abstract: Android has been the dominant platform in which most of the mobile development is being done. By the end of the second quarter of 2014, 84.7 percent of the entire world mobile phones market share had been captured by Android. The Android library internally uses the modified Linux kernel as the part of its stack. The I/O scheduler, is a part of the Linux kernel, responsible for scheduling data requests to the internal and the external memory devices that are attached to the mobile systems. The usage of solid state drives in the Android tablet has also seen a rise owing to its speed of operation and mechanical stability. The I/O schedulers that exist in the present Linux kernel are not better suited for handling solid state drives in particular to exploit the inherent parallelism offered by the solid state drives. The Android provides information to the Linux kernel about the processes running in the foreground and background. Based on this information the kernel decides the process scheduling and the memory management, but no such information exists for the I/O scheduling. Research shows that the resource management could be done better if the operating system is aware of the characteristics of the requester. Thus, there is a need for a better I/O scheduler that could schedule I/O operations based on the application and also exploit the parallelism in the solid state drives. The scheduler proposed through this research does that. It contains two algorithms working in unison one focusing on the solid state drives and the other on the application awareness. The Android application context aware scheduler has the features of increasing the responsiveness of the time sensitive applications and also increases the throughput by parallel scheduling of request in the solid state drive. The suggested scheduler is tested using standard benchmarks and real-time scenarios, the results convey that our scheduler outperforms the existing default completely fair queuing scheduler of the Android. / Dissertation/Thesis / Masters Thesis Computer Science 2014

Esta tese se concentra no escalonamento de operações de entrada e saída (E/S) como uma solução para melhorar o desempenho de sistemas de arquivos paralelos, aleviando os efeitos da interferência. É usual que sistemas de computação de alto desempenho (HPC) ofereçam uma infraestrutura compartilhada de armazenamento para as aplicações. Nessa situação, em que múltiplas aplicações acessam o sistema de arquivos compartilhado de forma concorrente, os acessos das aplicações causarão interferência uns nos outros, comprometendo a eficácia de técnicas para otimização de E/S. Uma avaliação extensiva de desempenho foi conduzida, abordando cinco algoritmos de escalonamento trabalhando nos servidores de dados de um sistema de arquivos paralelo. Foram executados experimentos em diferentes plataformas e sob diferentes padrões de acesso. Os resultados indicam que os resultados obtidos pelos escalonadores são afetados pelo padrão de acesso das aplicações, já que é importante que o ganho de desempenho provido por um algoritmo de escalonamento ultrapasse o seu sobrecusto. Ao mesmo tempo, os resultados do escalonamento são afetados pelas características do subsistema local de E/S - especialmente pelos dispositivos de armazenamento. Dispositivos diferentes apresentam variados níveis de sensibilidade à sequencialidade dos acessos e ao seu tamanho, afetando o quanto técnicas de escalonamento de E/S são capazes de aumentar o desempenho. Por esses motivos, o principal objetivo desta tese é prover escalonamento de E/S com dupla adaptabilidade: às aplicações e aos dispositivos. Informações sobre o padrão de acesso das aplicações são obtidas através de arquivos de rastro, vindos de execuções anteriores. Aprendizado de máquina foi aplicado para construir um classificador capaz de identificar os aspectos espacialidade e tamanho de requisição dos padrões de acesso através de fluxos de requisições anteriores. Além disso, foi proposta uma técnica para obter eficientemente a razão entre acessos sequenciais e aleatórios para dispositivos de armazenamento, executando testes para apenas um subconjunto dos parâmetros e estimando os demais através de regressões lineares. Essas informações sobre características de aplicações e dispositivos de armazenamento são usadas para decidir a melhor escolha em algoritmo de escalonamento através de uma árvore de decisão. A abordagem proposta aumenta o desempenho em até 75% sobre uma abordagem que usa o mesmo algoritmo para todas as situações, sem adaptabilidade. Além disso, essa técnica melhora o desempenho para até 64% mais situações, e causa perdas de desempenho em até 89% menos situações. Os resultados obtidos evidenciam que ambos aspectos - aplicações e dispositivos de armazenamento - são essenciais para boas decisões de escalonamento. Adicionalmente, apesar do fato de não haver algoritmo de escalonamento capaz de prover ganhos de desempenho para todas as situações, esse trabalho mostra que através da dupla adaptabilidade é possível aplicar técnicas de escalonamento de E/S para melhorar o desempenho, evitando situações em que essas técnicas prejudicariam o desempenho. / This thesis focuses on I/O scheduling as a tool to improve I/O performance on parallel file systems by alleviating interference effects. It is usual for High Performance Computing (HPC) systems to provide a shared storage infrastructure for applications. In this situation, when multiple applications are concurrently accessing the shared parallel file system, their accesses will affect each other, compromising I/O optimization techniques’ efficacy. We have conducted an extensive performance evaluation of five scheduling algorithms at a parallel file system’s data servers. Experiments were executed on different platforms and under different access patterns. Results indicate that schedulers’ results are affected by applications’ access patterns, since it is important for the performance improvement obtained through a scheduling algorithm to surpass its overhead. At the same time, schedulers’ results are affected by the underlying I/O system characteristics - especially by storage devices. Different devices present different levels of sensitivity to accesses’ sequentiality and size, impacting on how much performance is improved through I/O scheduling. For these reasons, this thesis main objective is to provide I/O scheduling with double adaptivity: to applications and devices. We obtain information about applications’ access patterns through trace files, obtained from previous executions. We have applied machine learning to build a classifier capable of identifying access patterns’ spatiality and requests size aspects from streams of previous requests. Furthermore, we proposed an approach to efficiently obtain the sequential to random throughput ratio metric for storage devices by running benchmarks for a subset of the parameters and estimating the remaining through linear regressions. We use this information on applications’ and storage devices’ characteristics to decide the best fit in scheduling algorithm though a decision tree. Our approach improves performance by up to 75% over an approach that uses the same scheduling algorithm to all situations, without adaptability. Moreover, our approach improves performance for up to 64% more situations, and decreases performance for up to 89% less situations. Our results evidence that both aspects - applications and storage devices - are essential for making good scheduling choices. Moreover, despite the fact that there is no scheduling algorithm able to provide performance gains for all situations, we show that through double adaptivity it is possible to apply I/O scheduling techniques to improve performance, avoiding situations where it would lead to performance impairment.

