5 research outputs found
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
Core VNT adaptation based on the aggregated metro-flow traffic model prediction
Aggregation of metro-flow traffic models is proposed to obtain valid core traffic predictive models for core VNT reconfiguration when metro and core networks are independently controlled. Exhaustive simulation results reveal large optical transponders usage savings.Peer Reviewe
Core VNT adaptation based on the aggregated metro-flow traffic model prediction
Aggregation of metro-flow traffic models is proposed to obtain valid core traffic predictive models for core VNT reconfiguration when metro and core networks are independently controlled. Exhaustive simulation results reveal large optical transponders usage savings.Peer Reviewe
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 generaciĂł tals com la televisiĂł en viu o el vĂdeo 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 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. ConseqĂĽentment, 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, s’estĂ 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 l’assignaciĂł dinĂ mica de recursos, complint requeriments de servei estrictes i minimitzant el cost total de la propietat. En aquest sentit, recentment s’ha demostrat com l’aplicaciĂł de “in-operation network planning” permet exitosament suportar diversos casos d’ús de reconfiguraciĂł de xarxa en escenaris prospectius. No obstant, Ă©s necessari dur a terme mĂ©s recerca per tal d’estendre “in-operation network planning” des d’un esquema reactiu d’optimitzaciĂł cap a un nou esquema proactiu basat en l’analĂtica de dades provinents del monitoritzat de la xarxa. El concepte de xarxes cognitives es tambĂ© troba al centre d’atenciĂł, on un elevat coneixement de la xarxa s’obtindria com a resultat d’introduir 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 “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 dinĂ micament topologies de xarxa virtual (VNT) a la capa MPLS, cobrint una sèrie d’objectius detallats. Primer comencem estudiant mecanismes pel modelat de fluxos de trĂ fic de xarxa, des del seu monitoritzat i transformaciĂł fins a l’estimaciĂł de models predictius de trĂ fic. Posteriorment, i mitjançant aquests models predictius, tractem un esquema cognitiu per adaptar periòdicament 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 l’objectiu d’aconseguir 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-l’art com sĂłn OpenFlow i IPFIX. La metodologia emprada per avaluar el treball anterior consisteix en una primera avaluaciĂł numèrica fent servir un simulador de xarxes Ăntegrament dissenyat i desenvolupat per a aquesta tesi. DesprĂ©s d’aquesta 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 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 generaciĂł tals com la televisiĂł en viu o el vĂdeo 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 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. ConseqĂĽentment, 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, s’estĂ 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 l’assignaciĂł dinĂ mica de recursos, complint requeriments de servei estrictes i minimitzant el cost total de la propietat. En aquest sentit, recentment s’ha demostrat com l’aplicaciĂł de “in-operation network planning” permet exitosament suportar diversos casos d’ús de reconfiguraciĂł de xarxa en escenaris prospectius. No obstant, Ă©s necessari dur a terme mĂ©s recerca per tal d’estendre “in-operation network planning” des d’un esquema reactiu d’optimitzaciĂł cap a un nou esquema proactiu basat en l’analĂtica de dades provinents del monitoritzat de la xarxa. El concepte de xarxes cognitives es tambĂ© troba al centre d’atenciĂł, on un elevat coneixement de la xarxa s’obtindria com a resultat d’introduir 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 “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 dinĂ micament topologies de xarxa virtual (VNT) a la capa MPLS, cobrint una sèrie d’objectius detallats. Primer comencem estudiant mecanismes pel modelat de fluxos de trĂ fic de xarxa, des del seu monitoritzat i transformaciĂł fins a l’estimaciĂł de models predictius de trĂ fic. Posteriorment, i mitjançant aquests models predictius, tractem un esquema cognitiu per adaptar periòdicament 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 l’objectiu d’aconseguir 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-l’art com sĂłn OpenFlow i IPFIX. La metodologia emprada per avaluar el treball anterior consisteix en una primera avaluaciĂł numèrica fent servir un simulador de xarxes Ăntegrament dissenyat i desenvolupat per a aquesta tesi. DesprĂ©s d’aquesta validaciĂł basada en simulaciĂł, la factibilitat experimental de les arquitectures de xarxa proposades Ă©s avaluada en un entorn de proves distribuĂŻt