Seattle Pacific University Catalog 2005-2006

https://digitalcommons.spu.edu/archives_catalogs/1087/thumbnail.jp

The time course of creativity: Multivariate classification of default and executive network contributions to creative cognition over time

Research indicates that creative cognition depends on both associative and controlled processes, corresponding to the brain's default mode network (DMN) and executive control network (ECN) networks. However, outstanding questions include how the DMN and ECN operate over time during creative task performance, and whether creative cognition involves distinct generative and evaluative stages. To address these questions, we used multivariate pattern analysis (MVPA) to assess how the DMN and ECN contribute to creative cognition over three successive time phases during the production of a single creative idea. Training classifiers to predict trial condition (creative vs non-creative), we used classification accuracy as a measure of the extent of creative activity in each brain network and time phase. Across both networks, classification accuracy was highest in early phases, decreased in mid phases, and increased again in later phases, following a U-shaped curve. Notably, classification accuracy was significantly greater in the ECN than the DMN during early phases, while differences between networks at later time phases were non-significant. We also computed correlations between classification accuracy and human-rated creative performance, to assess how relevant the creative activity in each network was to the creative quality of ideas. In line with expectations, classification accuracy in the DMN was most related to creative quality in early phases, decreasing in later phases, while classification accuracy in the ECN was least related to creative quality in early phases, increasing in later phases. Given the theorized roles of the DMN in generation and the ECN in evaluation, we interpret these results as tentative evidence for the existence of separate generative and evaluative stages in creative cognition that depend on distinct neural substrates

P4TE: PISA Switch Based Traffic Engineering in Fat-Tree Data Center Networks

This work presents P4TE, an in-band traffic monitoring, load-aware packet forwarding, and flow rate controlling mechanism for traffic engineering in fat-tree topology-based data center networks using PISA switches. It achieves sub-RTT reaction time to change in network conditions, improved flow completion time, and balanced link utilization. Unlike the classical probe-based monitoring approach, P4TE uses an in-band monitoring approach to identify traffic events in the data plane. Based on these events, it re-adjusts the priorities of the paths. It uses a heuristic-based load-aware forwarding path selection mechanism to respond to changing network conditions and control the flow rate by sending feedback to the end hosts. It is implementable on emerging v1model.p4 architecture-based programmable switches and capable of maintaining the line-rate performance. Our evaluation shows that P4TE uses a small amount of resources in the PISA pipeline and achieves an improved flow completion time than ECMP and HULA

A comprehensive methodology for computational fluid dynamics combustion modeling of industrial diesel engines

Combustion control and optimization is of great importance to meet future emission standards in diesel engines: increase in break mean effective pressure at high loads and extension of the operating range of advanced combustion modes seem to be the most promising solutions to reduce fuel consumption and pollutant emissions at the same time. Within this context, detailed computational fluid dynamics tools are required to predict the different involved phenomena such as fuel-air mixing, unsteady diffusion combustion and formation of noxious species. Detailed kinetics, consistent spray models and high quality grids are necessary to perform predictive simulations which can be used either for design or diagnostic purposes. In this work, the authors present a comprehensive approach which was developed using an open-source computational fluid dynamics code. To minimize the pre-processing time and preserve results' accuracy, algorithms for automatic mesh generation of spray-oriented grids were developed and successfully applied to different combustion chamber geometries. The Lagrangian approach was used to describe the spray evolution while the combustion process is modeled employing detailed chemistry and, eventually, considering turbulence-chemistry interaction. The proposed computational fluid dynamics methodology was first assessed considering inert and reacting experiments in a constant-volume vessel, where operating conditions typical of heavy-duty diesel engines were reproduced. Afterward, engine simulations were performed considering two different load points and two piston bowl geometries, respectively. Experimental validation was carried out by comparing computed and experimental data of in-cylinder pressure, heat release rate and pollutant emissions (NOx, CO and soot)

Computational Study of the Injection Process in Gasoline Direct Injection (GDI) Engines

