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<p> Hybrid task-lists are sets of periodic and asynchronous processes. To verifiably schedule hybrid tasks-lists with hard and soft real-time requirements, Xu and Lam proposed Integrated Pre-Run-Time scheduling (IPRTS) [13], a two phase method that first makes use of pre-run-time scheduling techniques, converting some asynchronous tasks with hard deadlines to periodic tasks and reserving processor capacity for the remaining hard deadline asynchronous tasks. These remaining asynchronous tasks are scheduled by a novel run-time scheduler that enforces arbitrary exclusion relations between any combination of periodic and asynchronous processes. The technique has two significant drawbacks: (i) a custom run-time scheduler is required that is not available on existing Real-Time Operating Systems (RTOS) and (ii) in many circumstances the reservation of processor capacity is overly pessimistic, causing the failure of the method for many simple task lists. To overcome these drawbacks, this thesis narrows the set of task-lists considered to those where the asynchronous tasks exclude periodic tasks and periodic processes do not exclude asynchronous tasks. A high priority polling server is then used to handle all hard asynchronous tasks. In cases where the method succeeds, it is easily implementable on any RTOS that has priority based scheduling with phased release times, and inherits the error handling and soft real-time process scheduling capabilities of the RTOS. A set of software tools which partially automates the technique, including an open source implementation of the Xu-Parnas pre-run-time scheduling algorithm [14], has been developed and applied to the examples in the thesis.</p> / Thesis / Master of Applied Science (MASc)

Time series anomaly detection can prove to be a very useful tool to inspect and maintain the health and quality of an infrastructure system. While tackling such a problem, the main concern lies in the imbalanced nature of the dataset. In order to mitigate this problem, this thesis proposes two unsupervised anomaly detection frameworks. The first one is an architecture which leverages the concept of matrix profile which essentially refers to a data structure containing the euclidean scores of the subsequences of two time series that is obtained through a similarity join.It is an architecture comprising of a data fusion technique coupled with using matrix profile analysis under the constraints of varied sampling rate for different time series. To this end, we have proposed a framework, through which a time series that is being evaluated for anomalies is quantitatively compared with a benchmark (anomaly-free) time series using the proposed asynchronous time series comparison that was inspired by matrix profile approach for anomaly detection on time series . In order to evaluate the efficacy of this framework, it was tested on a case study comprising of a Class I Rail road dataset. The data collection system integrated into this railway system collects data through different data acquisition channels which represent different transducers. This framework was applied to all the channels and the best performing channels were identified. The average Recall and Precision achieved on the single channel evaluation through this framework was 93.5% and 55% respectively with an error threshold of 0.04 miles or 211 feet. A limitation that was noticed in this framework was that there were some false positive predictions. In order to overcome this problem, a second framework has been proposed which incorporates the idea of extracting signature patterns in a time series also known as motifs which can be leveraged to identify anomalous patterns. This second framework proposed is a motif based framework which operates under the same constraints of a varied sampling rate. Here, a feature extraction method and a clustering method was used in the training process of a One Class Support Vector Machine (OCSVM) coupled with a Kernel Density Estimation (KDE) technique. The average Recall and Precision achieved on the same case study through this frame work was 74% and 57%. In comparison to the first, the second framework does not perform as well. There will be future efforts focused on improving this classification-based anomaly detection method / Master of Science / Time series anomaly detection refers to the identification of any outliers or deviations present in a time series data. This technique could prove to be useful to mitigate any unplanned events by facilitating early maintenance. The first method proposed involves comparing an anomaly-free dataset with the time series of interest. The difference between these two time series are noted and the point with the highest difference will be considered to be an anomaly. The performance of this model was evaluated on a Rail road dataset and the cumuluative average Recall (how useful the predictions are) and average Precison (how accurate the predictions are) 93.5% and 55% respectively with an acceptable error range of 0.04 miles or 211 feet. The second method proposed involves extracting all segments in the anomaly-free dataset and grouping them according to their similarity. Here, a OCSVM is used to train these individual groups. OCSVM is a machine learning algorithm which learns to classify a data as either anomalous or normal. It is then coupled with the KDE which creates a distribution across all the anomalies and identifies the anomaly as one with a high distribution of predictions.The performance of this model was evaluated on a Rail road dataset and the cumulative average Recall and cumulative average Precision 74% and 57% respectively with an acceptable error range of 0.04 miles or 211 feet.

