2,418 research outputs found
Investigation of the tolerance of wavelength-routed optical networks to traffic load variations.
This thesis focuses on the performance of circuit-switched wavelength-routed optical network with unpredictable traffic pattern variations. This characteristic of optical networks is termed traffic forecast tolerance. First, the increasing volume and heterogeneous nature of data and voice traffic is discussed. The challenges in designing robust optical networks to handle unpredictable traffic statistics are described. Other work relating to the same research issues are discussed. A general methodology to quantify the traffic forecast tolerance of optical networks is presented. A traffic model is proposed to simulate dynamic, non-uniform loads, and used to test wavelength-routed optical networks considering numerous network topologies. The number of wavelengths required and the effect of the routing and wavelength allocation algorithm are investigated. A new method of quantifying the network tolerance is proposed, based on the calculation of the increase in the standard deviation of the blocking probabilities with increasing traffic load non-uniformity. The performance of different networks are calculated and compared. The relationship between physical features of the network topology and traffic forecast tolerance is investigated. A large number of randomly connected networks with different sizes were assessed. It is shown that the average lightpath length and the number of wavelengths required for full interconnection of the nodes in static operation both exhibit a strong correlation with the network tolerance, regardless of the degree of load non-uniformity. Finally, the impact of wavelength conversion on network tolerance is investigated. Wavelength conversion significantly increases the robustness of optical networks to unpredictable traffic variations. In particular, two sparse wavelength conversion schemes are compared and discussed: distributed wavelength conversion and localized wavelength conversion. It is found that the distributed wavelength conversion scheme outperforms localized wavelength conversion scheme, both with uniform loading and in terms of the network tolerance. The results described in this thesis can be used for the analysis and design of reliable WDM optical networks that are robust to future traffic demand variations
Towards cognitive in-operation network planning
Next-generation internet services such as live TV and video on demand require high bandwidth and ultra-low latency. The ever-increasing volume, dynamicity and stringent requirements of these servicesâ demands are generating new challenges to nowadays telecom networks. To decrease expenses, service-layer content providers are delivering their content near the end users, thus allowing a low latency and tailored content delivery. As a consequence of this, unseen metro and even core traffic dynamicity is arising with changes in the volume and direction of the traffic along the day.
A tremendous effort to efficiently manage networks is currently ongoing towards the realisation of 5G networks. This translates in looking for network architectures supporting dynamic resource allocation, fulfilling strict service requirements and minimising the total cost of ownership (TCO). In this regard, in-operation network planning was recently proven to successfully support various network reconfiguration use cases in prospective scenarios. Nevertheless, additional research to extend in-operation planning capabilities from typical reactive optimization schemes to proactive and predictive schemes based on the analysis of network monitoring data is required.
A hot topic raising increasing attention is cognitive networking, where an elevated knowledge about the network could be obtained as a result of introducing data analytics in the telecom operatorâs infrastructure. By using predictive knowledge about the network traffic, in-operation network planning mechanisms could be enhanced to efficiently adapt the network by means of future traffic prediction, thus achieving cognitive in-operation network planning.
