2016 Research Week

RESEARCH WEEK 2016 TABLE OF CONTENTS ADMINISTRATION RESEARCH COMMITTEES ACTIVITY SCHEDULE LETTERS OF ENDORSEMENT GENERAL SESSION FACULTY, STAFF, AND STUDENT POSTER PRESENTATIONS FACULTY AND STUDENT ORAL PRESENTATION

Real-Time Road Hazard Information System

Infrastructure is a significant factor in economic growth for systems of government. In order to increase economic productivity, maintaining infrastructure quality is essential. One of the elements of infrastructure is roads. Roads are means which help local and national economies be more productive. Furthermore, road damage such as potholes, debris, or cracks is the cause of many on-road accidents that have cost the lives of many drivers. In this paper, we propose a system that uses Convolutional Neural Networks to detect road degradations without data pre-processing. We utilize the state-of-the-art object detection algorithm, YOLO detector for the system. First, we developed a basic system working on data collecting, pre-processing, and classification. Secondly, we improved the classification performance achieving 97.98% in the overall model testing, and then we utilized pixel-level classification and detection with a method called semantic segmentation. We were able to achieve decent results using this method to detect and classify four different classes (Manhole, Pothole, Blurred Crosswalk, Blurred Street Line). We trained a segmentation model that recognizes the four classes mentioned above and achieved great results with this model allowing the machine to effectively and correctly identify and classify our four classes in an image. Although we obtained excellent accuracy from the detectors, these do not perform particularly well on embedded systems due to their network size. Therefore, we opted for a smaller, less accurate detector that will run in real time on a cheap embedded system, like the Google Coral Dev Board, without needing a powerful and expensive GPU

Competing mechanisms and scaling laws for carbon nanotube scission by ultrasonication

Dispersion of carbon nanotubes (CNTs) into liquids typically requires ultrasonication to exfoliate individuals CNTs from bundles. Experiments show that CNT length drops with sonication time (or energy) as a power law t?m. Yet the breakage mechanism is not well understood, and the experimentally reported power law exponent m ranges from approximately 0.2 to 0.5. Here we simulate the motion of CNTs around cavitating bubbles by coupling Brownian dynamics with the Rayleigh-Plesset equation. We observe that, during bubble growth, CNTs align tangentially to the bubble surface. Surprisingly, we find two dynamical regimes during the collapse: shorter CNTs align radially, longer ones buckle.We compute the phase diagram for CNT collapse dynamics as a function of CNT length, stiffness, and initial distance from the bubble nuclei and determine the transition from aligning to buckling. We conclude that, depending on their length, CNTs can break due to either buckling or stretching. These two mechanisms yield different power laws for the length decay (0.25 and 0.5, respectively), reconciling the apparent discrepancy in the experimental data

Challenges in real-time virtualization and predictable cloud computing

Cloud computing and virtualization technology have revolutionized general-purpose computing applications in the past decade. The cloud paradigm offers advantages through reduction of operation costs, server consolidation, flexible system configuration and elastic resource provisioning. However, despite the success of cloud computing for general-purpose computing, existing cloud computing and virtualization technology face tremendous challenges in supporting emerging soft real-time applications such as online video streaming, cloud-based gaming, and telecommunication management. These applications demand real-time performance in open, shared and virtualized computing environments. This paper identifies the technical challenges in supporting real-time applications in the cloud, surveys recent advancement in real-time virtualization and cloud computing technology, and offers research directions to enable cloud-based real-time applications in the future

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Evaluating and Enabling Scalable High Performance Computing Workloads on Commercial Clouds

Performance, usability, and accessibility are critical components of high performance computing (HPC). Usability and performance are especially important to academic researchers as they generally have little time to learn a new technology and demand a certain type of performance in order to ensure the quality and quantity of their research results. We have observed that while not all workloads run well in the cloud, some workloads perform well. We have also observed that although commercial cloud adoption by industry has been growing at a rapid pace, its use by academic researchers has not grown as quickly. We aim to help close this gap and enable researchers to utilize the commercial cloud more efficiently and effectively. We present our results on architecting and benchmarking an HPC environment on Amazon Web Services (AWS) where we observe that there are particular types of applications that are and are not suited for the commercial cloud. Then, we present our results on architecting and building a provisioning and workflow management tool (PAW), where we developed an application that enables a user to launch an HPC environment in the cloud, execute a customizable workflow, and after the workflow has completed delete the HPC environment automatically. We then present our results on the scalability of PAW and the commercial cloud for compute intensive workloads by deploying a 1.1 million vCPU cluster. We then discuss our research into the feasibility of utilizing commercial cloud infrastructure to help tackle the large spikes and data-intensive characteristics of Transportation Cyberphysical Systems (TCPS) workloads. Then, we present our research in utilizing the commercial cloud for urgent HPC applications by deploying a 1.5 million vCPU cluster to process 211TB of traffic video data to be utilized by first responders during an evacuation situation. Lastly, we present the contributions and conclusions drawn from this work

Enabling virtualization technologies for enhanced cloud computing

Cloud Computing is a ubiquitous technology that offers various services for individual users, small businesses, as well as large scale organizations. Data-center owners maintain clusters of thousands of machines and lease out resources like CPU, memory, network bandwidth, and storage to clients. For organizations, cloud computing provides the means to offload server infrastructure and obtain resources on demand, which reduces setup costs as well as maintenance overheads. For individuals, cloud computing offers platforms, resources and services that would otherwise be unavailable to them. At the core of cloud computing are various virtualization technologies and the resulting Virtual Machines (VMs). Virtualization enables cloud providers to host multiple VMs on a single Physical Machine (PM). The hallmark of VMs is the inability of the end-user to distinguish them from actual PMs. VMs allow cloud owners such essential features as live migration, which is the process of moving a VM from one PM to another while the VM is running, for various reasons. Features of the cloud such as fault tolerance, geographical server placement, energy management, resource management, big data processing, parallel computing, etc. depend heavily on virtualization technologies. Improvements and breakthroughs in these technologies directly lead to introduction of new possibilities in the cloud. This thesis identifies and proposes innovations for such underlying VM technologies and tests their performance on a cluster of 16 machines with real world benchmarks. Specifically the issues of server load prediction, VM consolidation, live migration, and memory sharing are attempted. First, a unique VM resource load prediction mechanism based on Chaos Theory is introduced that predicts server workloads with high accuracy. Based on these predictions, VMs are dynamically and autonomously relocated to different PMs in the cluster in an attempt to conserve energy. Experimental evaluations with a prototype on real world data- center load traces show that up to 80% of the unused PMs can be freed up and repurposed, with Service Level Objective (SLO) violations as little as 3%. Second, issues in live migration of VMs are analyzed, based on which a new distributed approach is presented that allows network-efficient live migration of VMs. The approach amortizes the transfer of memory pages over the life of the VM, thus reducing network traffic during critical live migration. The prototype reduces network usage by up to 45% and lowers required time by up to 40% for live migration on various real-world loads. Finally, a memory sharing and management approach called ACE-M is demonstrated that enables VMs to share and utilize all the memory available in the cluster remotely. Along with predictions on network and memory, this approach allows VMs to run applications with memory requirements much higher than physically available locally. It is experimentally shown that ACE-M reduces the memory performance degradation by about 75% and achieves a 40% lower network response time for memory intensive VMs. A combination of these innovations to the virtualization technologies can minimize performance degradation of various VM attributes, which will ultimately lead to a better end-user experience