The components of high-performance systems continue to become more complex on the road to exascale. This complexity is exposed at the level of: multi/many-core CPUs, accelerators (GPUs), interconnects (horizontal communication), and memory hierarchies (vertical communication). A crucial task is designing an algorithm and a programming model that scale to the same order of the HPC system size at multiple levels. This trend in HPC architecture more critically affects memory-intensive appli- cations than compute-bound applications. Accomplishing this task involves adopting less synchronous forms of the mathematical algorithm, reducing synchronization in the computational implementation, introducing more SIMT-style concurrency at the finest level of system hierarchy, and increasing arithmetic intensity as the bottleneck shifts from number of floating-point operations to number of memory accesses. This dissertation addresses these challenges in scientific simulation focusing in the dominant kernels of a memory-bound application: sparse solvers in implicit model- ing, and I/O in explicit reverse time migration in seismic imaging. We introduce asynchronous task-based parallelism into iterative algebraic preconditioners. We also introduce a task-based framework that hides the latency of I/O with computation. This dissertation targets two main applications in the oil and gas industry: reservoir simulation and seismic imaging simulation. It presents results on multi- and many- core systems and GPUs on four Top500 supercomputers: Summit, TSUBAME 3.0, Shaheen II, and Makman-2. We introduce an asynchronous implementation of four major memory-bound kernels: Algebraic multigrid (MPI+OmpSs), tridiagonal solve (MPI+OpenMP), Additive Schwarz Preconditioned Inexact Newton (MPI+MPI), and Reverse Time Migration (StarPU/StarPU+MPI and CUDA).

Online education is popular from a consumer perspective, but there are elements of face-to-face instruction and assessment that are difficult to reproduce online (Bassoppo-Moyo 2006). The difficulty of reproducing valued elements of a face-to-face setting leads to concerns regarding the overall quality of the online learning experience. Videoconferencing is one technology that has been used to incorporate elements of a face-to-face environment. However, videoconferencing over the Internet is fraught with technical difficulties and live discussions remove one of the main benefits of distance education: time flexibility. A more recent development has been to use asynchronous video as a communications method in online courses. Griffiths and Graham (2009) described several pilots using asynchronous video in online courses at Brigham Young University. Asynchronous video conveys the verbal and nonverbal signals necessary for immediacy and social presence and retains the time flexibility benefit of distance education. Following the pilot studies, a prototype design theory titled the Asynchronous Video Learning Model (AVLM) was created for the use of asynchronous video in online courses. A study was designed to study a practical implementation of AVLM. The major purpose of the study was to observe and analyze the practical experiences of participants and improve the AVLM model. A class named IPT286 (Using Instructional Technology in Teaching) taught by the department of IP&T at BYU was redesigned to be an online class using AVLM. Data were gathered during the semester and then analyzed according to the methods described in this study. Results showed that many of the principles of the AVLM model were successfully implemented and led to positive experiences. Some elements of the model were not adequately implemented which led to some negative experiences. In addition, experiences led to new elements being added to the model. The study also revealed some interesting principles related to general learning theory. The data consistently revealed the importance of relationships in the learning process. Relationships between students and the instructor were shown to influence the student learning experience, and therefore the personality and style of the instructor impacted overall student learning to some degree.

