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

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 services’ 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 operator’s 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 d’internet de nova generació tals com la televisió en viu o el vídeo sota demanda requereixen d’un gran ample de banda i d’ultra-baixa latència. L’increment continu del volum, dinamicitat i requeriments d’aquests serveis està generant nous reptes pels teleoperadors de xarxa. Per reduir costs, els proveïdors de contingut estan disposant aquests més a prop dels usuaris finals, aconseguint així una entrega de contingut feta a mida. Conseqüentment, estem presenciant una dinamicitat mai vista en el tràfic de xarxes de metro amb canvis en la direcció i el volum del tràfic al llarg del dia. Actualment, s’està duent a terme un gran esforç cap a la realització de xarxes 5G. Aquest esforç es tradueix en cercar noves arquitectures de xarxa que suportin l’assignació dinàmica de recursos, complint requeriments de servei estrictes i minimitzant el cost total de la propietat. En aquest sentit, recentment s’ha demostrat com l’aplicació de “in-operation network planning” permet exitosament suportar diversos casos d’ús de reconfiguració de xarxa en escenaris prospectius. No obstant, és necessari dur a terme més recerca per tal d’estendre “in-operation network planning” des d’un esquema reactiu d’optimització cap a un nou esquema proactiu basat en l’analítica de dades provinents del monitoritzat de la xarxa. El concepte de xarxes cognitives es també troba al centre d’atenció, on un elevat coneixement de la xarxa s’obtindria com a resultat d’introduir analítica de dades en la infraestructura del teleoperador. Mitjançant un coneixement predictiu sobre el tràfic de xarxa, els mecanismes de in-operation network planning es podrien millorar per adaptar la xarxa eficientment basant-se en predicció de tràfic, assolint així el que anomenem com a “cognitive in-operation network Planning”. En aquesta tesi ens centrem en l’estudi de mecanismes que permetin establir “el cognitive in-operation network Planning” en xarxes de core. En particular, ens centrem en reconfigurar dinàmicament topologies de xarxa virtual (VNT) a la capa MPLS, cobrint una sèrie d’objectius detallats. Primer comencem estudiant mecanismes pel modelat de fluxos de tràfic de xarxa, des del seu monitoritzat i transformació fins a l’estimació de models predictius de tràfic. Posteriorment, i mitjançant aquests models predictius, tractem un esquema cognitiu per adaptar periòdicament la VNT utilitzant matrius de tràfic basades en predicció de parells origen-destí (OD). Aquesta optimització, anomenada VENTURE, és resolta eficientment fent servir heurístiques dedicades i és posteriorment avaluada sota escenaris de tràfic de xarxa dinàmics. A continuació, estenem VENTURE considerant la dinamicitat dels fluxos de tràfic de xarxes de metro, el qual representa un escenari rellevant de dinamicitat de tràfic. Aquesta extensió involucra millores per coordinar els operadors de metro i core amb l’objectiu d’aconseguir una ràpida adaptació de models de tràfic OD. Finalment, proposem dues arquitectures de xarxa necessàries per aplicar els mecanismes anteriors en entorns experimentals, emprant protocols estat-de-l’art com són OpenFlow i IPFIX. La metodologia emprada per avaluar el treball anterior consisteix en una primera avaluació numèrica fent servir un simulador de xarxes íntegrament dissenyat i desenvolupat per a aquesta tesi. Després d’aquesta validació basada en simulació, la factibilitat experimental de les arquitectures de xarxa proposades és avaluada en un entorn de proves distribuït.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 services’ 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 operator’s 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 d’internet de nova generació tals com la televisió en viu o el vídeo sota demanda requereixen d’un gran ample de banda i d’ultra-baixa latència. L’increment continu del volum, dinamicitat i requeriments d’aquests serveis està generant nous reptes pels teleoperadors de xarxa. Per reduir costs, els proveïdors de contingut estan disposant aquests més a prop dels usuaris finals, aconseguint així una entrega de contingut feta a mida. Conseqüentment, estem presenciant una dinamicitat mai vista en el tràfic de xarxes de metro amb canvis en la direcció i el volum del tràfic al llarg del dia. Actualment, s’està duent a terme un gran esforç cap a la realització de xarxes 5G. Aquest esforç es tradueix en cercar noves arquitectures de xarxa que suportin l’assignació dinàmica de recursos, complint requeriments de servei estrictes i minimitzant el cost total de la propietat. En aquest sentit, recentment s’ha demostrat com l’aplicació de “in-operation network planning” permet exitosament suportar diversos casos d’ús de reconfiguració de xarxa en escenaris prospectius. No obstant, és necessari dur a terme més recerca per tal d’estendre “in-operation network planning” des d’un esquema reactiu d’optimització cap a un nou esquema proactiu basat en l’analítica de dades provinents del monitoritzat de la xarxa. El concepte de xarxes cognitives es també troba al centre d’atenció, on un elevat coneixement de la xarxa s’obtindria com a resultat d’introduir analítica de dades en la infraestructura del teleoperador. Mitjançant un coneixement predictiu sobre el tràfic de xarxa, els mecanismes de in-operation network planning es podrien millorar per adaptar la xarxa eficientment basant-se en predicció de tràfic, assolint així el que anomenem com a “cognitive in-operation network Planning”. En aquesta tesi ens centrem en l’estudi de mecanismes que permetin establir “el cognitive in-operation network Planning” en xarxes de core. En particular, ens centrem en reconfigurar dinàmicament topologies de xarxa virtual (VNT) a la capa MPLS, cobrint una sèrie d’objectius detallats. Primer comencem estudiant mecanismes pel modelat de fluxos de tràfic de xarxa, des del seu monitoritzat i transformació fins a l’estimació de models predictius de tràfic. Posteriorment, i mitjançant aquests models predictius, tractem un esquema cognitiu per adaptar periòdicament la VNT utilitzant matrius de tràfic basades en predicció de parells origen-destí (OD). Aquesta optimització, anomenada VENTURE, és resolta eficientment fent servir heurístiques dedicades i és posteriorment avaluada sota escenaris de tràfic de xarxa dinàmics. A continuació, estenem VENTURE considerant la dinamicitat dels fluxos de tràfic de xarxes de metro, el qual representa un escenari rellevant de dinamicitat de tràfic. Aquesta extensió involucra millores per coordinar els operadors de metro i core amb l’objectiu d’aconseguir una ràpida adaptació de models de tràfic OD. Finalment, proposem dues arquitectures de xarxa necessàries per aplicar els mecanismes anteriors en entorns experimentals, emprant protocols estat-de-l’art com són OpenFlow i IPFIX. La metodologia emprada per avaluar el treball anterior consisteix en una primera avaluació numèrica fent servir un simulador de xarxes íntegrament dissenyat i desenvolupat per a aquesta tesi. Després d’aquesta validació basada en simulació, la factibilitat experimental de les arquitectures de xarxa proposades és avaluada en un entorn de proves distribuït

