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The Visualization, Exploration, and Data Analysis (VEDA) Platform: A Modular, Open Platform Lowering the Barrier to Entry to Cloud Computing
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Cloud Computing for Mission Design and Operations
No abstract available
Enhancing Utility Operations with Digital Threads: The role of cloud computing in integrated data management and decision making
This slide deck is intended to be presented at the Digital Innovation Center for Excellence (DICE) conference May 2025. A lot of this information has already been approved through other presentations, but some information is new.
Redefining Tactical Operations for MER Using Cloud Computing
The Mars Exploration Rover Mission (MER) includes the twin rovers, Spirit and Opportunity, which have been performing geological research and surface exploration since early 2004. The rovers' durability well beyond their original prime mission (90 sols or Martian days) has allowed them to be a valuable platform for scientific research for well over 2000 sols, but as a by-product it has produced new challenges in providing efficient and cost-effective tactical operational planning. An early stage process adaptation was the move to distributed operations as mission scientists returned to their places of work in the summer of 2004, but they would still came together via teleconference and connected software to plan rover activities a few times a week. This distributed model has worked well since, but it requires the purchase, operation, and maintenance of a dedicated infrastructure at the Jet Propulsion Laboratory. This server infrastructure is costly to operate and the periodic nature of its usage (typically heavy usage for 8 hours every 2 days) has made moving to a cloud based tactical infrastructure an extremely tempting proposition. In this paper we will review both past and current implementations of the tactical planning application focusing on remote plan saving and discuss the unique challenges present with long-latency, distributed operations. We then detail the motivations behind our move to cloud based computing services and as well as our system design and implementation. We will discuss security and reliability concerns and how they were addressed
Interactive Quantum Chemistry Enabled by Machine Learning, Graphical Processing Units, and Cloud Computing
Modern quantum chemistry algorithms are increasingly able to accurately predict molecular properties that are useful for chemists in research and education. Despite this progress, performing such calculations is currently unattainable to the wider chemistry community, as they often require domain expertise, computer programming skills, and powerful computer hardware. In this review, we outline methods to eliminate these barriers using cutting-edge technologies. We discuss the ingredients needed to create accessible platforms that can compute quantum chemistry properties in real time, including graphical processing units–accelerated quantum chemistry in the cloud, artificial intelligence–driven natural molecule input methods, and extended reality visualization. We end by highlighting a series of exciting applications that assemble these components to create uniquely interactive platforms for computing and visualizing spectra, 3D structures, molecular orbitals, and many other chemical properties.
Deliver Signal Phase and Timing (SPAT) for Energy Optimization of Vehicle Cohort Via Cloud-Computing and LTE Communications
Predictive Signal Phase and Timing (SPAT) message set is one fundamental building block for vehicle-to-infrastructure (V2I) applications such as Eco-Approach and Departure (EAD) at traffic signal controlled urban intersections. Among the two complementary communication methods namely short-range sidelink (PC5) and long-range cellular radio link (Uu), this paper documents the work with long-range link: the complete data chain includes connecting to the traffic signals via existing backhaul communication network, collecting the raw signal phase state data, predicting the signal state changes and delivering the SPAT data via a geofenced service to requests over HTTP protocols. An Application Programming Interface (API) library is developed to support various cellular data transmission reduction and latency improvement techniques. An emulation-based algorithm is applied to predict the traffic signal state changes to provide adequate prediction horizon (e.g., at minimum 2 minutes) for the cohort energy optimization. In fact, the same connectivity and SPAT delivery methodology has been applied to traffic signalized intersections nationwide in the United States upon public agency approvals for access to their firewalled traffic control network and signal control systems or directly to individual controllers. This methodology proves its effectiveness and potential for rapid growth of such SPAT deliveries at mass production scale without needing infrastructure hardware retrofit or excessive communication means. To support the energy optimization of light and heavy-duty vehicle cohorts of mixed automation and propulsion systems (EV, ICE and hybrid), the connection and SPAT deliveries at two sites were completed, including public roads in Washtenaw County, Michigan and closed track test sites at American Center for Mobility (ACM) in Ypsilanti, Michigan. However, only closed test track results at ACM will be presented in this paper. A neuroevolution based optimizer is developed and implemented to control the speed of a vehicle cohort with different propulsion systems and automation levels. Closed track tests showed significant energy savings of the cohort operation.
Big Data and Cloud Computing Expert Panel
This talk is a brief introduction to the exploratory cloud prototypes currently underway within ESDIS and the implications of cloud technologies on existing systems.
