Scalable Model-Based Management of Massive High Frequency Wind Turbine Data with ModelarDB

Modern wind turbines are monitored by sensors that generate massive amounts of time series data that is ingested on the edge before it is transferred to the cloud where it is stored and queried. This results in at least four challenges: 1) High frequency time series data must be ingested on limited hardware fast enough to keep up with the generation; 2) Limited bandwidth makes it impossible to transfer the data without compression; 3) High storage costs when data is stored; and 4) Low data quality to unbounded lossy compression methods commonly used by practitioners. Practitioners currently use solutions that only solve some of these challenges. In this paper, we evaluate a solution for the entire pipeline based on the Time Series Management System ModelarDB that addresses all four challenges efficiently. With ModelarDB, the user can exploit both lossless and error-bounded lossy compression. We evaluate the solution in a realistic edge-to-cloud scenario with real-world data under different aspects. For lossless compression, ModelarDB achieves up to 1.5x better compression and 1.2x better transfer efficiency than lossless solutions commonly used by practitioners. For lossy compression, ModelarDB offers significant compression comparable to a lossy method commonly used by practitioners today. However, ModelarDB has orders of magnitude smaller errors

A framework for mobile SOA using compression

The widely accepted standards of Service-Oriented Architecture (SOA) have changed the way many organisations conduct their everyday business. The significant popularity of mobile devices has seen a rapid increase in the rate of mobile technology enhancements, which have become widely used for communication, as well as conducting everyday tasks. An increased requirement in many businesses is for staff not to be tied down to the office. Consequently, mobile devices play an important role in achieving the mobility and information access that people desire. Due to the popularity and increasing use of SOA and mobile devices, Mobile Service-Oriented Architecture (Mobile SOA) has become a new industry catch-phrase. Many challenges, however, exist within the Mobile SOA environment. These issues include limitations on mobile devices, such as a reduced screen size, lack of processing power, insufficient processing memory, limited battery life, poor storage capacity, unreliable network connections, limited bandwidth available and high transfer costs. This research aimed to provide an elegant solution to the issues of a mobile device, which hinders the performance of Mobile SOA. The main objective of this research was to improve the effectiveness and efficiency of Mobile SOA. In order to achieve this goal, a framework was proposed, which supported intelligent compression of files used within a Web Service. The proposed framework provided a set of guidelines that facilitate the quick development of a system. A proof-of-concept prototype was developed, based on these guidelines and the framework design principles. The prototype provided practical evidence of the effectiveness of implementing a system based on the proposed framework. An analytical evaluation was conducted to determine the effectiveness of the prototype within the Mobile SOA environment. A performance evaluation was conducted to determine efficiency it provides. Additionally, the performance evaluation highlighted the decrease in file transfer time, as well as the significant reduction in transfer costs. The analytical and performance evaluations demonstrated that the prototype optimises the effectiveness and efficiency of Mobile SOA. The framework could, thus, be used to facilitate efficient file transfer between a Server and (Mobile) Client

Interstellar: Using Halide's Scheduling Language to Analyze DNN Accelerators

We show that DNN accelerator micro-architectures and their program mappings represent specific choices of loop order and hardware parallelism for computing the seven nested loops of DNNs, which enables us to create a formal taxonomy of all existing dense DNN accelerators. Surprisingly, the loop transformations needed to create these hardware variants can be precisely and concisely represented by Halide's scheduling language. By modifying the Halide compiler to generate hardware, we create a system that can fairly compare these prior accelerators. As long as proper loop blocking schemes are used, and the hardware can support mapping replicated loops, many different hardware dataflows yield similar energy efficiency with good performance. This is because the loop blocking can ensure that most data references stay on-chip with good locality and the processing units have high resource utilization. How resources are allocated, especially in the memory system, has a large impact on energy and performance. By optimizing hardware resource allocation while keeping throughput constant, we achieve up to 4.2X energy improvement for Convolutional Neural Networks (CNNs), 1.6X and 1.8X improvement for Long Short-Term Memories (LSTMs) and multi-layer perceptrons (MLPs), respectively.Comment: Published as a conference paper at ASPLOS 202