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International Telemetering Conference Proceedings / October 25-28, 1993 / Riviera Hotel and Convention Center, Las Vegas, Nevada / To deliver high bandwidth, a ubiquitous inter-/intra-building cable plant consisting of single mode and multimode fiber as well as twisted pair copper is required. The selection of the "glue" to transport and interconnect distributed LANs with central facility resources over a pervasive cable plant is the focus of this paper. A description of the traditional problems that must be overcome to provide very high bandwidth beyond the narrow confines of a computer center is given. The applicability of Asynchronous Transfer Mode (ATM) switching (interconnection) and Synchronous Optical NETwork (SONET) (transport) for high bandwidth delivery is described using the environment and requirements of Sandia National Laboratories. Other methods for distributing high data rates are compared and contrasted. Sandia is implementing a standards based foundation utilizing a pervasive single mode fiber cable plant, SONET transport, and ATM switching to meet the goals of gigabit networking.

International Telemetering Conference Proceedings / October 26-29, 1998 / Town & Country Resort Hotel and Convention Center, San Diego, California / As aircraft become more complex and require more resources over larger areas, the challenge of the test ranges is to provide economical solutions to move telemetry data from the test article to the data processing facility. Edwards AFB is in the process of upgrading the ground transmission facilities to transport data including telemetry using Asynchronous Transfer Mode (ATM). This paper documents the challenge of supporting telemetry over ATM, different approaches that are available, the benefits of using ATM, and discussion of candidate hardware options. The effort at Edwards include the linking of the major range facilities over a fiber optic backbone and links to other major test ranges in the Southwest Range Complex via microwave. The fiber optic backbone is expected to be OC-12c (622 Mbps) ATM supporting new capabilities as well as all of the legacy systems. The backbone system will be designed so that migration to OC-48 is possible without service disruption. The microwave links are multiple DS-3 capable. Some of these DS-3s may support legacy systems, but the ability to link ranges using ATM is expected simultaneously.

International Telemetering Conference Proceedings / October 27-30, 1997 / Riviera Hotel and Convention Center, Las Vegas, Nevada / In recent years we have seen an increase in the use of MIL-STD-1553 buses and other asynchronous data sources used in new missile and launcher designs. The application of multiplexed asynchronous buses in missiles and launchers is very common today. With increasing application of asynchronous data sources into very complex systems the need to acquire, analyze, and present one hundred percent of the bus traffic in real time or near real time has become especially important during testing and diagnostic operations. This paper discusses ways of converting asynchronous data, including MIL-STD-1553, into a telemetry format that is suitable for encryption, telemetering, recording, and presenting with Inter Range Instrumentation Group (IRIG) compatible off-the-shelf hardware. The importance of these designs is to provide the capability to conserve data bandwidth and to maximize the use of existing hardware. In addition, this paper will discuss a unique decode and time tagging design that conserves data storage when compared to the methods in IRIG Standard 106-96 and still maintains a very accurate time tag.

International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada / As the flight test community moves into the 21st century, the ever increasing demand for higher telemetry data rates and the need to transport additional data types is becoming the challenge of every flight test range. The evolution of the flight test range has grown from low telemetry data rates and a few 2400 baud tracking sources into high-speed telemetry, GPS based tracking, networking, digital video, and more. Recognizing the need to change the way data is managed has resulted in an effort to redefine the work centers at the Air Force Flight Test Center (AFFTC) at Edwards AFB. The Technical Control Center (TCC) within the Ridley Mission Control Center at Edwards AFB is currently being relocated with the intent of achieving tomorrow’s vision, while supporting the missions of today. One major goal of this redefinition is the elimination of as much analog transmission equipment as possible in favor of digital transmission. The new digital range requires management of data and allocation of that management in different ways than the past. Moving to an all-digital range has advantages that are just now being realized. This paper outlines the current and future design, configuration, maintenance, and operation of the TCC and touches on how some of the other range functions are impacted. In addition, the challenges and benefits of implementing the next generation in range communications will be discussed.