The deployment of wireless communications coupled with the popularity of portable devices has led to significant research in the area of mobile data caching. Prior research has focused on the development of solutions that allow applications to run in wireless environments using proxy based techniques. Most of these approaches are semantic based and do not provide adequate support for representing the context of a user (i.e., the interpreted human intention.). Although the context may be treated implicitly it is still crucial to data management. In order to address this challenge this dissertation focuses on two characteristics: how to predict (i) the future location of the user and (ii) locations of the fetched data where the queried data item has valid answers. Using this approach, more complete information about the dynamics of an application environment is maintained. The contribution of this dissertation is a novel data caching mechanism for pervasive computing environments that can adapt dynamically to a mobile user's context. In this dissertation, we design and develop a conceptual model and context aware protocols for wireless data caching management. Our replacement policy uses the validity of the data fetched from the server and the neighboring locations to decide which of the cache entries is less likely to be needed in the future, and therefore a good candidate for eviction when cache space is needed. The context aware driven prefetching algorithm exploits the query context to effectively guide the prefetching process. The query context is defined using a mobile user's movement pattern and requested information context. Numerical results and simulations show that the proposed prefetching and replacement policies significantly outperform conventional ones. Anticipated applications of these solutions include biomedical engineering, telehealth, medical information systems and business.

There is a growing need to assess and issue micro-credentials within STEM curricula. Although one approach is to insert a free-standing academic activity into the students learning and degree path, herein the development and mechanism of an alternative approach rooted in leveraging responses on digitized quiz-based assessments is developed. An online assessment and remediation protocol with accompanying Python-based toolset was developed to engage undergraduate tutors who identify and fill knowledge gaps of at-risk learners. Digitized assessments, personalized tutoring, and automated micro-credentialing scripts for Canvas LMS are used to issue skill-specific badges which motivate the learner incrementally, while increasing self-efficacy. This consisted of building upon the available Canvas LMS application programming interface to design an algorithm that takes the given Canvas LMS data to develop the automation of dispersing badges. In addition, a user centric interface was prototyped and implemented to garner high user acceptance. As well as pioneering the potential steps to efficiently migrating the classical quizzes to New Quizzes format and investigating potential steps to provide personalized YouTube video recommendations to students, based on assessment performance. Moreover, foundational research, operational objectives, and prototyping a user interface for instructor-facing micro-credentialing was established through the work represented in this document. The approach developed is shown to provide a fine-grained analysis that credentials students understanding of material from a semester-wide perspective using a scalable automation approach evaluated within the Canvas LMS.

Over the recent years, deep learning has risen in popularity due to its capabilities in learning from data and extracting features from it in an automatic manner during training. This automatic feature extraction can be a useful tool in domains which require subject-matter-experts to manually or algorithmically extract features from the data, such as in the biomedical domain. However, automatic feature extraction requires a large amount of data, which in turn makes deep learning models data-hungry. This is a challenge for adoption of deep learning to these domains which often have small amounts of training data. In this work, deep learning is implemented in the biomedical and expert-based domains in a practical manner. Through selective transfer learning, learned knowledge from other related or unrelated datasets and tasks are transferred to the target domain, alleviating the problem of low training data. Transfer learning is studied as pre-trained model transfer or off-the-shelf feature extractor transfer in expert-based domains such as drug discovery, electrocardiogram signal arrhythmia detection, and biometric recognition. The results demonstrate that deep learning's automatic feature extraction out-performs traditional expert-made features. Moreover, transfer learning stabilizes the training when low amount of data is present and enables transfer of useful knowledge and patterns to the target domain which results in better feature extraction. Having better features or higher performance in these domains can translate to real-world changes, ranging from finding a suitable drug candidate in a timely manner, to not miss-diagnosing an Electrocardiogram arrhythmia.