In this thesis, we focus on studying mechanisms to enable cognitive in-operation network planning in core networks. In particular, we focus on dynamically reconfiguring virtual network topologies (VNT) at the MPLS layer, covering a number of detailed objectives. First, we start studying mechanisms to allow network traffic flow modelling, from monitoring and data transformation to the estimation of predictive traffic model based on this data. By means of these traffic models, then we tackle a cognitive approach to periodically adapt the core VNT to current and future traffic, using predicted traffic matrices based on origin-destination (OD) predictive models. This optimization approach, named VENTURE, is efficiently solved using dedicated heuristic algorithms and its feasibility is demonstrated in an experimental in-operation network planning environment. Finally, we extend VENTURE to consider core flows dynamicity as a result of metro flows re-routing, which represents a meaningful dynamic traffic scenario. This extension, which entails enhancements to coordinate metro and core network controllers with the aim of allowing fast adaption of core OD traffic models, is evaluated and validated in terms of traffic models accuracy and experimental feasibility.Els serveis dâinternet de nova generaciĂł tals com la televisiĂł en viu o el vĂdeo sota demanda requereixen dâun gran ample de banda i dâultra-baixa latĂšncia. Lâincrement continu del volum, dinamicitat i requeriments dâaquests serveis estĂ generant nous reptes pels teleoperadors de xarxa. Per reduir costs, els proveĂŻdors de contingut estan disposant aquests mĂ©s a prop dels usuaris finals, aconseguint aixĂ una entrega de contingut feta a mida. ConseqĂŒentment, estem presenciant una dinamicitat mai vista en el trĂ fic de xarxes de metro amb canvis en la direcciĂł i el volum del trĂ fic al llarg del dia. Actualment, sâestĂ duent a terme un gran esforç cap a la realitzaciĂł de xarxes 5G. Aquest esforç es tradueix en cercar noves arquitectures de xarxa que suportin lâassignaciĂł dinĂ mica de recursos, complint requeriments de servei estrictes i minimitzant el cost total de la propietat. En aquest sentit, recentment sâha demostrat com lâaplicaciĂł de âin-operation network planningâ permet exitosament suportar diversos casos dâĂșs de reconfiguraciĂł de xarxa en escenaris prospectius. No obstant, Ă©s necessari dur a terme mĂ©s recerca per tal dâestendre âin-operation network planningâ des dâun esquema reactiu dâoptimitzaciĂł cap a un nou esquema proactiu basat en lâanalĂtica de dades provinents del monitoritzat de la xarxa. El concepte de xarxes cognitives es tambĂ© troba al centre dâatenciĂł, on un elevat coneixement de la xarxa sâobtindria com a resultat dâintroduir analĂtica de dades en la infraestructura del teleoperador. Mitjançant un coneixement predictiu sobre el trĂ fic de xarxa, els mecanismes de in-operation network planning es podrien millorar per adaptar la xarxa eficientment basant-se en predicciĂł de trĂ fic, assolint aixĂ el que anomenem com a âcognitive in-operation network Planningâ. En aquesta tesi ens centrem en lâestudi de mecanismes que permetin establir âel cognitive in-operation network Planningâ en xarxes de core. En particular, ens centrem en reconfigurar dinĂ micament topologies de xarxa virtual (VNT) a la capa MPLS, cobrint una sĂšrie dâobjectius detallats. Primer comencem estudiant mecanismes pel modelat de fluxos de trĂ fic de xarxa, des del seu monitoritzat i transformaciĂł fins a lâestimaciĂł de models predictius de trĂ fic. Posteriorment, i mitjançant aquests models predictius, tractem un esquema cognitiu per adaptar periĂČdicament la VNT utilitzant matrius de trĂ fic basades en predicciĂł de parells origen-destĂ (OD). Aquesta optimitzaciĂł, anomenada VENTURE, Ă©s resolta eficientment fent servir heurĂstiques dedicades i Ă©s posteriorment avaluada sota escenaris de trĂ fic de xarxa dinĂ mics. A continuaciĂł, estenem VENTURE considerant la dinamicitat dels fluxos de trĂ fic de xarxes de metro, el qual representa un escenari rellevant de dinamicitat de trĂ fic. Aquesta extensiĂł involucra millores per coordinar els operadors de metro i core amb lâobjectiu dâaconseguir una rĂ pida adaptaciĂł de models de trĂ fic OD. Finalment, proposem dues arquitectures de xarxa necessĂ ries per aplicar els mecanismes anteriors en entorns experimentals, emprant protocols estat-de-lâart com sĂłn OpenFlow i IPFIX. La metodologia emprada per avaluar el treball anterior consisteix en una primera avaluaciĂł numĂšrica fent servir un simulador de xarxes Ăntegrament dissenyat i desenvolupat per a aquesta tesi. DesprĂ©s dâaquesta validaciĂł basada en simulaciĂł, la factibilitat experimental de les arquitectures de xarxa proposades Ă©s avaluada en un entorn de proves distribuĂŻt.Postprint (published version
Towards cognitive in-operation network planning
Next-generation internet services such as live TV and video on demand require high bandwidth and ultra-low latency. The ever-increasing volume, dynamicity and stringent requirements of these servicesâ demands are generating new challenges to nowadays telecom networks. To decrease expenses, service-layer content providers are delivering their content near the end users, thus allowing a low latency and tailored content delivery. As a consequence of this, unseen metro and even core traffic dynamicity is arising with changes in the volume and direction of the traffic along the day.