[ES] La creciente preocupación por los problemas medioambientales, la disponibilidad de combustibles fósiles unido a la gran demanda de vehículos, han llevado a los gobiernos a regular las emisiones emitidas a la atmósfera. Existen propuestas de adoptar fuentes de energía renovables. Sin embargo, la sustitución de los combustibles derivados del petróleo no será fácil, rápida o rentable, y el transporte propulsado por motores de combustión interna (ICE) seguirá destacando en los próximos años. La eficiencia de la combustión y el rendimiento del motor están influenciados por el complejo proceso de inyección. La inyección directa de gasolina (GDI) aumenta el ahorro de combustible y cumple los requisitos de emisiones contaminantes, aunque queda potencial por descubrir. Por ello, ha sido objeto de estudio en los últimos años y, en consecuencia, de la presente Tesis. Este trabajo tiene como motivación mejorar el entendimiento en el campo del GDI. La compleja naturaleza transitoria del proceso de inyección hace que el estudio experimental sea un desafío. La Mecánica de Fluidos Computacional (CFD) surge como una potente alternativa a los experimentos y ha sido adoptada para esta investigación. Bajo este contexto, el objetivo de la presente Tesis es desarrollar una metodología predictiva para la caracterización hidráulica del inyector, capaz de ser aplicada a las actuales y futuras generaciones de inyectores GDI, independientemente de las características del inyector y del software de estudio. Una vez validada, el objetivo posterior es utilizar los resultados para analizar el comportamiento del chorro. Este enfoque busca seguir los pasos de la comunidad científica sustituyendo la práctica experimental. La validación de la metodología se lleva a cabo mediante su aplicación en dos inyectores GDI solenoides multi-orificio diferentes. Además, se han utilizado dos códigos CFD comerciales: CONVERGE y StarCCM+. La metodología predictiva se centra en el estudio del flujo interno y el campo cercano para caracterizar hidráulicamente el inyector. El problema a tratar se define como un sistema multifásico en un marco Euleriano y considerando un único fluido. El tratamiento del flujo multifásico se realiza mediante el enfoque Volume-of-Fluid (VOF). Además, se emplea el Homogeneous Relaxation Model (HRM) para considerar el intercambio de masa entre las fases líquida y vapor debido a cavitación y flash boiling. La turbulencia se ha tratado a partir de los enfoques Reynolds-Averaged Navier-Stokes (RANS) y Large Eddy Simulations (LES). Por otro lado, en cuanto al estudio del flujo externo, se ha adoptado el Discrete Droplet Model (DDM). La atomización y el chorro están influenciados por la geometría de la tobera, por lo que la estrategia de acoplamiento del flujo interno y externo complementa los análisis. Se han adoptado enfoques de acoplamiento unidireccional y mapeado, utilizando como parámetros de entrada los datos de flujo interno de la validada metodología. Esta Tesis aporta una nueva y valiosa metodología predictiva con una elevada precisión a la hora de caracterizar el proceso de inyección en comparativa con datos experimentales. Por otro lado, es directamente trasferible a distintos códigos de cálculo así como aplicable a inyectores con características dispares sin perjudicar las exigencias del modelo. La correcta caracterización del flujo interno ha permitido emplear los datos obtenidos para analizar el comportamiento del chorro eliminando la necesidad de usar datos experimentales. Los resultados obtenidos capturan el comportamiento macroscópico del chorro con una precisión comparable a los experimentos. Aunque todavía hay muchos retos que afrontar, la presente Tesis supone un gran avance en el campo del GDI. El remarcable progreso se debe al desarrollo y uso de una metodología totalmente predictiva, que permite prescindir de la mayoría de los experimentos para contribuir a una mayor y más amplia visión de la física del proceso de inyección.[CA] La creixent preocupació pels problemes ambientals, la limitada disponibilitat de combustibles fòssils, acompanyat a la gran demanda de vehicles, ha portat el govern a regular els nivells d'emissions emesos a l'atmosfera. Existeixen propostes d'adoptar fonts d'energia renovables. Tanmateix, la substitució dels combustibles líquids derivats del petroli no es durà a terme de forma fàcil, ràpida o rentable, i el transport propulsat per motors de combustió interna (ICE) continuarà destacant en els pròxims anys. L'eficiència de la combustió i el rendiment del motor són fortament influenciats pel complex procés d'injecció. La injecció directa de gasolina (GDI) augmenta l'estalvi de combustible i complix amb els requisits d'emissions, encara que queda molt potencial per descobrir. Per això, aquest ha sigut objecte d'investigació en els últims anys i, com a conseqüència, d'aquesta Tesi. Aquest treball té com a motivació millorar l'enteniment en el camp del GDI. La complexa natura transitòria de la injecció fa que l'estudi experimental siga força complex. La Mecànica de Fluids Computacional (CFD) sorgeix com una potent alternativa als experiments, i ha sigut adoptada per aquesta investigació. Baix aquest mateix context, es proposa com a objectiu principal d'aquesta Tesi el desenvolupament d'una metodologia predictiva per a la caracterització hidràulica de l'injector, capaç de ser aplicada a les actuals i futures generacions d'injectors GDI (independentment de les característiques de l'injector i del software d'estudi). Una vegada validada, el posterior objectiu és analitzar el comportament de l'esprai. Aquest enfocament busca seguir els passos de la comunitat científica substituint la pràctica experimental. La validació de la metodologia ha sigut duta a terme mitjançant la seva aplicació en dos injectors GDI solenoides multi-orifici. A més, s'han utilitzat dos software CFD comercials: CONVERGE i StarCCM+. La metodologia predictiva se centra en l'estudi del flux intern i el camp proper per tal de caracteritzar hidràulicament l'injector. El problema a tractar es defineix en base a un sistema multi-fàsic en un marc Eulerià i considerant un únic fluid. El tractament del fluid multi-fàsic es realitza mitjançant l'aproximació Volume-of-Fluid (VOF). A més, s'utilitza el Homogeneous Relaxation Model (HRM) per tal de considerar l'intercambi de massa entre les fases líquida i vapor degut als fenòmens