Advanced Protocols for Peer-to-Peer Data Transmission in Wireless Gigabit Networks

This thesis tackles problems on IEEE 802.11 MAC layer, network layer and application layer, to further push the performance of wireless P2P applications in a holistic way. It contributes to the better understanding and utilization of two major IEEE 802.11 MAC features, frame aggregation and block acknowledgement, to the design and implementation of opportunistic networks on off-the-shelf hardware and proposes a document exchange protocol, including document recommendation. First, this thesis contributes a measurement study of the A-MPDU frame aggregation behavior of IEEE 802.11n in a real-world, multi-hop, indoor mesh testbed. Furthermore, this thesis presents MPDU payload adaptation (MPA) to utilize A-MPDU subframes to increase the overall throughput under bad channel conditions. MPA adapts the size of MAC protocol data units to channel conditions, to increase the throughput and lower the delay in error-prone channels. The results suggest that under erroneous conditions throughput can be maximized by limiting the MPDU size. As second major contribution, this thesis introduces Neighborhood-aware OPPortunistic networking on Smartphones (NOPPoS). NOPPoS creates an opportunistic, pocket-switched network using current generation, off-the-shelf mobile devices. As main novel feature, NOPPoS is highly responsive to node mobility due to periodic, low-energy scans of its environment, using Bluetooth Low Energy advertisements. The last major contribution is the Neighborhood Document Sharing (NDS) protocol. NDS enables users to discover and retrieve arbitrary documents shared by other users in their proximity, i.e. in the communication range of their IEEE 802.11 interface. However, IEEE 802.11 connections are only used on-demand during file transfers and indexing of files in the proximity of the user. Simulations show that NDS interconnects over 90 \% of all devices in communication range. Finally, NDS is extended by the content recommendation system User Preference-based Probability Spreading (UPPS), a graph-based approach. It integrates user-item scoring into a graph-based tag-aware item recommender system. UPPS utilizes novel formulas for affinity and similarity scoring, taking into account user-item preference in the mass diffusion of the recommender system. The presented results show that UPPS is a significant improvement to previous approaches

Connectivity and Data Transmission over Wireless Mobile Systems

We live in a world where wireless connectivity is pervasive and becomes ubiquitous. Numerous devices with varying capabilities and multiple interfaces are surrounding us. Most home users use Wi-Fi routers, whereas a large portion of human inhabited land is covered by cellular networks. As the number of these devices, and the services they provide, increase, our needs in bandwidth and interoperability are also augmented. Although deploying additional infrastructure and future protocols may alleviate these problems, efficient use of the available resources is important. We are interested in the problem of identifying the properties of a system able to operate using multiple interfaces, take advantage of user locations, identify the users that should be involved in the routing, and setup a mechanism for information dissemination. The challenges we need to overcome arise from network complexity and heterogeneousness, as well as the fact that they have no single owner or manager. In this thesis I focus on two cases, namely that of utilizing "in-situ" WiFi Access Points to enhance the connections of mobile users, and that of establishing "Virtual Access Points" in locations where there is no fixed roadside equipment available. Both environments have attracted interest for numerous related works. In the first case the main effort is to take advantage of the available bandwidth, while in the second to provide delay tolerant connectivity, possibly in the face of disasters. Our main contribution is to utilize a database to store user locations in the system, and to provide ways to use that information to improve system effectiveness. This feature allows our system to remain effective in specific scenarios and tests, where other approaches fail

The Proceedings of 14th Australian Digital Forensics Conference, 5-6 December 2016, Edith Cowan University, Perth, Australia

