Combination of Advanced Reservation and Resource Periodic Arrangement for RMSA in EON with Deep Reinforcement Learning

The Elastic Optical Networks (EON) provide a solution to the massive demand for connections and extremely high data traffic with the Routing Modulation and Spectrum Assignment (RMSA) as a challenge. In previous RMSA research, there was a high blocking probability because the route to be passed by the K-SP method with a deep neural network approach used the First Fit policy, and the modulation problem was solved with Modulation Format Identification (MFI) or BPSK using Deep Reinforcement Learning. The issue might be apparent in spectrum assignment because of the influence of Advanced Reservation (AR) and Resource Periodic Arrangement (RPA), which is a decision block on a connection request path with both idle and active data traffic. The study’s limitation begins with determining the modulation of m = 1 and m = 4, followed by the placement of frequencies, namely 13 with a combination of standard block frequencies 41224–24412, so that the simulation results are less than 0.0199, due to the combination of block frequency slices with spectrum allocation rule techniques.

Learning for Network Applications and Control

The emergence of new Internet applications and technologies have resulted in an increased complexity as well as a need for lower latency, higher bandwidth, and increased reliability. This ultimately results in an increased complexity of network operation and management. Manual management is not sufficient to meet these new requirements. There is a need for data driven techniques to advance from manual management to autonomous management of network systems. One such technique, Machine Learning (ML), can use data to create models from hidden patterns in the data and make autonomous modifications. This approach has shown significant improvements in other domains (e.g., image recognition and natural language processing). The use of ML, along with advances in programmable control of Software- Defined Networks (SDNs), will alleviate manual network intervention and ultimately aid in autonomous network operations. However, realizing a data driven system that can not only understand what is happening in the network but also operate autonomously requires advances in the networking domain, as well as in ML algorithms. In this thesis, we focus on developing ML-based network architectures and data driven net- working algorithms whose objective is to improve the performance and management of future networks and network applications. We focus on problems spanning across the network protocol stack from the application layer to the physical layer. We design algorithms and architectures that are motivated by measurements and observations in real world or experimental testbeds. In Part I we focus on the challenge of monitoring and estimating user video quality of experience (QoE) of encrypted video traffic for network operators. We develop a system for REal-time QUality of experience metric detection for Encrypted Traffic, Requet. Requet uses a detection algorithm to identify video and audio chunks from the IP headers of encrypted traffic. Features extracted from the chunk statistics are used as input to a random forest ML model to predict QoE metrics. We evaluate Requet on a YouTube dataset we collected, consisting of diverse video assets delivered over various WiFi and LTE network conditions. We then extend Requet, and present a study on YouTube TV live streaming traffic behavior over WiFi and cellular networks covering a 9-month period. We observed pipelined chunk requests, a reduced buffer capacity, and a more stable chunk duration across various video resolutions compared to prior studies of on-demand streaming services. We develop a YouTube TV analysis tool using chunks statistics detected from the extracted data as input to a ML model to infer user QoE metrics. In Part II we consider allocating end-to-end resources in cellular networks. Future cellular networks will utilize SDN and Network Function Virtualization (NFV) to offer increased flexibility for network infrastructure operators to utilize network resources. Combining these technologies with real-time network load prediction will enable efficient use of network resources. Specifically, we leverage a type of recurrent neural network, Long Short-Term Memory (LSTM) neural networks, for (i) service specific traffic load prediction for network slicing, and (ii) Baseband Unit (BBU) pool traffic load prediction in a 5G cloud Radio Access Network (RAN). We show that leveraging a system with better accuracy to predict service requirements results in a reduction of operation costs. We focus on addressing the optical physical layer in Part III. Greater network flexibility through SDN and the growth of high bandwidth services are motivating faster service provisioning and capacity management in the optical layer. These functionalities require increased capacity along with rapid reconfiguration of network resources. Recent advances in optical hardware can enable a dramatic reduction in wavelength provisioning times in optical circuit switched networks. To support such operations, it is imperative to reconfigure the network without causing a drop in service quality to existing users. Therefore, we present a ML system that uses feedforward neural networks to predict the dynamic response of an optically circuit-switched 90-channel multi-hop Reconfigurable Optical Add-Drop Multiplexer (ROADM) network. We show that the trained deep neural network can recommend wavelength assignments for wavelength switching with minimal power excursions. We extend the performance of the ML system by implementing and testing a Hybrid Machine Learning (HML) model, which combines an analytical model with a neural network machine learning model to achieve higher prediction accuracy. In Part IV, we use a data-driven approach to address the challenge of wireless content delivery in crowded areas. We present the Adaptive Multicast Services (AMuSe) system, whose objective is to enable scalable and adaptive WiFi multicast. Specifically, we develop an algorithm for dynamic selection of a subset of the multicast receivers as feedback nodes. Further, we describe the Multicast Dynamic Rate Adaptation (MuDRA) algorithm that utilizes AMuSe’s feedback to optimally tune the physical layer multicast rate. Our experimental evaluation of MuDRA on the ORBIT testbed shows that MuDRA outperforms other schemes and supports high throughput multicast flows to hundreds of nodes while meeting quality requirements. We leverage the lessons learned from AMuSe for WiFi and use order statistics to address the performance issues with LTE evolved Multimedia Broadcast/Multicast Service (eMBMS). We present the Dynamic Monitoring (DyMo) system which provides low-overhead and real-time feedback about eMBMS performance to be used for network optimization. We focus on the Quality of Service (QoS) Evaluation module and develop a Two-step estimation algorithm which can efficiently identify the SNR Threshold as a one time estimation. DyMo significantly outperforms alternative schemes based on the Order-Statistics estimation method which relies on random or periodic sampling