A tremendous effort to efficiently manage networks is currently ongoing towards the realisation of 5G networks. This translates in looking for network architectures supporting dynamic resource allocation, fulfilling strict service requirements and minimising the total cost of ownership (TCO). In this regard, in-operation network planning was recently proven to successfully support various network reconfiguration use cases in prospective scenarios. Nevertheless, additional research to extend in-operation planning capabilities from typical reactive optimization schemes to proactive and predictive schemes based on the analysis of network monitoring data is required.
A hot topic raising increasing attention is cognitive networking, where an elevated knowledge about the network could be obtained as a result of introducing data analytics in the telecom operatorâs infrastructure. By using predictive knowledge about the network traffic, in-operation network planning mechanisms could be enhanced to efficiently adapt the network by means of future traffic prediction, thus achieving cognitive in-operation network planning.
In this thesis, we focus on studying mechanisms to enable cognitive in-operation network planning in core networks. In particular, we focus on dynamically reconfiguring virtual network topologies (VNT) at the MPLS layer, covering a number of detailed objectives. First, we start studying mechanisms to allow network traffic flow modelling, from monitoring and data transformation to the estimation of predictive traffic model based on this data. By means of these traffic models, then we tackle a cognitive approach to periodically adapt the core VNT to current and future traffic, using predicted traffic matrices based on origin-destination (OD) predictive models. This optimization approach, named VENTURE, is efficiently solved using dedicated heuristic algorithms and its feasibility is demonstrated in an experimental in-operation network planning environment. Finally, we extend VENTURE to consider core flows dynamicity as a result of metro flows re-routing, which represents a meaningful dynamic traffic scenario. This extension, which entails enhancements to coordinate metro and core network controllers with the aim of allowing fast adaption of core OD traffic models, is evaluated and validated in terms of traffic models accuracy and experimental feasibility.Els serveis dâinternet de nova generaciĂł tals com la televisiĂł en viu o el vĂdeo sota demanda requereixen dâun gran ample de banda i dâultra-baixa latĂšncia. Lâincrement continu del volum, dinamicitat i requeriments dâaquests serveis estĂ generant nous reptes pels teleoperadors de xarxa. Per reduir costs, els proveĂŻdors de contingut estan disposant aquests mĂ©s a prop dels usuaris finals, aconseguint aixĂ una entrega de contingut feta a mida. ConseqĂŒentment, estem presenciant una dinamicitat mai vista en el trĂ fic de xarxes de metro amb canvis en la direcciĂł i el volum del trĂ fic al llarg del dia. Actualment, sâestĂ duent a terme un gran esforç cap a la realitzaciĂł de xarxes 5G. Aquest esforç es tradueix en cercar noves arquitectures de xarxa que suportin lâassignaciĂł dinĂ mica de recursos, complint requeriments de servei estrictes i minimitzant el cost total de la propietat. En aquest sentit, recentment sâha demostrat com lâaplicaciĂł de âin-operation network planningâ permet exitosament suportar diversos casos dâĂșs de reconfiguraciĂł de xarxa en escenaris prospectius. No obstant, Ă©s necessari dur a terme mĂ©s recerca per tal dâestendre âin-operation network planningâ des dâun esquema reactiu dâoptimitzaciĂł cap a un nou esquema proactiu basat en lâanalĂtica de dades provinents del monitoritzat de la xarxa. El concepte de xarxes cognitives es tambĂ© troba al centre dâatenciĂł, on un elevat coneixement de la xarxa sâobtindria com a resultat dâintroduir analĂtica de dades en la infraestructura del teleoperador. Mitjançant un coneixement predictiu sobre el trĂ fic de xarxa, els mecanismes de in-operation network planning es podrien millorar per adaptar la xarxa eficientment basant-se en predicciĂł de trĂ fic, assolint aixĂ el que anomenem com a âcognitive in-operation network Planningâ. En aquesta tesi ens centrem en lâestudi de mecanismes que permetin establir âel cognitive in-operation network Planningâ en xarxes de core. En particular, ens centrem en reconfigurar dinĂ micament topologies de xarxa virtual (VNT) a la capa MPLS, cobrint una sĂšrie dâobjectius detallats. Primer comencem estudiant mecanismes pel modelat de fluxos de trĂ fic de xarxa, des del seu monitoritzat i transformaciĂł fins a lâestimaciĂł de models predictius de trĂ fic. Posteriorment, i mitjançant aquests models predictius, tractem un esquema cognitiu per adaptar periĂČdicament la VNT utilitzant matrius de trĂ fic basades en predicciĂł de parells origen-destĂ (OD). Aquesta optimitzaciĂł, anomenada VENTURE, Ă©s resolta eficientment fent servir heurĂstiques dedicades i Ă©s posteriorment avaluada sota escenaris de trĂ fic de xarxa dinĂ mics. A continuaciĂł, estenem VENTURE considerant la dinamicitat dels fluxos de trĂ fic