In-operation planning in flexgrid optical core networks

New generation applications, such as cloud computing or video distribution, can run in a telecom cloud infrastructure where the datacenters (DCs) of telecom operators are integrated in their networks thus, increasing connections' dynamicity and resulting in time-varying traffic capacities, which might also entail changes in the traffic direction along the day. As a result, a flexible optical technology able to dynamically set-up variable-capacity connections, such as flexgrid, is needed. Nonetheless, network dynamicity might entail network performance degradation thus, requiring re-optimizing the network while it is in operation. This thesis is devoted to devise new algorithms to solve in-operation network planning problems aiming at enhancing the performance of optical networks and at studying their feasibility in experimental environments. In-operation network planning requires from an architecture enabling the deployment of algorithms that must be solved in stringent times. That architecture can be based on a Path Computation Element (PCE) or a Software Defined Networks controller. In this thesis, we assume the former split in a front-end PCE, in charge of provisioning paths and handling network events, and a specialized planning tool in the form of a back-end PCE responsible for solving in-operation planning problems. After the architecture to support in-operation planning is assessed, we focus on studying the following applications: 1) Spectrum fragmentation is one of the most important problems in optical networks. To alleviate it to some extent without traffic disruption, we propose a hitless spectrum defragmentation strategy. 2) Each connection affected by a failure can be recovered using multiple paths to increase traffic restorability at the cost of poor resource utilization. We propose re-optimizing the network after repairing the failure to aggregate and reroute those connections to release spectral resources. 3) We study two approaches to provide multicast services: establishing a point-to-multipoint connections at the optical layer and using multi-purpose virtual network topologies (VNT) to serve both unicast and multicast connectivity requests. 4) The telecom cloud infrastructure, enables placing contents closer to the users. Based on it, we propose a hierarchical content distribution architecture where VNTs permanently interconnect core DCs and metro DCs periodically synchronize contents to the core DCs. 5) When the capacity of the optical backbone network becomes exhausted, we propose using a planning tool with access to inventory and operation databases to periodically decide the equipment and connectivity to be installed at the minimum cost reducing capacity overprovisioning. 6) In multi-domain multi-operator scenarios, a broker on top of the optical domains can provision multi-domain connections. We propose performing intra-domain spectrum defragmentation when no contiguous spectrum can be found for a new connection request. 7) Packet nodes belonging to a VNT can collect and send incoming traffic monitoring data to a big data repository. We propose using the collected data to predict next period traffic and to adapt the VNT to future conditions. The methodology followed in this thesis consists in proposing a problem statement and/or a mathematical formulation for the problems identified and then, devising algorithms for solving them. Those algorithms are simulated and then, they are experimentally assessed in real test-beds. This thesis demonstrates the feasibility of performing in-operation planning in optical networks, shows that it enhances the performance of the network and validates the feasibility of its deployment in real networks. It shall be mentioned that part of the work reported in this thesis has been done within the framework of several research projects, namely IDEALIST (FP7-ICT-2011-8) and GEANT (238875) funded by the EC and SYNERGY (TEC2014-59995-R) funded by the