Design, monitoring and performance evaluation of high capacity optical networks

Premi Extraordinari de Doctorat, promoció 2018-2019. Àmbit de les TICInternet traffic is expected to keep increasing exponentially due to the emergence of a vast number of innovative online services and applications. Optical networks, which are the cornerstone of the underlying Internet infrastructure, have been continuously evolving to carry the ever-increasing traffic in a more flexible, cost-effective, and intelligent way. Having these three targets in mind, this PhD thesis focuses on two general areas for the performance improvement and the evolution of optical networks: i) introducing further cognition to the optical layer, and ii) introducing new networking solutions revolutionizing the optical transport infrastructure. In the first part, we present novel failure detection and identification solutions in the optical layer utilizing the optical spectrum traces captured by cost-effective coarse-granular Optical Spectrum Analyzers (OSA). We demonstrate the effectiveness of the developed solutions for detecting and identifying filter-related failures in the context of Spectrum-Switched Optical Networks (SSON), as well as transmitter-related laser failures in Filter-less Optical Networks (FON). In addition, at the subsystem level we propose an Autonomic Transmission Agent (ATA), which triggers local or remote transceiver reconfiguration by predicting Bit-Error-Rate (BER) degradation by monitoring State-of-Polarization (SOP) data obtained by coherent receivers. I have developed solutions to push further the performance of the currently deployed optical networks through reducing the margins and introducing intelligence to better manage their resources. However, it is expected that the spectral efficiency of the current standard Single-Mode Fiber (SMF) based optical network approaches the Shannon capacity limits in the near future, and therefore, a new paradigm is required to keep with the pace of the current huge traffic increase. In this regard, Space Division Multiplexing (SDM) is proposed as the ultimate solution to address the looming capacity crunch with a reduced cost-per-bit delivered to the end-users. I devote the second part of this thesis to investigate different flavors of SDM based optical networks with the aim of finding the best compromise for the realization of a spectrally and spatially flexible optical network. SDM-based optical networks can be deployed over various types of transmission media. Additionally, due to the extra dimension (i.e., space) introduced in SDM networks, optical switching nodes can support wavelength granularity, space granularity, or a combination of both. In this thesis, we evaluate the impact of various spectral and spatial switching granularities on the performance of SDM-based optical networks serving different profiles of traffic with the aim of understanding the impact of switching constraints on the overall network performance. In this regard, we consider two different generations of wavelength selective switches (WSS) to reflect the technology limitations on the performance of SDM networks. In addition, we present different designs of colorless direction-less, and Colorless Directionless Contention-less (CDC) Reconfigurable Optical Add/Drop Multiplexers (ROADM) realizing SDM switching schemes and compare their performance in terms of complexity and implementation cost. Furthermore, with the aim of revealing the benefits and drawbacks of SDM networks over different types of transmission media, we preset a QoT-aware network planning toolbox and perform comparative performance analysis among SDM network based on various types of transmission media. We also analyze the power consumption of Multiple-Input Multiple-Output (MIMO) Digital Signal Processing (DSP) units of transceivers operating over three different types of transmission media. The results obtained in the second part of the thesis provide a comprehensive outlook to different realizations of SDM-based