3,038 research outputs found
Cost Function based Event Triggered Model Predictive Controllers - Application to Big Data Cloud Services
International audienceHigh rate cluster reconfigurations is a costly issue in Big Data Cloud services. Current control solutions manage to scale the cluster according to the workload, however they do not try to minimize the number of system reconfigurations. Event-based control is known to reduce the number of control updates typically by waiting for the system states to degrade below a given threshold before reacting. However, computer science systems often have exogenous inputs (such as clients connections) with delayed impacts that can enable to anticipate states degradation. In this paper, a novel event-triggered approach is proposed. This triggering mechanism relies on a Model Predictive Controller and is defined upon the value of the optimal cost function instead of the state or output error. This controller reduces the number of control changes, in the normal operation mode, through constraints in the MPC formulation but also assures a very reactive behavior to changes of exogenous inputs. This novel control approach is evaluated using a model validated on a real Big Data system. The controller efficiently scales the cluster according to specifications, meanwhile reducing its reconfigurations
Intelligent Management and Efficient Operation of Big Data
This chapter details how Big Data can be used and implemented in networking
and computing infrastructures. Specifically, it addresses three main aspects:
the timely extraction of relevant knowledge from heterogeneous, and very often
unstructured large data sources, the enhancement on the performance of
processing and networking (cloud) infrastructures that are the most important
foundational pillars of Big Data applications or services, and novel ways to
efficiently manage network infrastructures with high-level composed policies
for supporting the transmission of large amounts of data with distinct
requisites (video vs. non-video). A case study involving an intelligent
management solution to route data traffic with diverse requirements in a wide
area Internet Exchange Point is presented, discussed in the context of Big
Data, and evaluated.Comment: In book Handbook of Research on Trends and Future Directions in Big
Data and Web Intelligence, IGI Global, 201
A survey of machine learning techniques applied to self organizing cellular networks
In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future
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
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
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
CONTROL-CORE: A Framework for Simulation and Design of Closed-Loop Peripheral Neuromodulation Control Systems
Closed-loop Vagus Nerve Stimulation (VNS) based on physiological feedback signals is a promising approach to regulate organ functions and develop therapeutic devices. Designing closed-loop neurostimulation systems requires simulation environments and computing infrastructures that support i) modeling the physiological responses of organs under neuromodulation, also known as physiological models, and ii) the interaction between the physiological models and the neuromodulation control algorithms. However, existing simulation platforms do not support closed-loop VNS control systems modeling without extensive rewriting of computer code and manual deployment and configuration of programs. The CONTROL-CORE project aims to develop a flexible software platform for designing and implementing closed-loop VNS systems. This paper proposes the software architecture and the elements of the CONTROL-CORE platform that allow the interaction between a controller and a physiological model in feedback. CONTROL-CORE facilitates modular simulation and deployment of closed-loop peripheral neuromodulation control systems, spanning multiple organizations securely and concurrently. CONTROL-CORE allows simulations to run on different operating systems, be developed in various programming languages (such as Matlab, Python, C++, and Verilog), and be run locally, in containers, and in a distributed fashion. The CONTROL-CORE platform allows users to create tools and testbenches to facilitate sophisticated simulation experiments. We tested the CONTROL-CORE platform in the context of closed-loop control of cardiac physiological models, including pulsatile and nonpulsatile rat models. These were tested using various controllers such as Model Predictive Control and Long-Short-Term Memory based controllers. Our wide range of use cases and evaluations show the performance, flexibility, and usability of the CONTROL-CORE platform
Digital Twins for an Industrial Internet of Things Platform
Com o avanço da IndĂşstria 4.0 e do surgimento de novas tecnologias de informação e comunicação tais como o IIoT (Industrial Internet of Things), o sector industrial tem procurado cada vez mais, evoluir as suas linhas de produção de modo a atingir a maior eficiĂŞncia de produção possĂvel. Aliado ao conceito IIoT, o termo Digital Twin e CPS (Cyber Physical System) começam a ganhar elevada relevância em vários sectores, nomeadamente no sector industrial. Apesar de serem conceitos que se podem confundir, o conceito de DT e CPS sĂŁo aplicados em diferentes domĂnios. O conceito de CPS relaciona-se com a conexĂŁo de duas direções que Ă© possĂvel estabelecer entre o meio fĂsico e o meio digital. Ele utiliza a rede IoT para capturar a informação do meio fĂsico atravĂ©s de sensores e controladores e com esta informação, Ă© possĂvel no meio digital tornar a rĂ©plica mais inteligente a fim de conseguir reproduzir o comportamento da entidade fĂsica. O conceito de DT Ă© um pouco menos abstrato, comparado com o conceito de CPS, e Ă© o DT que implementa o CPS. O DT utiliza as funcionalidades do CPS para realizar modelos de simulação das entidades fĂsicas de forma a conseguir espelhar a geometria e o comportamento da mesma no meio digital. Com estes modelos digitais Ă© possĂvel realizar uma monitorização e controlo em tempo real das entidades fĂsicas.
De forma a aplicar estes conceitos, esta dissertação tem como principal objetivo a implementação de um DT capaz de replicar o comportamento de uma determinada entidade fĂsica no meio digital. Dessa forma, atravĂ©s da aplicação web Jurassic Park como plataforma IoT, a dissertação pretende adicionar um conjunto de novas funcionalidades de controlo e monitorização Ă sua interface-gráfica, de modo a que o utilizador consiga nĂŁo sĂł observar em tempo real a variação de valores de variáveis previamente subscritas mas tambĂ©m controlar alguns eventos que tambĂ©m foram selecionados pelo utilizador previamente
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