de xarxes de metro, el qual representa un escenari rellevant de dinamicitat de trĂ fic. Aquesta extensiĂł involucra millores per coordinar els operadors de metro i core amb lâobjectiu dâaconseguir una rĂ pida adaptaciĂł de models de trĂ fic OD. Finalment, proposem dues arquitectures de xarxa necessĂ ries per aplicar els mecanismes anteriors en entorns experimentals, emprant protocols estat-de-lâart com sĂłn OpenFlow i IPFIX. La metodologia emprada per avaluar el treball anterior consisteix en una primera avaluaciĂł numĂšrica fent servir un simulador de xarxes Ăntegrament dissenyat i desenvolupat per a aquesta tesi. DesprĂ©s dâaquesta validaciĂł basada en simulaciĂł, la factibilitat experimental de les arquitectures de xarxa proposades Ă©s avaluada en un entorn de proves distribuĂŻt
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
Structure-aware image denoising, super-resolution, and enhancement methods
Denoising, super-resolution and structure enhancement are classical image processing applications. The motive behind their existence is to aid our visual analysis of raw digital images. Despite tremendous progress in these fields, certain difficult problems are still open to research. For example, denoising and super-resolution techniques which possess all the following properties, are very scarce: They must preserve critical structures like corners, should be robust to the type of noise distribution, avoid undesirable artefacts, and also be fast. The area of structure enhancement also has an unresolved issue: Very little efforts have been put into designing models that can tackle anisotropic deformations in the image acquisition process. In this thesis, we design novel methods in the form of partial differential equations, patch-based approaches and variational models to overcome the aforementioned obstacles. In most cases, our methods outperform the existing approaches in both quality and speed, despite being applicable to a broader range of practical situations.Entrauschen, Superresolution und Strukturverbesserung sind klassische Anwendungen der Bildverarbeitung. Ihre Existenz bedingt sich in dem Bestreben, die visuelle Begutachtung digitaler Bildrohdaten zu unterstĂŒtzen. Trotz erheblicher Fortschritte in diesen Feldern bedĂŒrfen bestimmte schwierige Probleme noch weiterer Forschung. So sind beispielsweise Entrauschungsund Superresolutionsverfahren, welche alle der folgenden Eingenschaften besitzen, sehr selten: die Erhaltung wichtiger Strukturen wie Ecken, Robustheit bezĂŒglich der Rauschverteilung, Vermeidung unerwĂŒnschter Artefakte und niedrige Laufzeit. Auch im Gebiet der Strukturverbesserung liegt ein ungelöstes Problem vor: Bisher wurde nur sehr wenig Forschungsaufwand in die Entwicklung von Modellen investieret, welche anisotrope Deformationen in bildgebenden Verfahren bewĂ€ltigen können. In dieser Arbeit entwerfen wir neue Methoden in Form von partiellen Differentialgleichungen, patch-basierten AnsĂ€tzen und Variationsmodellen um die oben erwĂ€hnten Hindernisse zu ĂŒberwinden. In den meisten FĂ€llen ĂŒbertreffen unsere Methoden nicht nur qualitativ die bisher verwendeten AnsĂ€tze, sondern lösen die gestellten Aufgaben auch schneller. Zudem decken wir mit unseren Modellen einen breiteren Bereich praktischer Fragestellungen ab
Soft computing for tool life prediction a manufacturing application of neural - fuzzy systems
Tooling technology is recognised as an element of vital importance within the manufacturing industry. Critical tooling decisions related to tool selection, tool life management, optimal determination of cutting conditions and on-line machining process monitoring and control are based on the existence of reliable detailed process models. Among the decisive factors of process planning and control activities, tool wear and tool life considerations hold a dominant role. Yet, both off-line tool life prediction, as well as real tune tool wear identification and prediction are still issues open to research. The main reason lies with the large number of factors, influencing tool wear, some of them being of stochastic nature. The inherent variability of workpiece materials, cutting tools and machine characteristics, further increases the uncertainty about the machining optimisation problem. In machining practice, tool life prediction is based on the availability of data provided from tool manufacturers, machining data handbooks or from the shop floor. This thesis recognises the need for a data-driven, flexible and yet simple approach in predicting tool life. Model building from sample data depends on the availability of a sufficiently rich cutting data set. Flexibility requires a tool-life model with high adaptation capacity. Simplicity calls for a solution with low complexity and easily interpretable by the user. A neural-fuzzy systems approach is adopted, which meets these targets and predicts tool life for a wide range of turning operations. A literature review has been carried out, covering areas such as tool wear and tool life, neural networks, frizzy sets theory and neural-fuzzy systems integration. Various sources of tool life