Low Latency Low Loss Scalable Throughput (L4S) is a technology which aims to provide high throughput and low latency for the IP traffic, lowering also the probability of packet loss. To reach this goal, it relies on Explicit Con- gestion Notification (ECN), a mechanism to signal congestion in the network avoiding packets drop. The congestion signals are then managed at sender and receiver side thanks to scalable congestion control algorithms. Initially, in this work the challenges to implement L4S in a 5G network have been analyzed. Using a proprietary state-of-the-art network simulator, L4S have been imple- mented at the Packed Data Convergence Protocol layer in a 5G network. The 5G network scenario represents a context where the physical layer has a carrier frequency of 600 MHz, a transmission bandwidth of 9 MHz, and the proto- col stack follows the New Radio (NR) specifications. L4S has been adopted to support Augmented Reality (AR) video gaming traffic, using the IETF ex- perimental standard Self-Clocked Rate Adaptation for Multimedia (SCReAM) for congestion control. The results showed that when supported by L4S, the video gaming traffic experiences lower delay than without L4S support. The improvement on latency comes with an intrinsic trade-off between throughput and latency. In all the cases analyzed, L4S yields to average application layer throughput above the minimum requirements of high-rate latency-critical ap- plication, even at high system load. Furthermore, the packet loss rate has been significantly reduced thanks to the introduction of L4S, and if used in combi- nation with a Delay Based Scheduler (DBS), a packet loss rate very close to zero has been reached. / Low Latency Low Loss Scalable Throughput (L4S) är en teknik som syftar till att ge hög bittakt och låg fördröjning för IP-trafik, vilket också minskar sanno- likheten för paketförluster. För att nå detta mål förlitar det sig på Explicit Cong- estion Notification (ECN), en mekanism för att signalera "congestion", det vill säga köuppbyggnad i nätverket för att undvika att paketet kastas. Congestion- signalerna hanteras sedan vid avsändare och mottagarsida där skalbar anpass- ning justerar bittakten efter rådande omständigheter. I detta arbete har utma- ningarna att implementera L4S i ett 5G-nätverk analyserats. Sedan har L4S implementerats på PDCP lagret i ett 5G-nätverkssammanhang genom att an- vända en proprietär nätverkssimulator. För att utvärdera fördelarna med imple- menteringen har L4S-funktionerna använts för att stödja Augmented Reality (AR) videospelstrafik, med IETF-experimentella standard Self-Clocked Rate Adaptation for Multimedia (SCReAM) för bitrate-kontroll. Resultaten visade att med stöd av L4S upplever videospelstrafiken lägre latens än utan stöd av L4S. Förbättringen av latens kommer med nackdelen av en minskning av bit- takt som dikteras av den inneboende avvägningen mellan bittakt och latens. I vilket fall som helst är kapacitetsminskningen med L4S rimlig, eftersom goda kapacitetsprestanda har uppnåtts även vid hög systembelastning. Vidare har paketförlustfrekvensen reducerats avsevärt tack vare införandet av L4S, och om den används i kombination med en Delay baserad schemaläggare (DBS) har en paketförluster mycket nära noll uppnåtts.