de cavitació i flash boiling. La turbulència s'ha tractac a través dels enfocaments Reynolds-Averaged Navier-Stokes (RANS) i Large Eddy Simulations (LES). Pel que fa a l'estudi del fluix extern, s'ha adoptat el Discrete Droplet Model (DDM). Sent conscients que el comportament l'atomització i l'esprai estan influenciats per la geometria de la tovera, l'estratègia d'acoblament del flux intern i extern complementa les anàlisis. S'han adoptat els enfocaments d'acoblament unidireccional i mapejat, utilitzant com a paràmetres d'entrada les dades del flux intern obtingudes amb la validada metodologia. Aquesta Tesi aporta una nova i valuosa metodologia predictiva amb una elevada precisió a l'hora de caracteritzar el procés d'injecció en comparativa amb dades experimentals. És directament transferible a diversos codis de càlcul així com aplicable a injectors amb característiques dispars sense perjudicar les exigències del model. La correcta caracterització del flux intern ha permès utilitzar les dades obtingudes per tal d'analitzar el comportament de l'esprai, eliminant la necessitat d'emprar dades experimentals. Els resultats obtinguts d'aquest estudi capturen el comportament macroscòpic de l'esprai amb una precisió comparable als experiments. Encara que queden molts reptes per afrontar, aquesta Tesi aporta un important avanç al camp del GDI. La ruptura prové del desenvolupament i ús d'una metodologia completament predictiva, que substitueix els experiments requerits i així contribueix a una millor i més ampla visió de la física del procés d'injecció.[EN] Concerns about climate change, availability of fuel resources and the high demand for vehicles, have led governments to regulate the level of pollution emitted by engines into the atmosphere. There is a strong desire to adopt renewable and sustainable energy sources. However, the substitution of liquid fuels derived from petroleum will not emerge easily, quickly or economically, and Internal Combustion Engines (ICE) will continue to excel for the next few years. Combustion efficiency and engine performance are strongly influenced by the complex fuel injection process. Gasoline Direct Injection (GDI) strategies increase fuel economy and meet emission requirements, although many challenges remain, which has therefore been one of the main research objectives in recent years and of this Thesis. The present research aims to provide a better understanding in the field of GDI. The transient and complex nature of the injection process makes the experimental study of GDI quite challenging. Therefore, Computational Fluid Dynamics (CFD) emerges as a powerful alternative adopted for this research. In this context, the main objective of the present Thesis is to develop a predictive methodology capable of being applied to current and future generations of GDI injectors, regardless of the injector features and the software employed, for the hydraulic characterization of the injector. Once validated, the subsequent goal is to employ the obtained results to analyze the behavior of the spray downstream of the injector. The approach attempts to follow the footsteps of the research community to avoid experimental practice. The predictive methodology has been validated through its application to two multi-hole solenoid GDI injectors with different features. In addition, the mentioned methodology has been evaluated using diverse commercial software: CONVERGE and StarCCM+. The methodology focuses on the study of the internal and near-field flow to hydraulically characterize the injector. So the problem to be addressed is a multi-phase system, performed in an Eulerian framework, modeled through a single-fluid approach. The multi-phase flow is treated by means of the Volume-of-Fluid (VOF) approach. Homogeneous Relaxation Model (HRM) is employed to consider the mass exchange between liquid and vapor fuel phases, due to cavitation and flash boiling. The turbulence treatment has been performed from both Reynolds-Averaged Navier-Stokes (RANS) and Large Eddy Simulations (LES) approaches. Regarding the external flow study, the Discrete Droplet Model (DDM) has been adopted. In addition, being aware that atomization and spray behavior is greatly influenced by the nozzle geometry, the coupling strategy of the internal and external flow complements the analyses. One-way coupling and mapping approaches have been adopted, using as input parameters the internal flow data obtained from the already validated methodology. Accordingly, this Thesis provides a new and valuable predictive methodology, which has demonstrated a high accuracy in characterizing the flow behavior during the injection process through comparison with experimental data. It has also proven to be directly transferable to different CFD software and applicable to injectors with dissimilar characteristics without compromising the requirements of the model. The correct internal flow characterization has made it possible to employ the obtained data to analyze the spray patterns, which eliminates the need to consider experimental data. The outcomes of this study macroscopically capture the jet behavior with an accuracy comparable to experiments under different operating conditions. Although there are still many challenges to face, the present Thesis brings a breakthrough in the field of GDI. The quantum leap arises from the development and use of a fully predictive methodology, allowing to avoid most experiments to contribute to a greater and broader vision of the injection process physics.María Martínez García has been founded through a grant from the Government of Generalitat Valenciana with reference ACIF/2018/118 and financial support from the European Union. These same institutions, Government of Generalitat Valenciana and the European Union, supported through a grant for pre-doctoral stays out of the Comunitat Valenciana with reference BEFPI/2020/057 the research carried out during the stay at Aerothermochemistry and Combustion Systems Laboratory, Swiss Federal Institute of Technology, ETH Zurich, Switzerland. Special gratitude from the author to both institutions, Government of Generalitat Valenciana and the European Union, for making this dream possibleMartínez García, M. (2022). Computational Study of the Injection Process in Gasoline Direct Injection (GDI) Engines [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/185180TESI