As demands for memory-intensive applications continue to grow, the memory capacity of each computing node is expected to grow at a similar pace. In high-performance computing (HPC) systems, the memory capacity per compute node is decided upon the most demanding application that would likely run on such a system, and hence the average capacity per node in future HPC systems is expected to grow significantly. However, diverse applications run on HPC systems with different memory requirements and memory utilization can fluctuate widely from one application to another. Since memory modules are private for a corresponding computing node, a large percentage of the overall memory capacity will likely be underutilized, especially when there are many jobs with small memory footprints. Thus, as HPC systems are moving towards the exascale era, better utilization of memory is strongly desired. Moreover, as new memory technologies come on the market, the flexibility of upgrading memory and system updates becomes a major concern since memory modules are tightly coupled with the computing nodes. To address these issues, vendors are exploring fabric-attached memories (FAM) systems. In this type of system, resources are decoupled and are maintained independently. Such a design has driven technology providers to develop new protocols, such as cache-coherent interconnects and memory semantic fabrics, to connect various discrete resources and help users leverage advances in-memory technologies to satisfy growing memory and storage demands. Using these new protocols, FAM can be directly attached to a system interconnect and be easily integrated with a variety of processing elements (PEs). Moreover, systems that support FAM can be smoothly upgraded and allow multiple PEs to share the FAM memory pools using well-defined protocols. The sharing of FAM between PEs allows efficient data sharing, improves memory utilization, reduces cost by allowing flexible integration of different PEs and memory modules from several vendors, and makes it easier to upgrade the system. However, adopting FAM in HPC systems brings in new challenges. Since memory is disaggregated and is accessed through fabric networks, latency in accessing memory (efficiency) is a crucial concern. In addition, quality of service, security from neighbor nodes, coherency, and address translation overhead to access FAM are some of the problems that require rethinking for FAM systems. To this end, we study and discuss various challenges that need to be addressed in FAM systems. Firstly, we developed a simulating environment to mimic and analyze FAM systems. Further, we showcase our work in addressing the challenges to improve the performance and increase the feasibility of such systems; enforcing quality of service, providing page migration support, and enhancing security from malicious neighbor nodes.

With the accessible data reaching zettabyte level, CMOS technology is reaching its limit for the data hungry applications. Moore's law has been reaching its depletion in recent studies. On the other hand, von Neumann architecture is approaching the bottleneck due to the data movement between the computing and memory units. With data movement and power budgets becoming the limiting factors of today's computing systems, in-memory computing using emerging non-volatile resistive devices has attracted an increasing amount of attention. A non-volatile resistive device may be realized using memristor, resistive random access memory (ReRAM), phase change memory (PCM), or spin-transfer torque magnetic random access memory (STT-MRAM). Resistive devices integrated into crossbar arrays simultaneously supports both dense storage and energy-efficient analog computation, which is highly desirable for processing of big data using both low-power mobile devices and high-performance computing (HPC) systems. However, analog computation is vulnerable and may suffer from robustness issues due to variations such as, array parasitics, device defects, non-ideal device characteristics, and various sources of errors. These non-ideal factors directly impact the computational accuracy of the in-memory computation and thereby the application level functional correctness. This dissertation is focused on improving the robustness and reliability of analog in-memory computing. Three directions are mainly explored: data layout organization techniques, software and hardware co-design, and hardware redundancy. Data layout organization aims to improve the robustness by masking the data to hardware according to the behavior of defective devices. Software and hardware co-design mitigates the impact by modifying the data in the neural networks or image compression applications to become amenable to device defects and data layout organizations. Hardware redundancy utilized multiple resistive device to realize each data, so each device can be programmed with different value and realize the data accurately with lower overhead.

As the size of cloud systems and the number of hosted virtual machines (VMs) rapidly grow, the scalability of shared VM storage systems becomes a serious issue. Client-side flash-based caching has the potential to improve the performance of cloud VM storage by employing flash storage available on the VM hosts to exploit the locality inherent in VM IOs. However, there are several challenges to the effective use of flash caching in cloud systems. First, cache configurations such as size, write policy, metadata persistency and RAID level have significant impacts on flash caching. Second, the typical capacity of flash devices is limited compared to the dataset size of consolidated VMs. Finally, flash devices wear out and face serious endurance issues which are aggravated by the use for caching. This dissertation presents the research for addressing these problems of cloud flash caching in the following three aspects. First, it presents a thorough study of different cache configurations including a new cache-optimized RAID configuration using a large amount of long-term traces collected from real-world public and private clouds. Second, it studies an on-demand flash cache management solution for meeting VM cache demands and minimizing device wear-out. It uses a new cache demand model Reuse Working Set (RWS) to capture the data with good temporal locality, and uses the RWS size (RWSS) to model a workload?s cache demand. Finally, to handle situations where a cache is insufficient for VMs? demands, it employs dynamic cache migration to balance cache load across hosts by live migrating cached data along with the VMs. The results show that the cache-optimized RAID improves performance by 137% without sacrificing reliability, compared to traditional RAID. The RWSS-based on-demand cache allocation reduces workload?s cache usage by 78% and lowers the amount of writes sent to cache device by 40%, compared to traditional working set based cache allocation. Combining on-demand cache allocation with dynamic cache migration for 12 concurrent VMs, results show 28% higher hit ratio and 28% lower 90th percentile IO latency, compared to the case without cache allocation.

