157 research outputs found
Artificial intelligence (AI) methods in optical networks: A comprehensive survey
Producción CientíficaArtificial 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 Economía, Industria y Competitividad (Project EC2014-53071-C3-2-P, TEC2015-71932-REDT
A Brief Analysis of Gravitational Search Algorithm (GSA) Publication from 2009 to May 2013
Gravitational Search Algorithm was introduced in year 2009. Since its introduction, the academic community shows a
great interest on this algorith. This can be seen by the high number of publications with a short span of time. This paper analyses the publication trend of Gravitational Search Algorithm since its introduction until May 2013. The objective of this paper is to give exposure to reader the publication trend in the area of Gravitational Search Algorithm
Evolution towards Smart Optical Networking: Where Artificial Intelligence (AI) meets the World of Photonics
Smart optical networks are the next evolution of programmable networking and
programmable automation of optical networks, with human-in-the-loop network
control and management. The paper discusses this evolution and the role of
Artificial Intelligence (AI)
Intelligent design of optical networks: which topology features help maximise throughput in the nonlinear regime?
The overarching goal in intelligent network design is to deliver capacity when and where it is
needed. The key to this is to understand which network topology characteristics impact the achievable
network throughput. This is explored through the use of a new generative network model, taking into
account physical layer network characteristics
Intelligent performance inference: A graph neural network approach to modeling maximum achievable throughput in optical networks
One of the key performance metrics for optical networks is the maximum achievable throughput for a given network. Determining it, however, is a nondeterministic polynomial time (NP) hard optimization problem, often solved via computationally expensive integer linear programming (ILP) formulations. These are infeasible to implement as objectives, even on very small node scales of a few tens of nodes. Alternatively, heuristics are used although these, too, require considerable computation time for a large number of networks. There is, thus, a need for an ultra-fast and accurate performance evaluation of optical networks. For the first time, we propose the use of a geometric deep learning model, message passing neural networks (MPNNs), to learn the relationship between node and edge features, the network structure, and the maximum achievable network throughput. We demonstrate that MPNNs can accurately predict the maximum achievable throughput while reducing the computational time by up to five-orders of magnitude compared to the ILP for small networks (10–15 nodes) and compared to a heuristic for large networks (25–100 nodes)—proving their suitability for the design and optimization of optical networks on different time- and distance-scales
ENERGY EFFICIENT WIRED NETWORKING
This research proposes a new dynamic energy management framework for a backbone Internet Protocol over Dense Wavelength Division Multiplexing (IP over DWDM) network. Maintaining the logical IP-layer topology is a key constraint of our architecture whilst saving energy by infrastructure sleeping and virtual router migration.
The traffic demand in a Tier 2/3 network typically has a regular diurnal pattern based on people‟s activities, which is high in working hours and much lighter during hours associated with sleep. When the traffic demand is light, virtual router instances can be consolidated to a smaller set of physical platforms and the unneeded physical platforms can be put to sleep to save energy. As the traffic demand increases the sleeping physical platforms can be re-awoken in order to host virtual router instances and so maintain quality of service.
Since the IP-layer topology remains unchanged throughout virtual router migration in our framework, there is no network disruption or discontinuities when the physical platforms enter or leave hibernation. However, this migration places extra demands on the optical layer as additional connections are needed to preserve the logical IP-layer topology whilst forwarding traffic to the new virtual router location. Consequently, dynamic optical connection management is needed for the new framework.