Optimising data centre operation by removing the transport bottleneck

Data centres lie at the heart of almost every service on the Internet. Data centres are used to provide search results, to power social media, to store and index email, to host “cloud” applications, for online retail and to provide a myriad of other web services. Consequently the more efficient they can be made the better for all of us. The power of modern data centres is in combining commodity off-the-shelf server hardware and network equipment to provide what Google’s Barrosso and Ho ̈lzle describe as “warehouse scale” computers. Data centres rely on TCP, a transport protocol that was originally designed for use in the Internet. Like other such protocols, TCP has been optimised to maximise throughput, usually by filling up queues at the bottleneck. However, for most applications within a data centre network latency is more critical than throughput. Consequently the choice of transport protocol becomes a bottleneck for performance. My thesis is that the solution to this is to move away from the use of one-size-fits-all transport protocols towards ones that have been designed to reduce latency across the data centre and which can dynamically respond to the needs of the applications. This dissertation focuses on optimising the transport layer in data centre networks. In particular I address the question of whether any single transport mechanism can be flexible enough to cater to the needs of all data centre traffic. I show that one leading protocol (DCTCP) has been heavily optimised for certain network conditions. I then explore approaches that seek to minimise latency for applications that care about it while still allowing throughput-intensive applications to receive a good level of service. My key contributions to this are Silo and Trevi. Trevi is a novel transport system for storage traffic that utilises fountain coding to max- imise throughput and minimise latency while being agnostic to drop, thus allowing storage traffic to be pushed out of the way when latency sensitive traffic is present in the network. Silo is an admission control system that is designed to give tenants of a multi-tenant data centre guaranteed low latency network performance. Both of these were developed in collaboration with others