Structural and functional brain images are generated as essential modalities for medical experts to learn about the different functions of the brain. These images are typically visually inspected by experts. Many software packages are available to process medical images, but they are complex and difficult to use. The software packages are also hardware intensive. As a consequence, this dissertation proposes a novel Neuroimaging Web Services Interface (NWSI) as a series of processing pipelines for a common platform to store, process, visualize and share data. The NWSI system is made up of password-protected interconnected servers accessible through a web interface. The web-interface driving the NWSI is based on Drupal, a popular open source content management system. Drupal provides a user-based platform, in which the core code for the security and design tools are updated and patched frequently. New features can be added via modules, while maintaining the core software secure and intact. The webserver architecture allows for the visualization of results and the downloading of tabulated data. Several forms are ix available to capture clinical data. The processing pipeline starts with a FreeSurfer (FS) reconstruction of T1-weighted MRI images. Subsequently, PET, DTI, and fMRI images can be uploaded. The Webserver captures uploaded images and performs essential functionalities, while processing occurs in supporting servers. The computational platform is responsive and scalable. The current pipeline for PET processing calculates all regional Standardized Uptake Value ratios (SUVRs). The FS and SUVR calculations have been validated using Alzheimer's Disease Neuroimaging Initiative (ADNI) results posted at Laboratory of Neuro Imaging (LONI). The NWSI system provides access to a calibration process through the centiloid scale, consolidating Florbetapir and Florbetaben tracers in amyloid PET images. The interface also offers onsite access to machine learning algorithms, and introduces new heat maps that augment expert visual rating of PET images. NWSI has been piloted using data and expertise from Mount Sinai Medical Center, the 1Florida Alzheimer’s Disease Research Center (ADRC), Baptist Health South Florida, Nicklaus Children's Hospital, and the University of Miami. All results were obtained using our processing servers in order to maintain data validity, consistency, and minimal processing bias.

One of the major difficulties in web application design is the tediousness of constructing new web pages from scratch. For traditional web application projects, the web application designers usually design and implement web application projects step by step, in detail. My project is called “automatic generation of web applications and management system.” This web application generator can generate the generic and customized web applications based on software engineering theories. The flow driven methodology will be used to drive the project by Business Process Model Notation (BPMN). Modules of the project are: database, web server, HTML page, functionality, financial analysis model, customer, and BPMN. The BPMN is the most important section of this entire project, due to the BPMN flow engine that most of the work and data flow depends on the engine. There are two ways to deal with the project. One way is to go to the main page, then to choose one web app template, and click the generating button. The other way is for the customers to request special orders. The project then will give suitable software development methodologies to follow up. After a software development life cycle, customers will receive their required product.

Computer data storage is growing at an astonishing rate. With cloud computing and the growth of the Internet enterprise storage has been predicted to grow at rates as high as 300\% per year. To fulfill this need technologies such as Redundant Array of Independent Disks or RAID are being used in industry today. Not only does RAID increase I/O performance but also provides redundancy measures to protect against hardware failure. Even though RAID has existed for some time now and is well understood, proprietary optimizations such as command scheduling and cache strategies that are employed by current RAID controllers are not well known. This thesis presents a model for RAID 5 that incorporates these features and describes the overall function of hardware RAID controllers. Also a python implementation of this model, Accurate Hardware RAID Simulator (AHRS) is presented and validated against a current hardware RAID controller. It is shown that AHRS can reproduce the behavior of a hardware RAID system with an accuracy of 97.92\% on average compared to a LSI hardware RAID controller.

Today’s economy and infrastructure is dependent on raw natural resources, like crude oil and natural gases, that are optimally transported through a net- work of hundreds of thousands of miles of pipelines throughout America[28]. A damaged pipe can negatively a↵ect thousands of homes and businesses so it is vital that they are monitored and quickly repaired[1]. Ideally, pipeline operators are able to detect damages before they occur, but ensuring the in- tegrity of the vast amount of pipes is unrealistic and would take an impractical amount of time and manpower[1]. Natural disasters, like earthquakes, as well as construction are just two of the events that could potentially threaten the integrity of pipelines. Due to the diverse collection of data sources, the necessary geospatial data is scat- tered across di↵erent physical locations, stored in di↵erent formats, and owned by di↵erent organizations. Pipeline companies do not have the resources to manually gather all input factors to make a meaningful analysis of the land surrounding a pipe. Our solution to this problem involves creating a single, centralized system that can be queried to get all necessary geospatial data and related informa- tion in a standardized and desirable format. The service simplifies client-side computation time by allowing our system to find, ingest, parse, and store the data from potentially hundreds of repositories in varying formats. An online web service fulfills all of the requirements and allows for easy remote access to do critical analysis of the data through computer based decision support systems (DSS). Our system, REST API for Pipeline Integrity Data (RAPID), is a multi- tenant REST API that utilizes HTTP protocol to provide a online and intuitive set of functions for DSS. RAPID’s API allows DSS to access and manage data stored in a geospatial database with a supported Django web framework. Full documentation of the design and implementation of RAPID’s API are detailed in this thesis document, supplemented with some background and validation of the completed system.