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Development of Computational Environmental Microbiome Workflows for the Laboratory and the International Space Station

Identification of microorganisms in the spaceflight environment is critical for crew health risk assessment on the International Space Station (ISS). Since 2017, nanopore sequencing technology has been used to support thein situ identification of microbial species during spaceflight. Beginning in 2018, a culture-independent, swab-to-sequencer method was implemented onboard the ISS to provide a more thorough insight of the ISS microbiome. Eliminating microbial culture enables identification of difficult-to-culture organisms, reduces risks associated with potentially pathogenic cultures, and could significantly reduce the time from sample-to-answer. However, this molecular-based approach generates large metagenomic datasets that require substantial computational resources for analysis. To process nanopore-generated sequencing data, the JSC Microbiology Laboratory established a bioinformatics workflow on Amazon EC2 under the security guidance of the NASA Science Managed Cloud Environment (SMCE).This resource allows for the development, testing, and accessing of computational tools for processing large and complex datasets. The work described here will address the downlinking of data from the ISS, the automated pipeline developed to identify targeted bacterial and fungal organisms, and the time from sampling onboard to microbial identification. The pipelines have been enhanced to address high and low biomass samples using optimization based on sample source (air, water, or surface) and type of collection (filter, colony, or swab).The resulting microbiome data can be assessed beyond microbial identifications to gain understanding toward population changes over time, potential selective environmental pressures, and evaluating correlations with a wide range of additional data sets. Metagenome analysis pipelines in development could allow for simultaneous identification of microbial species, gene function, and gene pathways present in the environment. Beyond the ground processing, the developed analysis pipeline is currently deployed onboard the ISS to allow for near real-time assessments of the ISS microbiome. This study serves as a critical foundation for exploration missions, where rapid microbiome analyses will be required.

G. Marie Sharp

Evaluating the Efficacy of the Cloud for Cluster Computation

Computing requirements vary by industry, and it follows that NASA and other research organizations have computing demands that fall outside the mainstream. While cloud computing made rapid inroads for tasks such as powering web applications, performance issues on highly distributed tasks hindered early adoption for scientific computation. One venture to address this problem is Nebula, NASA's homegrown cloud project tasked with delivering science-quality cloud computing resources. However, another industry development is Amazon's high-performance computing (HPC) instances on Elastic Cloud Compute (EC2) that promises improved performance for cluster computation. This paper presents results from a series of benchmarks run on Amazon EC2 and discusses the efficacy of current commercial cloud technology for running scientific applications across a cluster. In particular, a 240-core cluster of cloud instances achieved 2 TFLOPS on High-Performance Linpack (HPL) at 70% of theoretical computational performance. The cluster's local network also demonstrated sub-100 ?s inter-process latency with sustained inter-node throughput in excess of 8 Gbps. Beyond HPL, a real-world Hadoop image processing task from NASA's Lunar Mapping and Modeling Project (LMMP) was run on a 29 instance cluster to process lunar and Martian surface images with sizes on the order of tens of gigapixels. These results demonstrate that while not a rival of dedicated supercomputing clusters, commercial cloud technology is now a feasible option for moderately demanding scientific workloads.

Cloud Computing

Leveraging the Cloud for Robust and Efficient Lunar Image Processing

The Lunar Mapping and Modeling Project (LMMP) is tasked to aggregate lunar data, from the Apollo era to the latest instruments on the LRO spacecraft, into a central repository accessible by scientists and the general public. A critical function of this task is to provide users with the best solution for browsing the vast amounts of imagery available. The image files LMMP manages range from a few gigabytes to hundreds of gigabytes in size with new data arriving every day. Despite this ever-increasing amount of data, LMMP must make the data readily available in a timely manner for users to view and analyze. This is accomplished by tiling large images into smaller images using Hadoop, a distributed computing software platform implementation of the MapReduce framework, running on a small cluster of machines locally. Additionally, the software is implemented to use Amazon's Elastic Compute Cloud (EC2) facility. We also developed a hybrid solution to serve images to users by leveraging cloud storage using Amazon's Simple Storage Service (S3) for public data while keeping private information on our own data servers. By using Cloud Computing, we improve upon our local solution by reducing the need to manage our own hardware and computing infrastructure, thereby reducing costs. Further, by using a hybrid of local and cloud storage, we are able to provide data to our users more efficiently and securely. 12 This paper examines the use of a distributed approach with Hadoop to tile images, an approach that provides significant improvements in image processing time, from hours to minutes. This paper describes the constraints imposed on the solution and the resulting techniques developed for the hybrid solution of a customized Hadoop infrastructure over local and cloud resources in managing this ever-growing data set. It examines the performance trade-offs of using the more plentiful resources of the cloud, such as those provided by S3, against the bandwidth limitations such use encounters with remote resources. As part of this discussion this paper will outline some of the technologies employed, the reasons for their selection, the resulting performance metrics and the direction the project is headed based upon the demonstrated capabilities thus far.