Application of a flamelet-based combustion model to diesel-like reacting sprays

[ES] El objetivo de esta tesis es la investigación y análisis de la estructura interna de los chorros diésel reactivos y el efecto de las condiciones de contorno en los parámetros asociados a la combustión. Este objetivo se consigue por medio de la simulación numérica del chorro con modelos de turbulencia RANS y LES usando un modelo de combustión avanzado basado en el concepto flamelet. Para este estudio, se aplica una aproximación simplificada de las flamelets de difusión, conocidas en la literatura como Flamelets de Difusión Aproximadas (ADF en inglés), como fundamento del modelo de combustión. En una primera etapa, el modelo se valida con combustibles de diferente complejidad química en regímenes estacionarios y transitorios para el conjunto de posibles velocidades de deformación. Una vez se confirma su idoneidad para condiciones encontradas en chorros diésel, se aplica a la simulación del chorro A del Engine Combustion Network (ECN), representativo de chorros diésel. Para proporcionar un cuadro completo de los fenómenos subyacentes, la combustión se analiza inicialmente para condiciones homogéneas y llamas laminares para las distintas condiciones de contorno de este experimento. Después este análisis se complementa con la simulación de diferentes mecanismos químicos para determinar cómo las características del encendido predichas por el esquema de oxidación afectan a la propagación de llama. Los resultados obtenidos en esta etapa se enlazan con el análisis del chorro turbulento en el contexto de simulaciones RANS y LES para describir cómo el fenómeno de la combustión se modifica con los diferentes niveles de complejidad física. La estructura del chorro turbulento se describe profundamente para las distintas condiciones de contorno y mecanismos químicos en términos de mezcla y escalares reactivos para las fases temporales y las regiones espaciales de la llama. La satisfactoria concordancia con los resultados experimentales muestran que el concepto flamelet, y más particularmente el modelo ADF, es adecuado para las simulaciones de chorros diésel.[CA] L'objectiu d'esta tesi és la investigació i anàlisi de l'estructura interna dels dolls dièsel reactius i l'efecte de les condicions de contorn en els paràmetres associats a la combustió. Este objectiu s'aconsegueix per mitjà de la simulació numèrica del doll amb models de turbulència RANS i LES usant un model de combustió avançat basat en el concepte flamelet. Per a este estudi, s'aplica una aproximació simplificada de les flamelets de difusió, conegudes a la literatura com Flamelets de Difusió Aproximades (ADF en anglés), com a fonament del model de combustió. En una primera etapa, el model es valida amb combustibles de diferent complexitat química en règims estacionaris i transitoris per al conjunt de possibles velocitats de deformació. Una vegada es confirma la seua idoneïtat per a condicions trobades en dolls dièsel, s'aplica a la simulació del doll A del Engine Combustion Network (ECN), representatiu de dolls dièsel. Per a proporcionar un cuadre complet dels fenòmens subjacents, la combustió s'analitza inicialment per a condicions homogènies i flames laminars per a les distintes condicions de contorn d'aquest experiment. Després esta anàlisi es complementa amb la simulació de diferents mecanismes químics per a determinar com les característiques de l'encesa predites per l'esquema d'oxidació afecten la propagació de flama. Els resultats obtinguts en esta etapa s'enllacen amb l'anàlisi del doll turbulent en el context de simulacions RANS i LES per a descriure com el fenomen de la combustió es modifica amb els diferents nivells de complexitat física. L'estructura del doll turbulent es descriu profundament per a les distintes condicions de contorn i mecanismes químics en termes de mescla i escalars reactius per a les fases temporals i les regions espacials de la flama. La satisfactòria concordança amb els resultats experimentals mostren que el concepte flamelet, i més particularment el model ADF, és adequat per a les simulacions de dolls dièsel.[EN] The objective of this thesis is the investigation and analysis of the internal structure of diesel-like reacting sprays and the effect of boundary conditions on combustion related parameters. This objective is achieved by means of the numerical simulation of the spray with RANS and LES turbulence models using an advanced combustion model based on the flamelet concept. For this study, a simplified approach for diffusion flamelets, known in the literature as Approximated Diffusion Flamelet (ADF), is applied as the basis of the combustion model. In a first step, this model is validated for fuels with different chemical complexity in steady and transient regimes for the whole set of possible strain rates. Once its suitability is confirmed for conditions found in diesel sprays, it is applied to the simulation of spray A from the Engine Combustion Network (ECN), representative of diesel-like sprays. In order to provide a complete picture of the underlying phenomena, combustion is initially analysed in homogeneous conditions and laminar flames for the different boundary conditions of this experiment. Later, this analysis is complemented with the simulation of different chemical mechanisms in order to determine how the ignition characteristics predicted by the oxidation scheme affect to the flame propagation. The results obtained at this stage are connected with the analysis of the turbulent spray in the context of RANS and LES simulations as a way to track how combustion phenomenon is modified at the different levels of physical complexity. The turbulent spray structure is thoroughly described for the different boundary conditions and chemical schemes in terms of mixing and reactive variables for both temporal phases and spatial flame regions. The satisfactory agreement with experimental results shows that the flamelet concept, and more particularly the ADF model, is suitable for diesel-like sprays simulations.Pérez Sánchez, EJ. (2019). Application of a flamelet-based combustion model to diesel-like reacting sprays [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/117316TESI