Esta tese se concentra no escalonamento de operações de entrada e saída (E/S) como uma solução para melhorar o desempenho de sistemas de arquivos paralelos, aleviando os efeitos da interferência. É usual que sistemas de computação de alto desempenho (HPC) ofereçam uma infraestrutura compartilhada de armazenamento para as aplicações. Nessa situação, em que múltiplas aplicações acessam o sistema de arquivos compartilhado de forma concorrente, os acessos das aplicações causarão interferência uns nos outros, comprometendo a eficácia de técnicas para otimização de E/S. Uma avaliação extensiva de desempenho foi conduzida, abordando cinco algoritmos de escalonamento trabalhando nos servidores de dados de um sistema de arquivos paralelo. Foram executados experimentos em diferentes plataformas e sob diferentes padrões de acesso. Os resultados indicam que os resultados obtidos pelos escalonadores são afetados pelo padrão de acesso das aplicações, já que é importante que o ganho de desempenho provido por um algoritmo de escalonamento ultrapasse o seu sobrecusto. Ao mesmo tempo, os resultados do escalonamento são afetados pelas características do subsistema local de E/S - especialmente pelos dispositivos de armazenamento. Dispositivos diferentes apresentam variados níveis de sensibilidade à sequencialidade dos acessos e ao seu tamanho, afetando o quanto técnicas de escalonamento de E/S são capazes de aumentar o desempenho. Por esses motivos, o principal objetivo desta tese é prover escalonamento de E/S com dupla adaptabilidade: às aplicações e aos dispositivos. Informações sobre o padrão de acesso das aplicações são obtidas através de arquivos de rastro, vindos de execuções anteriores. Aprendizado de máquina foi aplicado para construir um classificador capaz de identificar os aspectos espacialidade e tamanho de requisição dos padrões de acesso através de fluxos de requisições anteriores. Além disso, foi proposta uma técnica para obter eficientemente a razão entre acessos sequenciais e aleatórios para dispositivos de armazenamento, executando testes para apenas um subconjunto dos parâmetros e estimando os demais através de regressões lineares. Essas informações sobre características de aplicações e dispositivos de armazenamento são usadas para decidir a melhor escolha em algoritmo de escalonamento através de uma árvore de decisão. A abordagem proposta aumenta o desempenho em até 75% sobre uma abordagem que usa o mesmo algoritmo para todas as situações, sem adaptabilidade. Além disso, essa técnica melhora o desempenho para até 64% mais situações, e causa perdas de desempenho em até 89% menos situações. Os resultados obtidos evidenciam que ambos aspectos - aplicações e dispositivos de armazenamento - são essenciais para boas decisões de escalonamento. Adicionalmente, apesar do fato de não haver algoritmo de escalonamento capaz de prover ganhos de desempenho para todas as situações, esse trabalho mostra que através da dupla adaptabilidade é possível aplicar técnicas de escalonamento de E/S para melhorar o desempenho, evitando situações em que essas técnicas prejudicariam o desempenho. / This thesis focuses on I/O scheduling as a tool to improve I/O performance on parallel file systems by alleviating interference effects. It is usual for High Performance Computing (HPC) systems to provide a shared storage infrastructure for applications. In this situation, when multiple applications are concurrently accessing the shared parallel file system, their accesses will affect each other, compromising I/O optimization techniques’ efficacy. We have conducted an extensive performance evaluation of five scheduling algorithms at a parallel file system’s data servers. Experiments were executed on different platforms and under different access patterns. Results indicate that schedulers’ results are affected by applications’ access patterns, since it is important for the performance improvement obtained through a scheduling algorithm to surpass its overhead. At the same time, schedulers’ results are affected by the underlying I/O system characteristics - especially by storage devices. Different devices present different levels of sensitivity to accesses’ sequentiality and size, impacting on how much performance is improved through I/O scheduling. For these reasons, this thesis main objective is to provide I/O scheduling with double adaptivity: to applications and devices. We obtain information about applications’ access patterns through trace files, obtained from previous executions. We have applied machine learning to build a classifier capable of identifying access patterns’ spatiality and requests size aspects from streams of previous requests. Furthermore, we proposed an approach to efficiently obtain the sequential to random throughput ratio metric for storage devices by running benchmarks for a subset of the parameters and estimating the remaining through linear regressions. We use this information on applications’ and storage devices’ characteristics to decide the best fit in scheduling algorithm though a decision tree. Our approach improves performance by up to 75% over an approach that uses the same scheduling algorithm to all situations, without adaptability. Moreover, our approach improves performance for up to 64% more situations, and decreases performance for up to 89% less situations. Our results evidence that both aspects - applications and storage devices - are essential for making good scheduling choices. Moreover, despite the fact that there is no scheduling algorithm able to provide performance gains for all situations, we show that through double adaptivity it is possible to apply I/O scheduling techniques to improve performance, avoiding situations where it would lead to performance impairment.