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<p>Advances in network technologies and computing resources have led to the deployment of large scale computational systems, such as those following Grid or Cloud architectures. The scheduling problem is a significant issue in these distributed computing environments, where a scheduling algorithm should consider multiple objectives and performance metrics. Moreover, heterogeneity is increasing at both the application and resource levels. The heterogeneity in these systems can have a huge impact on performance in terms of metrics such as average completion time. However, traditional Grid and Cloud scheduling algorithms neglect heterogeneity in their scheduling decisions. This PhD dissertation studies the scheduling challenges in Computational Grid, Data Grid, and Cloud computing systems, and introduces new scheduling algorithms for each of these systems.</p> <p>The main issue in Grid scheduling is the wide distribution of resources. As a result, gathering full state information can add huge overhead to the scheduler. This thesis introduces a Computational Grid scheduling algorithm which simultaneously addresses minimizing completion times (by considering system heterogeneity), while requiring zero dynamic state information. Simulation results show the promising performance of this algorithm, and its robustness with respect to errors in parameter estimates.</p> <p>In the case of Data Grid schedulers, an efficient scheduling decision should select a combination of resources for a task that simultaneously mitigates the computation and the communication costs. Therefore, these schedulers need to consider a large search space to find an appropriate combination. This thesis introduces a new Data Grid scheduling algorithm, which dynamically makes replication and scheduling decisions. The proposed algorithm reduces the search space, decreases the required state information, and improves the performance by considering the system heterogeneity. Simulation results illustrate the promising performance of the introduced algorithm.</p> <p>Cloud computing can be considered as a next generation of Grid computing. One of the main challenges in Cloud systems is the enormous expansion of data in different applications. The MapReduce programming model and Hadoop framework were designed as a solution for executing large scale data-intensive applications. A large number of (heterogeneous) users, using the same Hadoop cluster, can result in tensions between the various performance metrics by which such systems are measured. This research introduces and implements a Hadoop scheduling system, which uses system information such as estimated job arrival rates and mean job execution times to make scheduling decisions. The proposed scheduling system, named COSHH (Classification and Optimization based Scheduler for Heterogeneous Hadoop systems), considers heterogeneity at both the application and cluster levels. The main objective of COSHH is to improve the average completion time of jobs. However, as it is concerned with other key Hadoop performance metrics, it also achieves competitive performance under minimum share satisfaction, fairness and locality metrics, with respect to other well-known Hadoop schedulers. The proposed scheduler can be efficiently applied in heterogeneous clusters, in contrast to most Hadoop schedulers, which assume homogeneous clusters.</p> <p>A Hadoop system can be described based on three factors: cluster, workload, and user. Each factor is either heterogeneous or homogeneous, which reflects the heterogeneity level of the Hadoop system. This PhD research studies the effect of heterogeneity in each of these factors on the performance of Hadoop schedulers. Three schedulers which consider different levels of Hadoop heterogeneity are used for the analysis: FIFO, Fair sharing, and COSHH. Performance issues are introduced for Hadoop schedulers, and experiments are provided to evaluate these issues. The reported results suggest guidelines for selecting an appropriate scheduler for different Hadoop systems. The focus of these guidelines is on systems which do not have significant fluctuations in the number of resources or jobs.</p> <p>There is a considerable challenge in Hadoop to schedule tasks and resources in a scalable manner. Moreover, the potential heterogeneous nature of deployed Hadoop systems tends to increase this challenge. This thesis analyzes the performance of widely used Hadoop schedulers including FIFO and Fair sharing and compares them with the COSHH scheduler. Based on the discussed insights, a hybrid solution is introduced, which selects appropriate scheduling algorithms for scalable and heterogeneous Hadoop systems with respect to the number of incoming jobs and available resources. The proposed hybrid scheduler considers the guidelines provided for heterogeneous Hadoop systems in the case that the number of jobs and resources change considerably.</p> <p>To improve the performance of high priority users, Hadoop guarantees minimum numbers of resource shares for these users at each point in time. This research compares different scheduling algorithms based on minimum share consideration and under different heterogeneous and homogeneous environments. For this evaluation, a real Hadoop system is developed and evaluated using Facebook workloads. Based on the experiments performed, a reliable scheduling algorithm is suggested which can work efficiently in different environments.</p> / Doctor of Philosophy (PhD)

Analyzing the brain's behavior in terms of its neuronal activity is the fundamental purpose of Brain-Machine Interfaces (BMIs). Neuronal activity is often assumed to be encoded in the rate of neuronal action potential spikes. Successful performance of a BMI system is tied to the efficiency of its individual processing elements such as spike detection, sorting and decoding. To achieve reliable operation, BMIs are equipped with hundreds of electrodes at the neural interface. While a single electrode/tetrode communicates with up to four neurons at a given instant of time, a typical interface communicates with an ensemble of hundreds or even thousands of neurons. However, translation of these signals (data) into usable information for real-time BMIs is bottlenecked due to the lack of efficient real-time algorithms and real-time hardware that can handle massively parallel channels of neural data. The research presented here addresses this issue by developing real-time neural processing algorithms that can be implemented in reconfigurable hardware and thus, can be scaled to handle thousands of channels in parallel. The developed reconfigurable system serves as an evaluation platform for investigating the fundamental design tradeoffs in allocating finite hardware resources for a reliable BMI. In this work, the generic architectural layout needed to process neural signals in a massive scale is discussed. A System-on-Chip design with embedded system architecture is presented for FPGA hardware realization that features (a) scalability (b) reconfigurability, and (c) real-time operability. A prototype design incorporating a dual processor system and essential neural signal processing routines such as real-time spike detection and sorting is presented. Two kinds of spike detectors, a simple threshold-based and non-linear energy operator-based, were implemented. To achieve real-time spike sorting, a fuzzy logic-based spike sorter was developed and synthesized in the hardware. Furthermore, a real-time kernel to monitor the high-level interactions of the system was implemented. The entire system was realized in a platform FPGA (Xilinx Virtex-5 LX110T). The system was tested using extracellular neural recordings from three different animals, a owl monkey, a macaque and a rat. Operational performance of the system is demonstrated for a 300 channel neural interface. Scaling the system to 900 channels is trivial. / Electrical and Computer Engineering

In this article we present an exploratory interface for the analysis of movies. Movies are segmented into shots, which are in turn displayed as scalable MovieBarcodes, i.e. film scholars can zoom into the MovieBarcode representation to explore single chapters or scenes. The tool also provides a search function that can be used to filter shots according to characters or keywords, which are extracted automatically from subtitles and movie scripts. The filtered results are also displayed as interactive MovieBarcodes. Our tool can be used to aid film scholars during the research process of a movie analysis, as it provides new perspectives on a continuous, time-based medium.