Conference Foreword This is the fifth year that the Australian Digital Forensics Conference has been held under the banner of the Security Research Institute, which is in part due to the success of the security conference program at ECU. As with previous years, the conference continues to see a quality papers with a number from local and international authors. 11 papers were submitted and following a double blind peer review process, 8 were accepted for final presentation and publication. Conferences such as these are simply not possible without willing volunteers who follow through with the commitment they have initially made, and I would like to take this opportunity to thank the conference committee for their tireless efforts in this regard. These efforts have included but not been limited to the reviewing and editing of the conference papers, and helping with the planning, organisation and execution of the conference. Particular thanks go to those international reviewers who took the time to review papers for the conference, irrespective of the fact that they are unable to attend this year. To our sponsors and supporters a vote of thanks for both the financial and moral support provided to the conference. Finally, to the student volunteers and staff of the ECU Security Research Institute, your efforts as always are appreciated and invaluable. Yours sincerely, Conference Chair Professor Craig Valli Director, Security Research Institut

Novel high performance techniques for high definition computer aided tomography

Mención Internacional en el título de doctorMedical image processing is an interdisciplinary field in which multiple research areas are involved: image acquisition, scanner design, image reconstruction algorithms, visualization, etc. X-Ray Computed Tomography (CT) is a medical imaging modality based on the attenuation suffered by the X-rays as they pass through the body. Intrinsic differences in attenuation properties of bone, air, and soft tissue result in high-contrast images of anatomical structures. The main objective of CT is to obtain tomographic images from radiographs acquired using X-Ray scanners. The process of building a 3D image or volume from the 2D radiographs is known as reconstruction. One of the latest trends in CT is the reduction of the radiation dose delivered to patients through the decrease of the amount of acquired data. This reduction results in artefacts in the final images if conventional reconstruction methods are used, making it advisable to employ iterative reconstruction algorithms. There are numerous reconstruction algorithms available, from which we can highlight two specific types: traditional algorithms, which are fast but do not enable the obtaining of high quality images in situations of limited data; and iterative algorithms, slower but more reliable when traditional methods do not reach the quality standard requirements. One of the priorities of reconstruction is the obtaining of the final images in near real time, in order to reduce the time spent in diagnosis. To accomplish this objective, new high performance techniques and methods for accelerating these types of algorithms are needed. This thesis addresses the challenges of both traditional and iterative reconstruction algorithms, regarding acceleration and image quality. One common approach for accelerating these algorithms is the usage of shared-memory and heterogeneous architectures. In this thesis, we propose a novel simulation/reconstruction framework, namely FUX-Sim. This framework follows the hypothesis that the development of new flexible X-ray systems can benefit from computer simulations, which may also enable performance to be checked before expensive real systems are implemented. Its modular design abstracts the complexities of programming for accelerated devices to facilitate the development and evaluation of the different configurations and geometries available. In order to obtain near real execution times, low-level optimizations for the main components of the framework are provided for Graphics Processing Unit (GPU) architectures. Other alternative tackled in this thesis is the acceleration of iterative reconstruction algorithms by using distributed memory architectures. We present a novel architecture that unifies the two most important computing paradigms for scientific computing nowadays: High Performance Computing (HPC). The proposed architecture combines Big Data frameworks with the advantages of accelerated computing. The proposed methods presented in this thesis provide more flexible scanner configurations as they offer an accelerated solution. Regarding performance, our approach is as competitive as the solutions found in the literature. Additionally, we