Technical Challenges and Lessons from the Migration of the GLOBE Data and Information System to Utilize Cloud Computing Service
The Global Learning and Observation to Benefit the Environment (GLOBE) Data and Information System supports an international science and education program with capabilities to accept local environment observations, archive, display and visualize them along with global satellite observations. Since its inception twenty years ago, the Web and database system has been upgraded periodically to accommodate the changes in technology and the steady growth of GLOBEs education community and collection of observations. Recently, near the end-of-life of the system hardware, new commercial computer platform options were explored and a decision made to utilize Cloud services. Now the GLOBE DIS has been fully deployed and maintained using Amazon Cloud services for over two years now. This paper reviews the early risks, actual challenges, and some unexpected findings as a result of the GLOBE DIS migration. We describe the plans, cost drivers and estimates, highlight adjustments that were made and suggest improvements. We present the trade studies for provisioning, for load balancing, networks, processing, storage, as well as production, staging and backup systems. We outline the migration teams skills and required level of effort for transition, and resulting changes in the overall maintenance and operations activities. Examples include incremental adjustments to processing capacity and frequency of backups, and efforts previously expended on hardware maintenance that were refocused onto application-specific enhancements.
Technical Challenges and Lessons from the Migration of the GLOBE Data and Information System to Utilize Cloud Computing Service
The Global Learning and Observation to Benefit the Environment (GLOBE) Data and Information System supports an international science and education program with capabilities to accept local environment observations, archive, display and visualize them along with global satellite observations. Since its inception twenty years ago, the Web and database system has been upgraded periodically to accommodate the changes in technology and the steady growth of GLOBEs education community and collection of observations. Recently, near the end-of-life of the system hardware, new commercial computer platform options were explored and a decision made to utilize Cloud services. Now the GLOBE DIS has been fully deployed and maintained using Amazon Cloud services for over two years now. This paper reviews the early risks, actual challenges, and some unexpected findings as a result of the GLOBE DIS migration. We describe the plans, cost drivers and estimates, highlight adjustments that were made and suggest improvements. We present the trade studies for provisioning, for load balancing, networks, processing, storage, as well as production, staging and backup systems. We outline the migration teams skills and required level of effort for transition, and resulting changes in the overall maintenance and operations activities. Examples include incremental adjustments to processing capacity and frequency of backups, and efforts previously expended on hardware maintenance that were refocused onto application-specific enhancements.
Cloud-Computing and Machine Learning in Support of Country-Level Land Cover and Ecosystem Extent Mapping in Liberia and Gabon
Liberia and Gabon joined the Gaborone Declaration for Sustainability in Africa (GDSA), established in 2012, with the goal of incorporating the value of nature intonational decision making by estimating the multiple services obtained from ecosystems using the natural capital accounting framework. In this study, we produced 30-m resolution 10 classes land cover maps for the 2015 epoch for Liberia and Gabon using the Google Earth Engine (GEE) cloud platform to support the ongoing natural capital accounting efforts in these nations. We pro-pose an integrated method of pixel-based classification using Landsat 8 data, the Random Forest(RF) classifier and ancillary data to produce high quality land cover products to fit abroad range of applications, including natural capital accounting. Our approach focuses on a pre-classification filtering (Masking Phase) based on spectral signature and ancillary data to reduce the number of pixels prone to be misclassified; therefore, increasing the quality of the final product. The proposed approach yields an overall accuracy of 83% and 81% for Liberia and Gabon, respectively, out performing prior land cover products for these countries in both thematic content and accuracy. Our approach, while relatively simple and highly replicable, was able to produce high quality land cover products to fill an observational gap in up to date land cover data at national scale for Liberia and Gabon.
Leveraging Cloud Computing to Improve Storage Durability, Availability, and Cost for MER Maestro
The Maestro for MER (Mars Exploration Rover) software is the premiere operation and activity planning software for the Mars rovers, and it is required to deliver all of the processed image products to scientists on demand. These data span multiple storage arrays sized at 2 TB, and a backup scheme ensures data is not lost. In a catastrophe, these data would currently recover at 20 GB/hour, taking several days for a restoration. A seamless solution provides access to highly durable, highly available, scalable, and cost-effective storage capabilities. This approach also employs a novel technique that enables storage of the majority of data on the cloud and some data locally. This feature is used to store the most recent data locally in order to guarantee utmost reliability in case of an outage or disconnect from the Internet. This also obviates any changes to the software that generates the most recent data set as it still has the same interface to the file system as it did before updates
A Metadata and Time-Series DAS Workflow Using Cloud Computing
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Development of a High Resolution Weather Forecast Model for Mesoamerica Using the NASA Ames Code I Private Cloud Computing Environment
Two projects at NASA Marshall Space Flight Center have collaborated to develop a high resolution weather forecast model for Mesoamerica: The NASA Short-term Prediction Research and Transition (SPoRT) Center, which integrates unique NASA satellite and weather forecast modeling capabilities into the operational weather forecasting community. NASA's SERVIR Program, which integrates satellite observations, ground-based data, and forecast models to improve disaster response in Central America, the Caribbean, Africa, and the Himalayas.
Cloud Computing Technologies Facilitate Earth Research
Under a Space Act Agreement, NASA partnered with Seattle-based Amazon Web Services to make the agency's climate and Earth science satellite data publicly available on the company's servers. Users can access the data for free, but they can also pay to use Amazon's computing services to analyze and visualize information using the same software available to NASA researchers.
Cloud Computing Techniques for Space Mission Design
The overarching objective of space mission design is to tackle complex problems producing better results, and faster. In developing the methods and tools to fulfill this objective, the user interacts with the different layers of a computing system.