LEARNING OF DENSE OPTICAL FLOW, MOTION AND DEPTH, FROM SPARSE EVENT CAMERAS

With recent advances in the field of autonomous driving, autonomous agents need to safely navigate around humans or other moving objects in unconstrained, highly dynamic environments. In this thesis, we demonstrate the feasibility of reconstructing dense depth, optical flow and motion information from a neuromorphic imaging device, called Dynamic Vision Sensor (DVS). The DVS only records sparse and asynchronous events when the changes of lighting occur at camera pixels. Our work is the first monocular pipeline that generates dense depth and optical flow from sparse event data only. To tackle this problem of reconstructing dense information from sparse information, we introduce the Evenly-Cascaded convolutional Network (ECN), a bio-inspired multi-level, multi-resolution neural network architecture. The network features an evenly-shaped design, and utilization of both high and low level features. With just 150k parameters, our self-supervised pipeline is able to surpass pipelines that are 100x larger. We evaluate our pipeline on the MVSEC self driving dataset and present results for depth, optical flow and and egomotion estimation in wild outdoor scenes. Due to the lightweight design, the inference part of the network runs at 250 FPS on a single GPU, making the pipeline ready for realtime robotics applications. Our experiments demonstrate significant improvements upon previous works that used deep learning on event data, as well as the ability of our pipeline to perform well during both day and night. We also extend our pipeline to dynamic indoor scenes with independent moving objects. In addition to camera egomotion and a dense depth map, the network utilizes a mixture model to segment and compute per-object 3D translational velocities for moving objects. For this indoor task we are able to train a shallow network with just 40k parameters, which computes qualitative depth and egomotion. Our analysis of the training shows modern neural networks are trained on tangled signals. This tangling effect can be imagined as a blurring introduced both by nature and by the training process. We propose to untangle the data with network deconvolution. We notice significantly better convergence without using any standard normalization techniques, which suggests us deconvolution is what we need

Understanding the characteristics of Internet traffic and designing an efficient RaptorQ-based data transport protocol for modern data centres