Cloud Computing

Cloud-Based Numerical Weather Prediction for Near Real-Time Forecasting and Disaster Response

The use of cloud computing resources continues to grow within the public and private sector components of the weather enterprise as users become more familiar with cloud‐computing concepts, and competition among service providers continues to reduce costs and other barriers to entry. Cloud resources can also provide capabilities similar to high‐performance computing environments, supporting multi‐node systems required for near real‐time, regional weather predictions. Referred to as "Infrastructure as a Service", or IaaS, the use of cloud-based computing hardware in an on‐demand payment system allows for rapid deployment of a modeling system in environments lacking access to a large, supercomputing infrastructure. Use of IaaS capabilities to support regional weather prediction may be of particular interest to developing countries that have not yet established large supercomputing resources, but would otherwise benefit from a regional weather forecasting capability. Recently, collaborators from NASA Marshall Space Flight Center and Ames Research Center have developed a scripted, on‐demand capability for launching the NOAA/NWS Science and Training Resource Center (STRC) Environmental Modeling System (EMS), which includes pre‐compiled binaries of the latest version of the Weather Research and Forecasting (WRF) model. The WRF‐EMS provides scripting for downloading appropriate initial and boundary conditions from global models, along with higher‐resolution vegetation, land surface, and sea surface temperature data sets provided by the NASA Short‐term Prediction Research and Transition (SPoRT) Center. This presentation will provide an overview of the modeling system capabilities and benchmarks performed on the Amazon Elastic Compute Cloud (EC2) environment. In addition, the presentation will discuss future opportunities to deploy the system in support of weather prediction in developing countries supported by NASA's SERVIR Project, which provides capacity building activities in environmental monitoring and prediction across a growing number of regional hubs throughout the world. Capacity‐building applications that extend numerical weather prediction to developing countries are intended to provide near real‐time applications to benefit public health, safety, and economic interests, but may have a greater impact during disaster events by providing a source for local predictions of weather‐related hazards, or impacts that local weather events may have during the recovery phase.

Molthan, Andrew

Exploring New Frontiers in Space Communications: Enhancing Delay Tolerant Networking through Cloud and Containerization

The High-rate Delay Tolerant Networking (HDTN) project at NASA Glenn Research Center has developed software that enables more flexible, reliable, and efficient space internetworking by using modern computing techniques such as cloud services, microservices, network function virtualization, software defined networking, and a distributed architecture. HDTN is built upon the Bundle Protocol and related convergence layers which have been developed to mitigate the challenges of the space networking environment including long delays, asymmetric data rates, and intermittent connectivity. The HDTN implementation employs asynchronous message processing tasks which allow for non-blocking operations as well as deployment in both centralized and distributed architectures. This paper investigates deploying HDTN in a containerized approach on the NASA Goddard’s Mission Cloud Platform using Amazon Web Services Elastic Compute Cloud (EC2). Commercial cloud computing will lower operating costs, provide flexible resource allocation, and allow for interconnectivity between multiple NASA centers as well as external partners. Containerization using Docker will enable greater portability and scalability for HDTN to be deployed into a variety of environments. We discuss possible NASA missions and use-cases such as the Laser Communications Relay Demonstration (LCRD) where the services provided by HDTN (reliable transport, high-rate message processing, and store-and-forward capabilities) will be enhanced through cloud computing and containerization. In addition, we describe the HDTN architecture and possible microservice-based networking approaches that can be obtained via HDTN’s configuration capabilities. Finally, we detail the EC2 specifications needed to achieve data rates greater than 1 Gbps to support optical communication missions such as LCRD.