Advances in hearing instrument technology have permitted the development of non-linear prescriptive methods to prescribe amplification characteristics for the hearing- impaired individual. The dispenser’s task in selecting the most appropriate prescriptive procedure for the young child is of utmost importance to ensure optimum hearing aid benefit for communication development. It was the aim of this study to compare and describe the effect of the two most widely used methods, DSL (i/o) and NAL-NL1, on speech recognition and loudness perception. An exploratory, descriptive research design was selected to realise this goal. Ten participants were selected using a convenient non-probability method of sampling. Articulation index calculations and a closed set speech recognition test were utilised in the evaluation of speech recognition, whereas functional gain results and loudness rating measurements provided an opportunity to describe loudness perception. The obtained results were analysed using the SAS (Statistical Analysis System). The study concluded that, although significant statistical differences existed in loudness perception, no statistical difference was observed in actual speech recognition measures. This effect may contribute to the individual amplification approaches of the two methods, which seem to reflect the uncertainties expressed by researchers as to the contribution of high frequency amplification to speech recognition in young children. / Dissertation (M (Communication Pathology))--University of Pretoria, 2006. / Speech-Language Pathology and Audiology / Unrestricted

Computing systems are becoming increasingly data-intensive because of the explosion of data and the needs for processing the data, and storage management is critical to application performance in such data-intensive computing systems. However, existing resource management frameworks in these systems lack the support for storage management, which causes unpredictable performance degradations when applications are under I/O contention. Storage management of data-intensive systems is a challenging problem because I/O resources cannot be easily partitioned and distributed storage systems require scalable management. This dissertation presents the solutions to address these challenges for typical data-intensive systems including high-performance computing (HPC) systems and big-data systems. For HPC systems, the dissertation presents vPFS, a performance virtualization layer for parallel file system (PFS) based storage systems. It employs user-level PFS proxies to interpose and schedule parallel I/Os on a per-application basis. Based on this framework, it enables SFQ(D)+, a new proportional-share scheduling algorithm which allows diverse applications with good performance isolation and resource utilization. To manage an HPC system’s total I/O service, it also provides two complementary synchronization schemes to coordinate the scheduling of large numbers of storage nodes in a scalable manner. For big-data systems, the dissertation presents IBIS, an interposition-based big-data I/O scheduler. By interposing the different I/O phases of big-data applications, it schedules the I/Os transparently to the applications. It enables a new proportional-share scheduling algorithm, SFQ(D2), to address the dynamics of the underlying storage by adaptively adjusting the I/O concurrency. Moreover, it employs a scalable broker to coordinate the distributed I/O schedulers and provide proportional sharing of a big-data system’s total I/O service. Experimental evaluations show that these solutions have low-overhead and provide strong I/O performance isolation. For example, vPFS’ overhead is less than 3% in through- put and it delivers proportional sharing within 96% of the target for diverse workloads; and IBIS provides up to 99% better performance isolation for WordCount and 30% better proportional slowdown for TeraSort and TeraGen than native YARN.

