes also show significant improvements in blocking probability performance compared to traditional dynamic RWA schemes like SP-FF and SP-MU algorithms
- âŠ