Two important issues are considered in the framework, i.e. when to trigger the virtual router migration and where to move virtual router instances to? For the first issue, a reactive mechanism is used to trigger the virtual router migration by monitoring the network state. Then, a new evolutionary-based algorithm called VRM_MOEA is proposed for solving the destination physical platform selection problem, which chooses the appropriate location of virtual router instances as traffic demand varies. A novel hybrid simulation platform is developed to measure the performance of new framework, which is able to capture the functionality of the optical layer, the IP layer data-path and the IP/optical control plane. Simulation results show that the performance of network energy saving depends on many factors, such as network topology, quiet and busy thresholds, and traffic load; however, savings of around 30% are possible with typical medium-sized network topologies
Recommended from our members
Scalable Capacity Estimation for Nonlinear Elastic All--Optical Core Networks
Routing and wavelength assignment (RWA) algorithms must strike a balance between finding routes with high quality of transmission (QoT) and finding routes that will not interfere with allocating future traffic. Too much emphasis on the first will concentrate traffic along major routes causing congestion whilst too much emphasis on the second will cause individual transceivers to operate below their capabilities increasing both cost and power consumption. This paper presents a low--complexity algorithm that shows that focusing on wavelength packing allows for greater overall traffic whilst giving only slight penalties for latency and required transceivers. Our algorithm comfortably outperforms kSP--FF routing for the same complexity and typically betters congestion aware routing whilst reducing complexity. We show these results on 4 simplified networks based on deployed topologies before replicating them on 2,000 artificially generated topologies based on real node locations in Germany and the USA. Capacity for each topology was found with an integer linear program to which our algorithm compares favorably suggesting it provides a scalable alternative to global optimization.RJV thanks EPSRC and BT through an iCASE studentship EP/N509103/1
1775341; DJI and SJS thank the EPSRC Programme Grant TRANSNET EP/R035342/
A metaheuristic and simheuristic approach for the p-Hub median problem from a telecommunication perspective
Tese (doutorado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2018.Avanços recentes no setor das telecomunicações oferecem grandes oportunidades para cidadãos
e organizações em um mundo globalmente conectado, ao mesmo tempo em que surge um vasto
número de desafios complexos que os engenheiros devem enfrentar. Alguns desses desafios podem
ser modelados como problemas de otimização. Alguns exemplos incluem o problema de alocação
de recursos em redes de comunicações, desenho de topologias de rede que satisfaça determinadas
propriedades associadas a requisitos de qualidade de serviço, sobreposição de redes multicast e
outros recursos importantes para comunicação de origem a destino.
O primeiro objetivo desta tese é fornecer uma revisão sobre como as metaheurísticas têm sido
usadas até agora para lidar com os problemas de otimização associados aos sistemas de telecomunicações, detectando as principais tendências e desafios. Particularmente, a análise enfoca os
problemas de desenho, roteamento e alocação de recursos. Além disso, devido á natureza desses
desafios, o presente trabalho discute como a hibridização de metaheurísticas com metodologias
como simulação pode ser empregada para ampliar as capacidades das metaheurísticas na resolução
de problemas de otimização estocásticos na indústria de telecomunicações.
Logo, é analisado um problema de otimização com aplicações práticas para redes de telecomunica
ções: o problema das p medianas não capacitado em que um número fixo de hubs tem
capacidade ilimitada, cada nó não-hub é alocado para um único hub e o número de hubs é conhecido
de antemão, sendo analisado em cenários determinísticos e estocásticos. Dada a sua variedade
e importância prática, o problema das p medianas vem sendo aplicado e estudado em vários contextos.
Seguidamente, propõem-se dois algoritmos imune-inspirados e uma metaheurística de dois estágios, que se baseia na combinação de técnicas tendenciosas e aleatórias com uma estrutura de
busca local iterada, além de sua integração com a técnica de simulação de Monte Carlo para resolver
o problema das p medianas. Para demonstrar a eficiência dos algoritmos, uma série de testes
computacionais é realizada, utilizando instâncias de grande porte da literatura. Estes resultados
contribuem para uma compreensão mais profunda da eficácia das metaheurísticas empregadas
para resolver o problema das p medianas em redes pequenas e grandes. Por último, uma aplicaçã
o ilustrativa do problema das p medianas é apresentada, bem como alguns insights sobre novas
possibilidades para ele, estendendo a metodologia proposta para ambientes da vida real.Recent advances in the telecommunication industry o er great opportunities to citizens and
organizations in a globally-connected world, but they also arise a vast number of complex challenges
that decision makers must face. Some of these challenges can be modeled as optimization
problems. Examples include the framework of network utility maximization for resource allocation
in communication networks, nding a network topology that satis es certain properties associated
with quality of service requirements, overlay multicast networks, and other important features for
source to destination communication.
First, this thesis provides a review on how metaheuristics have been used so far to deal with
optimization problems associated with telecommunication systems, detecting the main trends and
challenges. Particularly the analysis focuses on the network design, routing, and allocation problems.