data have been examined. It is concluded that a combined use of simulated data from existing tool life models and real life data is the best policy to follow. The neurofuzzy tool life model developed is constructed by employing neural network-like learning algorithms. The trained model stores the learned knowledge in the form of frizzy IF-THEN rules on its structure, thus featuring desired transparency. Low model complexity is ensured by employing an algorithm which constructs a rule base of reduced size from the available data. In addition, the flexibility of the developed model is demonstrated by the ease, speed and efficiency of its adaptation on the basis of new tool life data. The development of the neurofuzzy tool life model is based on the Fuzzy Logic Toolbox (vl.0) of MATLAB (v4.2cl), a dedicated tool which facilitates design and evaluation of fuzzy logic systems. Extensive results are presented, which demonstrate the neurofuzzy model predictive performance. The model can be directly employed within a process planning system, facilitating the optimisation of turning operations. Recommendations aremade for further enhancements towards this direction
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
Innovation in manufacturing through digital technologies and applications: Thoughts and Reflections on Industry 4.0
The rapid pace of developments in digital technologies offers many opportunities to increase the efficiency, flexibility and sophistication of manufacturing processes; including the potential for easier customisation, lower volumes and rapid changeover of products within the same manufacturing cell or line. A number of initiatives on this theme have been proposed around the world to support national industries under names such as Industry 4.0 (Industrie 4.0 in Germany, Made-in-China in China and Made Smarter in the UK).
This book presents an overview of the state of art and upcoming developments in digital technologies pertaining to manufacturing. The starting point is an introduction on Industry 4.0 and its potential for enhancing the manufacturing process. Later on moving to the design of smart (that is digitally driven) business processes which are going to rely on sensing of all relevant parameters, gathering, storing and processing the data from these sensors, using computing power and intelligence at the most appropriate points in the digital workflow including application of edge computing and parallel processing.
A key component of this workflow is the application of Artificial Intelligence and particularly techniques in Machine Learning to derive actionable information from this data; be it real-time automated responses such as actuating transducers or informing human operators to follow specified standard operating procedures or providing management data for operational and strategic planning. Further consideration also needs to be given to the properties and behaviours of particular machines that are controlled and materials that are transformed during the manufacturing process and this is sometimes referred to as Operational Technology (OT) as opposed to IT. The digital capture of these properties and behaviours can then be used to define so-called Cyber Physical Systems.
Given the power of these digital technologies it is of paramount importance that they operate safely and are not vulnerable to malicious interference. Industry 4.0 brings unprecedented cybersecurity challenges to manufacturing and the overall industrial sector and the case is made here that new codes of practice are needed for the combined Information Technology and Operational Technology worlds, but with a framework that should be native to Industry 4.0. Current computing technologies are also able to go in other directions than supporting the digital âsense to actionâ process described above. One of these is to use digital technologies to enhance the ability of the human operators who are still essential within the manufacturing process. One such technology, that has recently become accessible for widespread adoption, is Augmented Reality, providing operators with real-time additional information in situ with the machines that they interact with in their workspace in a hands-free mode.
Finally, two linked chapters discuss the specific application of digital technologies to High Pressure Die Casting (HDPC) of Magnesium components. Optimizing the HPDC process is a key task for increasing productivity and reducing defective parts and the first chapter provides an overview of the HPDC process with attention to the most common defects and their sources. It does this by first looking at real-time process control mechanisms, understanding the various process variables and assessing their impact on the end product quality. This understanding drives the choice of sensing methods and the associated smart digital workflow to allow real-time control and mitigation of variation in the identified variables. Also, data from this workflow can be captured and used for the design of optimised dies and associated processes
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