Real games har utformat programvaran HOME I/O i realtid där ett 3D-hus har allt som behövs för att simulera och övervaka ett smart hem.Syftet med det här examensarbetet är att analyser och utvärdera simuleringsprogramvaran. Målen är att använda en extern PLC och en SoftPLC, sista målet är att ta fram laborationsinstruktion förutbildningsändamål i automationsingenjörsprogrammet.Laborationsinstruktionen innehåller tre delar, där användning av simuleringsverktyget och det med följande programmet Connect I/O.Den första delen är att se hur en SoftPLC kan användas med simuleringsverktyget. I den andra delen skall en extern PLC kopplas till simuleringsverktyget där väsentliga delar av TIA Portals tas upp för Siemens S7-1200 och sista delen skall flera scenario kopplas upp.

This thesis deals with an implementation of non-blocking I/O interface for the k-Wave project, which is designed for time-domain simulation of ultrasound propagation. Main focus is on large domain simulations that, due to high computing power requirements, must run on supercomputers and produce tens of GB of data in a single simulation step. In this thesis, I have designed and implemented a non-blocking interface for storing data using dedicated threads, which allows to overlap simulation calculations with disk operations in order to speed up the simulation. An acceleration of up to 33% was achieved compared to the current implementation of project k-Wave, which resulted, among other things, also to reduce cost of the simulation.

This thesis presents the I/O layer of Linux kernel and shows various tools for tuning and optimization of its performance. Many tools are presented and their usage and outputs are studied. The thesis then focuses on the means of combining such tools to create more applicable methodology of system analysis and monitoring. The practical part consists of applying SystemTap scripts for blktrace subsystem and creating a fragmentation monitoring tool with graphical output.

Space-based computing applications often demand reliable, high-bandwidth communication systems. FPGAs with Mulit-Gigabit Transceivers (MGTs) provide an effective platform for such systems, but it is important that system designers understand the possible susceptibilities MGTs present to the system. Previous work has provided a foundation for understanding the susceptibility of raw FPGA MGTs but has fallen short of testing MGTs as part of a larger system. This work focuses on answering the questions MGT system designers need to know in order to build a reliable space-based MGT system. Two radiation tests were performed with a test architecture built on the Aurora protocol. These tests were specifically designed to discover system susceptibilities, and effective mechanisms for upset detection, recovery, and recovery detection. Test results reveal that the Aurora protocol serves as an effective basis for simple point-to-point communication for space-based systems but that some additional logic is necessary for high reliability. Particularly, additional upset detection and recovery mechanisms are necessary as well as additional status indicators. These additions are minimal, however, and not all are necessary depending on system requirements. The most susceptible part of the MGT system is the MGT tile components on the RX data path. Upsets to these components most often results in data corruption only and do not affect system operation or disrupt the communication link. Most other upsets which do disrupt normal system operation can be recovered automatically by the Aurora protocol with built-in mechanisms. Only 1% of observed events in testing required additional recovery mechanisms not supplied by Aurora. In addition to test data results, this work also provides suggestions for system designers based on various system requirements and a proposed MGT system design based on the Aurora protocol. The proposed system serves as an example to illustrate how test data can be used to guide the system design and determine system availability. With this knowledge designers are able to build reliable MGT systems for a variety of space-based systems.

This work has extended an existing Aurora protocol for high-speed serial I/O between FPGAs to provide greater fault recovery in the presence of high-energy radiation. To improve on the Aurora protocol, additional resets that affect larger portions of the system were used. Detection for additional error modes that occurred but were not detected by the Aurora protocol was designed. Radiation testing was performed on the Aurora protocol with the additional mitigation hardware. The test gathered large amounts of data on the various error modes of the Aurora protocol and how the additional mitigation circuitry affected the system. The test results showed that the addition of the recovery circuitry greatly enhanced the Aurora protocol's ability to recover from errors. The recovery circuit recovered from all but 0.01% of errors that the Aurora protocol could not. The recovery circuit further increased the availability of the transmission link by proactively applying resets at much shorter intervals than used in previous testing. This quick recovery caused the recovery mechanism to fix some errors that may have recovered automatically with enough time. However, the system still showed an increase in performance, and unrecoverable errors were reduced 100x. The estimated unrecoverable error rate of the system is 5.9E-07 in geosynchronous orbit. The bit error rate of the enhanced system was 8.47754E-015, an order of magnitude improvement.