MINECO.Les aplicacions de nova generació, com ara el cloud computing o la distribució de vídeo, es poden executar a infraestructures de telecom cloud (TCI) on operadors integren els seus datacenters (DC) a les seves xarxes. Aquestes aplicacions fan que incrementi tant la dinamicitat de les connexions, com la variabilitat de les seves capacitats en el temps, arribant a canviar de direcció al llarg del dia. Llavors, cal disposar de tecnologies òptiques flexibles, tals com flexgrid, que suportin aquesta dinamicitat a les connexions. Aquesta dinamicitat pot degradar el rendiment de la xarxa, obligant a re-optimitzar-la mentre és en operació. Aquesta tesis està dedicada a idear nous algorismes per a resoldre problemes de planificació sobre xarxes en operació (in-operation network planning) per millorar el rendiment de les xarxes òptiques i a estudiar la seva factibilitat en entorns experimentals. Aquests problemes requereixen d’una arquitectura que permeti desplegar algorismes que donin solucions en temps restrictius. L’arquitectura pot estar basada en un Element de Computació de Rutes (PCE) o en un controlador de Xarxes Definides per Software. En aquesta tesis, assumim un PCE principal encarregat d’aprovisionar rutes i gestionar esdeveniments de la xarxa, i una eina de planificació especialitzada en forma de PCE de suport per resoldre problemes d’in-operation planning. Un cop validada l’arquitectura que dona suport a in-operation planning, estudiarem les següents aplicacions: 1) La fragmentació d’espectre és un dels principals problemes a les xarxes òptiques. Proposem reduir-la en certa mesura, fent servir una estratègia que no afecta al tràfic durant la desfragmentació. 2) Cada connexió afectada per una fallada pot ser recuperada fent servir múltiples rutes incrementant la restaurabilitat de la xarxa, tot i empitjorar-ne la utilització de recursos. Proposem re-optimitzar la xarxa després de reparar una fallada per agregar i re-enrutar aquestes connexions tractant d’alliberar recursos espectrals. 3) Estudiem dues solucions per aprovisionar serveis multicast: establir connexions punt-a-multipunt sobre la xarxa òptica i utilitzar Virtual Network Topologies (VNT) multi-propòsit per a servir peticions de connectivitat tant unicast com multicast. 4) La TCI permet mantenir els continguts a prop dels usuaris. Proposem una arquitectura jeràrquica de distribució de continguts basada en la TCI, on els DC principals s’interconnecten per mitjà de VNTs permanents i els DCs metropolitans periòdicament sincronitzen continguts amb els principals. 5) Quan la capacitat de la xarxa òptica s’exhaureix, proposem utilitzar una eina de planificació amb accés a bases de dades d’inventari i operacionals per decidir periòdicament l’equipament i connectivitats a instal·lar al mínim cost i reduir el sobre-aprovisionament de capacitat. 6) En entorns multi-domini multi-operador, un broker per sobre dels dominis òptics pot aprovisionar connexions multi-domini. Proposem aplicar desfragmentació d’espectre intra-domini quan no es pot trobar espectre contigu per a noves peticions de connexió. 7) Els nodes d’una VNT poden recollir i enviar informació de monitorització de tràfic entrant a un repositori de big data. Proposem utilitzar aquesta informació per adaptar la VNT per a futures condicions. La metodologia que hem seguit en aquesta tesis consisteix en formalitzar matemàticament els problemes un cop aquests son identificats i, després, idear algorismes per a resoldre’ls. Aquests algorismes son simulats i finalment validats experimentalment en entorns reals. Aquesta tesis demostra la factibilitat d’implementar mecanismes d’in-operation planning en xarxes òptiques, mostra els beneficis que aquests aporten i valida la seva aplicabilitat en xarxes reals. Part del treball presentat en aquesta tesis ha estat dut a terme en el marc dels projectes de recerca IDEALIST (FP7-ICT-2011-8) i GEANT (238875), finançats per la CE, i SYNERGY (TEC2014-59995-R), finançat per el MINECO.Postprint (published version