In addition, due to the nature of these challenges, this work discusses how the hybridization
of metaheuristics with methodologies such as simulation can be employed to extend the capabilities
of metaheuristics when solving stochastic optimization problems.
Then, a popular optimization problem with practical applications to the design of telecommunication
networks: the Uncapacitated Single Allocation p-Hub Median Problem (USApHMP) where
a xed number of hubs have unlimited capacity, each non-hub node is allocated to a single hub
and the number of hubs is known in advance is analyzed in deterministic and stochastic scenarios.
p-hub median problems are concerned with optimality of telecommunication and transshipment
networks, and seek to minimize the cost of transportation or establishing.
Next, two immune inspired metaheuristics are proposed to solve the USApHMP, besides that,
a two-stage metaheuristic which relies on the combination of biased-randomized techniques with
an iterated local search framework and its integration with simulation Monte Carlo technique for
solving the same problem is proposed. In order to show their e ciency, a series of computational
tests are carried out using small and large size instances from the literature. These results contribute
to a deeper understanding of the e ectiveness of the employed metaheuristics for solving
the USApHMP in small and large networks. Finally, an illustrative application of the USApHMP
is presented as well as some insights about some new possibilities for it, extending the proposed
methodology to real-life environments.Els últims avenços en la industria de les telecomunicacions ofereixen grans oportunitats per
ciutadans i organitzacions en un món globalment connectat, però a la vegada, presenten reptes als
que s'enfronten tècnics i enginyers que prenen decisions. Alguns d'aquests reptes es poden modelitzar
com problemes d'optimització. Exemples inclouen l'assignació de recursos a les xarxes de
comunicació, trobant una topologia de xarxa que satisfà certes propietats associades a requisits de
qualitat de servei, xarxes multicast superposades i altres funcions importants per a la comunicació
origen a destinació.
El primer objectiu d'aquest treball és proporcionar un revisió de la literatura sobre com s'han
utilitzat aquestes tècniques, tradicionalment, per tractar els problemes d'optimització associats a
sistemes de telecomunicació, detectant les principals tendències i desa aments. Particularment,
l'estudi es centra en els problemes de disseny de xarxes, enrutament i problemes d'assignació de
recursos. Degut a la naturalesa d'aquests problemes, aquest treball també analitza com es poden
combinar les tècniques metaheurístiques amb metodologies de simulació per ampliar les capacitats
de resoldre problemes d'optimització estocàstics.
A més, es tracta un popular problema d'optimització amb aplicacions pràctiques per xarxes de
telecomunicació, el problema de la p mediana no capacitat, analitzant-lo des d'escenaris deterministes
i estocàstics. Aquest problema consisteix en determinar el nombre d'instal lacions (medianes)
en una xarxa, minimitzant la suma de tots els costs o distàncies des d'un punt de demanda a la
instal lació més propera. En general, el problema de la p mediana està lligat amb l'optimització de
xarxes de telecomunicacions i de transport, i busquen minimitzar el cost de transport o establiment
de la xarxa.
Es proposa dos algoritmes immunològics i un algoritme metaheurístic de dues etapes basat en
la combinació de tècniques aleatòries amb simulacions Monte Carlo. L'e ciència de les algoritmes
es posa a prova mitjançant alguns dels test computacionals més utilitzats a la literatura, obtenint
uns resultats molt satisfactoris, ja que es capaç de resoldre casos petits i grans en qüestió de segons i amb un baix cost computacional. Finalment, es presenta una aplicació il lustrativa del problema
de la p mediana, així com algunes noves idees sobre aquest, que estenen la metodologia proposta
a problemes de la vida real
A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions
In this paper, a review on the three most important communication techniques (ground, aerial, and underwater vehicles) has been presented that throws light on trajectory planning, its optimization, and various issues in a summarized way. This kind of extensive research is not often seen in the literature, so an effort has been made for readers interested in path planning to fill the gap. Moreover, optimization techniques suitable for implementing ground, aerial, and underwater vehicles are also a part of this review. This paper covers the numerical, bio-inspired techniques and their hybridization with each other for each of the dimensions mentioned. The paper provides a consolidated platform, where plenty of available research on-ground autonomous vehicle and their trajectory optimization with the extension for aerial and underwater vehicles are documented
- …