[pt] À medida que a internet das coisas (IoT) incorpora cada vez mais dispositivos móveis e objetos, isso também demanda serviços escaláveis capazes de lidar com um número crescente de dispositivos móveis conectados simultaneamente. Como resultado, a capacidade de oferecer serviços confiáveis, que sejam adaptáveis a diferentes cenários, eficientes e de alto desempenho em um ambiente altamente móvel é crucial para atender às expectativas dos usuários e impulsionar a adoção em massa de aplicações móveis IoT (IoMT). Neste trabalho, projetamos e implementamos uma arquitetura autoescalável e configurável, de maneira que o administrador das aplicações consiga configurar parâmetros de escalabilidade de acordo com a necessidade, usando o Kubernetes no ContextNet, um middleware distribuído IoMT, e avaliamos o desempenho de nossa implementação em diferentes cenários de escalabilidade e mobilidade. / [en] As the Internet of Things (IoT) increasingly incorporates mobile devices and objects, this also calls for scalable services capable of handling a growing number of concurrently connected mobile devices. Consequently, the ability to provide reliable services that are adaptable to different scenarios, efficient, and high-performing in a highly mobile environment is crucial to meet user expectations and drive widespread adoption of IoT mobile applications (IoMT). In this work, we design and implement a self-scalable and configurable architecture, allowing application administrators to configure scalability parameters according to their needs, using Kubernetes in ContextNet, a distributed IoMT middleware, and evaluate the performance of our implementation in different scalability and mobility scenarios.

The automotive industry is facing some of the most difficult design challenges in industry history. Developing innovative methods to reduce fossil fuel dependence is imperative for maintaining compliance with government regulations and consumer demand. In addition to powertrain design, route selection contributes to vehicle environmental impact. The objective of this thesis is to develop a methodology for evaluating the energy consumption of each route option for a specific vehicle. A 'backwards' energy tracking method determines tractive demand at the wheels from route requirements and vehicle characteristics. Next, this method tracks energy quantities at each powertrain component. Each component model is scalable such that different vehicle powertrains may be approximated. Using an 'ecorouting' process, the most ideal route is selected by weighting relative total energy consumption and travel time. Only limited powertrain characteristics are publicly available. As the future goal of this project is to apply the model to many vehicle powertrain types, the powertrain model must be reasonably accurate with minimal vehicle powertrain characteristics. Future work expands this model to constantly re-evaluate energy consumption with real-time traffic and terrain information. While ecorouting has been applied to conventional vehicles in many publications, electrified vehicles are less studied. Hybrid vehicles are particularly complicated to model due to additional components, systems, and operation modes. This methodology has been validated to represent conventional, battery electric, and parallel hybrid electric vehicles. A sensitivity study demonstrates that the model is capable of differentiating powertrains with different parameters and routes with different characteristics. / Master of Science

<p dir="ltr">Creating synthetic materials that mimic native tissue is an overarching goal in tissue engineering and regenerative medicine. It is essential to embed molecular-resolution chemical patterning into soft synthetic polymers to achieve this. Even though fundamental principles from surface science offer broad control over the position of even individual atoms on a pristine surface, this degree of control remains restricted to two-dimensional hard crystalline materials under particular environmental conditions that are incompatible with life. Therefore, developing strategies to translate these principles into soft, amorphous interfaces is challenging<i>. </i>This will lead to the development of <i>nanopatterned soft materials</i> that closely resemble native tissue. Popular approaches in materials science fail to produce such <i>high-resolution polymers</i>.</p><p dir="ltr">Hydrogels are soft, three-dimensional networks that can hold large amounts of an aqueous solvent while retaining their structure. These materials have applicability in contexts where polymer materials must interface with biology (e.g., drug delivery, biosensing, tissue engineering, and regenerative medicine) as one can easily tune their mechanical, chemical, and biological properties. However, the main limitation of these materials is that the hydrogel network is amorphous, with substantial variability in mesh size up to the micron-scale. This limits their application when highly structured interactions with biomolecules, typically at sub-10 nm scales, are required. This dissertation shows a strategy to generate 1 nm-wide ordered patterns of functional groups on polyacrylamide (PAAm) hydrogel surfaces. When 1 nm-wide linear patterns are transferred to PAAm, patterning specific biological polyelectrolyte interactions at the hydrogel surface is possible. This represents a first step towards developing robust methods for nanopattern hydrogels at the proposed resolution.</p><p dir="ltr">One last subject this thesis dissertation seeks to explore is the extension of chemical patterning to a dynamic range of scales to adapt this technological advancement to industrial setups. Enabling the practical applicability of nanopatterned soft materials in macroscopic contexts (e.g., synthetic tissue development, wearable electronics, etc). However, extending this degree of control to a high throughput process applicable to heterogeneous interfaces remains a challenge. We demonstrated a scalable inkjet printing method to produce functional hierarchical patterns on two-dimensional crystalline substrates, which can be transferred to hydrogels. Finally, we studied the specific biosensing capabilities of these micro-patterned surfaces.</p>