Dossier: Distributed operating system and infrastructure for scientific data management

As scientific advancement and discovery have become increasingly data-driven and interdisciplinary, there are urging needs for advanced cyberinfrastructure to support managing and process- ing scientific data generated from day-to-day research. However, the development of data-driven cyberinfrastructure for scientific research areas has often lagged behind the development of such tools in other engineering and IT-related fields. Such the development gap is due to various diversity challenges of scientific data management and processing. First, these are the challenges in terms of the diversity of scientific data and data processing tasks, as the cyberinfrastructure should be able to support managing and processing heterogeneous types of scientific data that have been captured from scientific instruments. Second, as the cyberinfrastructure must help to shorten time from digital capture of data to interpretation and insights, it is challenging for the infrastructure to deal with the diversity of users and scientific workload. Third, it is the diversity of scientific instruments. Since there is still a significant number of scientific instruments that run their scientific software tools on old operating systems (e.g., Windows XP, Windows NT, Windows 2000), the cyberinfrastructure must help to bridge the performance and security gap between old scientific instruments and its advanced cloud-based infrastructure. In this thesis, we aim to address the above diversity challenges by taking a holistic approach in designing a distributed operating system and infrastructure for scientific data management, named DOSSIER. At the core of DOSSIER is an adaptive control microservice infrastructure that is de- signed to tackle the aforementioned challenges of data cyberinfrastructure for distributed scientific data management. Particularly, to handle heterogeneous scientific data processing and analysis, we start with redesigning the execution environment for scientific workflows, which traditionally follows a monolithic approach, using a novel microservice architecture and latest virtualization technology (i.e., container technology). The microservice design enables dynamic composition of workflows, and thus, is efficient in dealing with heterogeneous workflows. The new microservice architecture also allows us to express system resources in a more simple way, and thus, enables the design of a new adaptive resource management mechanism to handle large-scale and dynamic scientific workloads. We are the first to apply feedback control theory to design a self-adaptation mechanism for scientific workflow management system to help shorten the time from data acquisition to insights. To address the security and performance gap issues when connecting old scientific instruments to cloud-based cyberinfrastructure, we design an edge-cloud architecture that puts cloudlet servers directly connected to the scientific instruments and act as the security shield for the aging instruments. Cloudlets will also coordinate with cloud-based backend system to tackle the performance issue by scheduling data transfer and offloading processing tasks to cloudlets to avoid traffic congestion and guarantee performance of data processing jobs across edge-cloud architecture. By designing, developing, and testing DOSSIER in the real scientific environments, we demonstrate that an edge-cloud microservice architecture with learning-based adaptive control resource management is needed for timely distributed scientific data management

Equity Of Urban Neighborhood Infrastructure: A Data-Driven Assessment

Neighborhood infrastructure, such as sidewalks, medical facilities, public transit, community gathering places, and tree canopy, provides essential support for safe, healthy, and resilient communities. This thesis proposes, develops, and implements an innovative approach to thoroughly examine the presence and condition of neighborhood infrastructure. It demonstrates the necessity of considering multiple infrastructure types when studying neighborhood infrastructure and its equity. This thesis provides an automated assessment framework as well as case studies among four major metropolitan cities across the United States, which expands the research opportunities for future infrastructure-related research

Green stormwater infrastructure, preference, and human well-being

Recently, cities across the world implemented Green Stormwater Infrastructure (GSI), a strategy that uses vegetation to manage stormwater. While evidence suggests that GSI provides ecological benefits to the urban environment, we know little about how GSI impacts human health and well-being. We know that urban nature, such as trees and parks, provides many human health benefits and is highly preferred. GSI, however, varies in shapes, sizes, and designs to support its unique stormwater functions. Its appearances greatly differ from the conventional urban landscape with mowed lawns and mature trees. Would these different forms of urban nature provides the same health benefits with trees alone? That is, to what extent do people prefer GSI as a part of urban landscape—and in extension, gain health benefits when they interact with GSI? This lack of knowledge prevents designers and planners from designing GSI that people prefer, which might reduce the effectiveness of urban nature. Furthermore, people are less likely to accept and contribute for the landscapes they do not prefer, which might affect the performance of GSI itself. To address this research gap, I conducted four empirical studies linking GSI and human preference and well-being. First, I examined how people perceived and preferred different type of GSI using photo-questionnaire and factor analysis. Then, I examined how people preferred GSI landscapes with different vegetation density levels by analyzing three photo-questionnaire and Browndog’s Green Index, a recently developed tool that identifies vegetation density via machine learning and image processing. Third, I investigated the relationships between GSI density and two psycho-physiological measures: stress and attention. Finally, I examined how other urban contexts, such as perceived messiness, perceived levels of urban developments, and perceived safety influence GSI preference, and whether these contexts mediate the relationship between vegetation density and preference. I found that different types of GSI are preferred differently, and messiness played a role in bio-retention preference. More vegetation density predicted higher preference, but an increase in vegetation density was associated with more dramatic changes in preference when the vegetation density level was low. I did not find a significant relationship between GSI, stress, and attention capacity. Finally, I found that perceived levels of urban developments and perceived safety predicted GSI preference, but did not mediate the relationship between GSI vegetation density and preference. From our results, I suggested that designers should not be discouraged from applying GSI in the urban environment, especially around the areas with low vegetation density. Designers and planners should emphasize neatness and cues of care in GSI designs. They should find a way to minimize perceived urban landscape, that is: make the landscape appear more natural, and increase perceived safety in GSI implementation, among other landscapes. This research is important because it is one of the first studies that objectively examined how people prefer some types of GSI, such as bio-retention. It also is the first few to use a recent research technology, Browndog’s Green Index, to further built-environment research. I proposed future studies to further examine the relationship between GSI density and stress and attention capacity, while considering the urban contexts and stormwater capacity and management of the GSI. This dissertation and my future studies will help contribute to make cities across the world healthier for humans and the surrounding ecosystem, and to bring nature to every doorstep