demonstrate that our solution scales with the size of the problem, enabling the reconstruction of high resolution images.El procesamiento de imágenes médicas es un campo interdisciplinario en el que participan múltiples áreas de investigación como la adquisición de imágenes, diseño de escáneres, algoritmos de reconstrucción de imágenes, visualización, etc. La tomografía computarizada (TC) de rayos X es una modalidad de imágen médica basada en el cálculo de la atenuación sufrida por los rayos X a medida que pasan por el cuerpo a escanear. Las diferencias intrínsecas en la atenuación de hueso, aire y tejido blando dan como resultado imágenes de alto contraste de estas estructuras anatómicas. El objetivo principal de la TC es obtener imágenes tomográficas a partir estas radiografías obtenidas mediante escáneres de rayos X. El proceso de construir una imagen o volumen en 3D a partir de las radiografías 2D se conoce como reconstrucción. Una de las últimas tendencias en la tomografía computarizada es la reducción de la dosis de radiación administrada a los pacientes a través de la reducción de la cantidad de datos adquiridos. Esta reducción da como resultado artefactos en las imágenes finales si se utilizan métodos de reconstrucción convencionales, por lo que es aconsejable emplear algoritmos de reconstrucción iterativos. Existen numerosos algoritmos de reconstrucción disponibles a partir de los cuales podemos destacar dos categorías: algoritmos tradicionales, rápidos pero no permiten obtener imágenes de alta calidad en situaciones en las que los datos son limitados; y algoritmos iterativos, más lentos pero más estables en situaciones donde los métodos tradicionales no alcanzan los requisitos en cuanto a la calidad de la imagen. Una de las prioridades de la reconstrucción es la obtención de las imágenes finales en tiempo casi real, con el fin de reducir el tiempo de diagnóstico. Para lograr este objetivo, se necesitan nuevas técnicas y métodos de alto rendimiento para acelerar estos algoritmos. Esta tesis aborda los desafíos de los algoritmos de reconstrucción tradicionales e iterativos, con respecto a la aceleración y la calidad de imagen. Un enfoque común para acelerar estos algoritmos es el uso de arquitecturas de memoria compartida y heterogéneas. En esta tesis, proponemos un nuevo sistema de simulación/reconstrucción, llamado FUX-Sim. Este sistema se construye alrededor de la hipótesis de que el desarrollo de nuevos sistemas de rayos X flexibles puede beneficiarse de las simulaciones por computador, en los que también se puede realizar un control del rendimiento de los nuevos sistemas a desarrollar antes de su implementación física. Su diseño modular abstrae las complejidades de la programación para aceleradores con el objetivo de facilitar el desarrollo y la evaluación de las diferentes configuraciones y geometrías disponibles. Para obtener ejecuciones en casi tiempo real, se proporcionan optimizaciones de bajo nivel para los componentes principales del sistema en las arquitecturas GPU. Otra alternativa abordada en esta tesis es la aceleración de los algoritmos de reconstrucción iterativa mediante el uso de arquitecturas de memoria distribuidas. Presentamos una arquitectura novedosa que unifica los dos paradigmas informáticos más importantes en la actualidad: computación de alto rendimiento (HPC) y Big Data. La arquitectura propuesta combina sistemas Big Data con las ventajas de los dispositivos aceleradores. Los métodos propuestos presentados en esta tesis proporcionan configuraciones de escáner más flexibles y ofrecen una solución acelerada. En cuanto al rendimiento, nuestro enfoque es tan competitivo como las soluciones encontradas en la literatura. Además, demostramos que nuestra solución escala con el tamaño del problema, lo que permite la reconstrucción de imágenes de alta resolución.This work has been mainly funded thanks to a FPU fellowship (FPU14/03875) from the Spanish Ministry of Education. It has also been partially supported by other grants: • DPI2016-79075-R. “Nuevos escenarios de tomografía por rayos X”, from the Spanish Ministry of Economy and Competitiveness. • TIN2016-79637-P Towards unification of HPC and Big Data Paradigms from the Spanish Ministry of Economy and Competitiveness. • Short-term scientific missions (STSM) grant from NESUS COST Action IC1305. • TIN2013-41350-P, Scalable Data Management Techniques for High-End Computing Systems from the Spanish Ministry of Economy and Competitiveness. • RTC-2014-3028-1 NECRA Nuevos escenarios clinicos con radiología avanzada from the Spanish Ministry of Economy and Competitiveness.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: José Daniel García Sánchez.- Secretario: Katzlin Olcoz Herrero.- Vocal: Domenico Tali