optical networks and showcases the benefits and drawbacks of different SDM realizations.Se espera que el tráfico de Internet siga aumentando exponencialmente debido a la continua aparición de gran cantidad de aplicaciones innovadoras. Las redes ópticas, que son la piedra angular de la infraestructura de Internet, han evolucionado continuamente para transportar el tráfico cada vez mayor de una manera más flexible, rentable e inteligente. Teniendo en cuenta estos tres objetivos, esta tesis doctoral se centra en dos áreas cruciales para la mejora del rendimiento y la evolución de las redes ópticas: i) introducción de funcionalidades cognitivas en la capa óptica, y ii) introducción de nuevas estructuras de red que revolucionarán el transporte óptico. En la primera parte, se presentan soluciones novedosas de detección e identificación de fallos en la capa óptica que utilizan trazas de espectro óptico obtenidas mediante analizadores de espectros ópticos (OSA) de baja resolución (y por tanto de coste reducido). Se demuestra la efectividad de las soluciones desarrolladas para detectar e identificar fallos derivados del filtrado imperfecto en las redes ópticas de conmutación de espectro (SSON), así como fallos relacionados con el láser transmisor en redes ópticas sin filtro (FON). Además, a nivel de subsistema, se propone un Agente de Transmisión Autónomo (ATA), que activa la reconfiguración del transceptor local o remoto al predecir la degradación de la Tasa de Error por Bits (BER), monitorizando el Estado de Polarización (SOP) de la señal recibida en un receptor coherente. Se han desarrollado soluciones para incrementar el rendimiento de las redes ópticas mediante la reducción de los márgenes y la introducción de inteligencia en la administración de los recursos de la red. Sin embargo, se espera que la eficiencia espectral de las redes ópticas basadas en fibras monomodo (SMF) se acerque al límite de capacidad de Shannon en un futuro próximo, y por tanto, se requiere un nuevo paradigma que permita mantener el crecimiento necesario para soportar el futuro aumento del tráfico. En este sentido, se propone el Multiplexado por División Espacial (SDM) como la solución que permita la continua reducción del coste por bit transmitido ante ése esperado crecimiento del tráfico. En la segunda parte de esta tesis se investigan diferentes tipos de redes ópticas basadas en SDM con el objetivo de encontrar soluciones para la realización de redes ópticas espectral y espacialmente flexibles. Las redes ópticas basadas en SDM se pueden implementar utilizando diversos tipos de medios de transmisión. Además, debido a la dimensión adicional (el espacio) introducida en las redes SDM, los nodos de conmutación óptica pueden conmutar longitudes de onda, fibras o una combinación de ambas. Se evalúa el impacto de la conmutación espectral y espacial en el rendimiento de las redes SDM bajo diferentes perfiles de tráfico ofrecido, con el objetivo de comprender el impacto de las restricciones de conmutación en el rendimiento de la red. En este sentido, se consideran dos generaciones diferentes de conmutadores selectivos de longitud de onda (WSS) para reflejar las limitaciones de la tecnología en el rendimiento de las redes SDM. Además, se presentan diferentes diseños de ROADM, independientes de la longitud de onda, de la dirección, y sin contención (CDC) utilizados para la conmutación SDM, y se compara su rendimiento en términos de complejidad y coste. Además, con el objetivo de cuantificar los beneficios e inconvenientes de las redes SDM, se ha generado una herramienta de planificación de red que prevé la QoT usando diferentes tipos de fibras. También se analiza el consumo de energía de las unidades DSP de los transceptores MIMO operando en redes SDM con tres tipos diferentes de medios de transmisión. Los resultados obtenidos en esta segunda parte de la tesis proporcionan una perspectiva integral de las redes SDM y muestran los beneficios e inconvenientes de sus diferentes implementacionesAward-winningPostprint (published version