This thesis is the amalgamation of research on efficient data transport protocols for data centres and a comprehensive and systematic study of Internet traffic, which came as a result of the need to understand traffic patterns and workloads in modern computer networks. The first part of the thesis is on the development of efficient data transport pro- tocols for data centres. We study modern data transport protocols for data centres through large scale simulations using the OMNeT++ simulator. We developed and experimented with an OMNeT++ model of NDP. This has led to the identification of limitations of the state of the art and the formulation of research questions with respect to data transport protocols for modern data centres. The developed model includes an implementation of a Fat-tree topology and per-packet ECMP load bal- ancing. We discuss how we integrated the model with the INET Framework and validated it by running various experiments that test different model parameters and components. This work revealed limitations of NDP with respect to efficient one-to-many and many-to-one communication in data centres, which led to the de- velopment of SCDP, a novel and general-purpose data transport protocol for data centres that, in contrast to all other protocols proposed to date, natively supports one-to-many and many-to-one data communication, which is extremely common in modern data centres. SCDP does so without compromising on efficiency for short and long unicast flows. SCDP achieves this by integrating RaptorQ codes with receiver-driven data transport, in-network packet trimming and Multi-Level Feed- back Queuing (MLFQ); (1) RaptorQ codes enable efficient one-to-many and many- to-one data transport; (2) on top of RaptorQ codes, receiver- driven flow control, in combination with in-network packet trimming, enable efficient usage of network re- sources as well as multi-path transport and packet spraying for all transport modes. Incast and Outcast are eliminated; (3) the systematic nature of RaptorQ codes, in combination with MLFQ, enable fast, decoding-free completion of short flows. We extensively evaluated SCDP in a wide range of simulated scenarios with realistic data centre workloads. For one-to-many and many-to-one transport sessions, SCDP performs significantly better than NDP. For short and long unicast flows, SCDP performs equally well or better compared to NDP. In the second part of the thesis, we extensively study Internet traffic. Getting good statistical models of traffic on network links is a well-known, often-studied problem. A lot of attention has been given to correlation patterns and flow duration. The distribution of the amount of traffic per unit time is an equally important but less studied problem. We study a large number of traffic traces from many different networks including academic, commercial and residential networks using state-of-the-art statistical techniques. We show that the log-normal distribution is a better fit than the Gaussian distribution. We also investigate a second, heavy- tailed distribution and show that its performance is better than Gaussian but worse than log-normal. We examine anomalous traces which are a poor fit for all tested distributions and show that this is often due to traffic outages or links that hit maximum capacity. Stationarity tests showed that the traffic is stationary at some range of aggregation times. We demonstrate the utility of the log-normal distribution in two contexts: predicting the proportion of time traffic will exceed a given level (for link capacity estimation) and predicting 95th percentile pricing. We also show the log-normal distribution is a better predictor than Gaussian orWeibull distributions

Enhancing QUIC over Satellite Networks

The use of Satellite Communication (SATCOM) networks for broadband connectivity has recently seen an increase in popularity due to, among other factors, the rise of the latest generations of cellular networks (5G/6G) and the deployment of high-throughput satellites. In parallel, major advances have been witnessed in the context of the transport layer: first, the standardization and early deployment of QUIC, a new-generation and general-purpose transport protocol; and second, modern congestion control proposals such as the Bottleneck Bandwidth and Round-trip propagation time (BBR) algorithm. Even though satellite links introduce several challenges for transport layer mechanisms, mainly due to their long propagation delay, satellite Internet providers have relied on TCP connection-splitting solutions implemented by Performance-Enhancing Proxies (PEPs) to greatly overcome many of these challenges. However, due to QUIC's fully encrypted nature, these performance-boosting solutions become nearly impossible for QUIC traffic, leaving it in great disadvantage when competing against TCP-PEP. In this context, IETF QUIC WG contributors are currently investigating this matter and suggesting new solutions that can help improve QUIC's performance over SATCOM. This thesis aims to study some of these proposals and evaluate them through experimentation using a real network testbed and an emulated satellite link