Blake LaFuente

On-demand Command and Control of ASTERIA with Cloud-based Ground Station Services

ASTERIA (Arcsecond Space Telescope Enabling Research in Astrophysics) was a 6-unit CubeSat technology demonstration mission that deployed from the International Space Station on November 20th, 2017. After successfully completing its 90-day primary mission that demonstrated arcsecond-level line-of-sight pointing and focal plane thermal stability for exoplanet detection, it entered an extended mission performing onboard software demonstrations to mature technology both in space and on the ground. One of the technologies was a completely cloud-based ground system leveraging Amazon Web Services (AWS) Ground Station service.Announced in December 2018 and launched in May 2019, AWS Ground Station is a fully managed ground station service that aims to reduce the overhead associated with developing and maintaining ground system infrastructure throughout the mission lifecycle. AWS Ground Station makes available the suite of features required for any ground system in support of low-Earth orbit (LEO) and medium-Earth Orbit (MEO) satellite operations on-demand and without setting up or maintaining long-term contracts. Charges are incurred on a per-minute basis for antenna usage during scheduled tracks. Support is available for S-band uplink and downlink, along with X-band narrowband and wideband downlink. Missions that use the service may reserve tracks with any licensed AWS Ground Station antennas located across each service region and have direct access to any AWS services in support of mission operations.The cloud-based architecture built around the AWS Ground Station service greatly enhanced ASTERIA mission operations by enabling end-to-end pass automation, on-demand contact scheduling and contingency planning, along with more efficient data downlink through station availability and station-to-station handovers. It incorporated open-source software, particularly NASA's AMMOS Instrument Toolkit (AIT) and Open Mission Control Technologies (OpenMCT), along with the AWS application programming interfaces (API) to the Ground Station, Elastic Compute Cloud (EC2) and Simple Storage Service (S3) services. After showcasing operability in August 2019, the team continued using and improving this novel ground system architecture until the end of mission in December 2019. This paper describes the cloud-based ground system, how it was designed, tested, and evaluated with an in-orbit spacecraft, the operational capabilities that it enabled, along with lessons learned and recommendations for future missions.

Fesq, Lorraine

On-demand Command and Control of ASTERIA with Cloud-based Ground Station Services

ASTERIA (Arcsecond Space Telescope Enabling Research in Astrophysics) was a 6-unit CubeSat technology demonstration mission that deployed from the International Space Station on November 20th, 2017. After successfully completing its 90-day primary mission that demonstrated arcsecond-level line-of-sight pointing and focal plane thermal stability for exoplanet detection, it entered an extended mission performing onboard software demonstrations to mature technology both in space and on the ground. One of the technologies was a completely cloud-based ground system leveraging Amazon Web Services (AWS) Ground Station service. Announced in December 2018 and launched in May 2019, AWS Ground Station is a fully managed ground station service that aims to reduce the overhead associated with developing and maintaining ground system infrastructure throughout the mission lifecycle. AWS Ground Station makes available the suite of features required for any ground system in support of low-Earth orbit (LEO) and medium-Earth Orbit (MEO) satellite operations on-demand and without setting up or maintaining long-term contracts. Charges are incurred on a per-minute basis for antenna usage during scheduled tracks. Support is available for S-band uplink and downlink, along with X-band narrowband and wideband downlink. Missions that use the service may reserve tracks with any licensed AWS Ground Station antennas located across each service region and have direct access to any AWS services in support of mission operations. The cloud-based architecture built around the AWS Ground Station service greatly enhanced ASTERIA mission operations by enabling end-to-end pass automation, on-demand contact scheduling and contingency planning, along with more efficient data downlink through station availability and station-tostation handovers. It incorporated open-source software, particularly NASA's AMMOS Instrument Toolkit (AIT) and Open Mission Control Technologies (OpenMCT), along with the AWS application programming interfaces (API) to the Ground Station, Elastic Compute Cloud (EC2) and Simple Storage Service (S3) services. After showcasing operability in August 2019, the team continued using and improving this novel ground system architecture until the end of mission in December 2019. This paper describes the cloud-based ground system, how it was designed, tested, and evaluated with an inorbit spacecraft, the operational capabilities that it enabled, along with lessons learned and recommendations for future missions.

Fesq, Lorraine