Software-Defined Cloud Computing: Architectural Elements and Open Challenges

The variety of existing cloud services creates a challenge for service providers to enforce reasonable Software Level Agreements (SLA) stating the Quality of Service (QoS) and penalties in case QoS is not achieved. To avoid such penalties at the same time that the infrastructure operates with minimum energy and resource wastage, constant monitoring and adaptation of the infrastructure is needed. We refer to Software-Defined Cloud Computing, or simply Software-Defined Clouds (SDC), as an approach for automating the process of optimal cloud configuration by extending virtualization concept to all resources in a data center. An SDC enables easy reconfiguration and adaptation of physical resources in a cloud infrastructure, to better accommodate the demand on QoS through a software that can describe and manage various aspects comprising the cloud environment. In this paper, we present an architecture for SDCs on data centers with emphasis on mobile cloud applications. We present an evaluation, showcasing the potential of SDC in two use cases-QoS-aware bandwidth allocation and bandwidth-aware, energy-efficient VM placement-and discuss the research challenges and opportunities in this emerging area.Comment: Keynote Paper, 3rd International Conference on Advances in Computing, Communications and Informatics (ICACCI 2014), September 24-27, 2014, Delhi, Indi

A control and management architecture supporting autonomic NFV services

The proposed control, orchestration and management (COM) architecture is presented from a high-level point of view; it enables the dynamic provisioning of services such as network data connectivity or generic network slicing instances based on virtual network functions (VNF). The COM is based on Software Defined Networking (SDN) principles and is hierarchical, with a dedicated controller per technology domain. Along with the SDN control plane for the provisioning of connectivity, an ETSI NFV management and orchestration system is responsible for the instantiation of Network Services, understood in this context as interconnected VNFs. A key, novel component of the COM architecture is the monitoring and data analytics (MDA) system, able to collect monitoring data from the network, datacenters and applications which outputs can be used to proactively reconfigure resources thus adapting to future conditions, like load or degradations. To illustrate the COM architecture, a use case of a Content Delivery Network service taking advantage of the MDA ability to collect and deliver monitoring data is experimentally demonstrated.Peer ReviewedPostprint (author's final draft

Dynamic core VNT adaptability based on predictive metro-flow traffic models

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.MPLS-over-optical virtual network topologies (VNTs) can be adapted to near-future traffic matrices based on predictive models that are estimated by applying data analytics on monitored origin-destination (OD) traffic. However, the deployment of independent SDN controllers for core and metro segments can bring large inefficiencies to this core network reconfiguration based on traffic prediction when traffic flows from metro areas are rerouted to different ingress nodes in the core. In such cases, OD traffic patterns in the core might severely change, thus affecting the quality of the predictive OD models. New traffic model re-estimation usually takes a long time, during which no predictive capabilities are available for the network operator. To alleviate this problem, we propose to extend data analytics to metro networks to obtain predictive models for the metro flows; by knowing how these flows are aggregated into OD pairs in the core, we can also aggregate their predictive models, thus accurately predicting OD traffic and therefore enabling core VNT reconfiguration. To obtain quality metro-flow models, we propose an estimation algorithmthat processes monitored data and returns a predictive model. In addition, a flow controller is proposed for the control architecture to allow metro and core controllers to exchange metro-flow model information. The proposed model aggregation is evaluated through exhaustive simulation, and eventually experimentally assessed together with the flow controller in a testbed connecting premises in CNIT (Pisa, Italy) and UPC (Barcelona, Spain).Peer ReviewedPostprint (author's final draft

Load Forecasting Based Distribution System Network Reconfiguration-A Distributed Data-Driven Approach

In this paper, a short-term load forecasting approach based network reconfiguration is proposed in a parallel manner. Specifically, a support vector regression (SVR) based short-term load forecasting approach is designed to provide an accurate load prediction and benefit the network reconfiguration. Because of the nonconvexity of the three-phase balanced optimal power flow, a second-order cone program (SOCP) based approach is used to relax the optimal power flow problem. Then, the alternating direction method of multipliers (ADMM) is used to compute the optimal power flow in distributed manner. Considering the limited number of the switches and the increasing computation capability, the proposed network reconfiguration is solved in a parallel way. The numerical results demonstrate the feasible and effectiveness of the proposed approach.Comment: 5 pages, preprint for Asilomar Conference on Signals, Systems, and Computers 201