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Issues and recommendations associated with distributed computation and data management systems for the space sciences

The primary purpose of the report is to explore management approaches and technology developments for computation and data management systems designed to meet future needs in the space sciences.The report builds on work presented in previous reports on solar-terrestrial and planetary reports, broadening the outlook to all of the space sciences, and considering policy issues aspects related to coordiantion between data centers, missions, and ongoing research activities, because it is perceived that the rapid growth of data and the wide geographic distribution of relevant facilities will present especially troublesome problems for data archiving, distribution, and analysis.

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An Overview of the Challenges With and Proposed Solutions for the Ingest and Distribution Processes for Airborne Data Management

The current data management practices for NASA airborne field projects have successfully served science team data needs over the past 30 years to achieve project science objectives, however, users have discovered a number of issues in terms of data reporting and format. The ICARTT format, a NASA standard since 2010, is currently the most popular among the airborne measurement community. Although easy for humans to use, the format standard is not sufficiently rigorous to be machine-readable. This makes data use and management tedious and resource intensive, and also create problems in Distributed Active Archive Center (DAAC) data ingest procedures and distribution. Further, most DAACs use metadata models that concentrate on satellite data observations, making them less prepared to deal with airborne data.

Beach, Aubrey↗

NASA's EOSDIS Cumulus: Ingesting, Archiving, Managing, and Distributing Earth Science Data from the Commercial Cloud

NASA's Earth Observing System Data and Information System (EOSDIS) has been working towards a vision of a cloud-based, highly-flexible, ingest, archive, management, and distribution system for its ever-growing and evolving data holdings. This system, Cumulus, is emerging from its prototyping stages and is poised to make a huge impact on how NASA manages and disseminates its Earth science data. This talk will outline the motivation for this work, present the achievements and hurdles of the past 18 months and will chart a course for the future expansion of the Cumulus expansion. We will explore on not just the technical, but also the socio-technical challenges that we face in evolving a system of this magnitude into the cloud and how we are rising to meet those challenges through open collaboration and intentional stakeholder engagement.

Earth Scienc↗

Space Station Furnace Facility. Volume 2: Summary of technical reports

The Space Station Furnace Facility (SSFF) is a modular facility for materials research in the microgravity environment of the Space Station Freedom (SSF). The SSFF is designed for crystal growth and solidification research in the fields of electronic and photonic materials, metals and alloys, and glasses and ceramics, and will allow for experimental determination of the role of gravitational forces in the solidification process. The facility will provide a capability for basic scientific research and will evaluate the commercial viability of low-gravity processing of selected technologically important materials. In order to accommodate the furnace modules with the resources required to operate, SSFF developed a design that meets the needs of the wide range of furnaces that are planned for the SSFF. The system design is divided into subsystems which provide the functions of interfacing to the SSF services, conditioning and control for furnace module use, providing the controlled services to the furnace modules, and interfacing to and acquiring data from the furnace modules. The subsystems, described in detail, are as follows: Power Conditioning and Distribution Subsystem; Data Management Subsystem; Software; Gas Distribution Subsystem; Thermal Control Subsystem; and Mechanical Structures Subsystem.

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A database management capability for Ada

The data requirements of mission critical defense systems have been increasing dramatically. Command and control, intelligence, logistics, and even weapons systems are being required to integrate, process, and share ever increasing volumes of information. To meet this need, systems are now being specified that incorporate data base management subsystems for handling storage and retrieval of information. It is expected that a large number of the next generation of mission critical systems will contain embedded data base management systems. Since the use of Ada has been mandated for most of these systems, it is important to address the issues of providing data base management capabilities that can be closely coupled with Ada. A comprehensive distributed data base management project has been investigated. The key deliverables of this project are three closely related prototype systems implemented in Ada. These three systems are discussed.

Chan, Arvola↗

Network Information Management Subsystem

The Deep Space Network is implementing a distributed data base management system in which the data are shared among several applications and the host machines are not totally dedicated to a particular application. Since the data and resources are to be shared, the equipment must be operated carefully so that the resources are shared equitably. The current status of the project is discussed and policies, roles, and guidelines are recommended for the organizations involved in the project.

Chatburn, C. C.↗

ACE: A distributed system to manage large data archives

Competitive pressures in the oil and gas industry are requiring a much tighter integration of technical data into E and P business processes. The development of new systems to accommodate this business need must comprehend the significant numbers of large, complex data objects which the industry generates. The life cycle of the data objects is a four phase progression from data acquisition, to data processing, through data interpretation, and ending finally with data archival. In order to implement a cost effect system which provides an efficient conversion from data to information and allows effective use of this information, an organization must consider the technical data management requirements in all four phases. A set of technical issues which may differ in each phase must be addressed to insure an overall successful development strategy. The technical issues include standardized data formats and media for data acquisition, data management during processing, plus networks, applications software, and GUI's for interpretation of the processed data. Mass storage hardware and software is required to provide cost effective storage and retrieval during the latter three stages as well as long term archival. Mobil Oil Corporation's Exploration and Producing Technical Center (MEPTEC) has addressed the technical and cost issues of designing, building, and implementing an Advanced Computing Environment (ACE) to support the petroleum E and P function, which is critical to the corporation's continued success. Mobile views ACE as a cost effective solution which can give Mobile a competitive edge as well as a viable technical solution.

Daily, Mike I.↗

Concurrent Image Processing Executive (CIPE). Volume 1: Design overview

The design and implementation of a Concurrent Image Processing Executive (CIPE), which is intended to become the support system software for a prototype high performance science analysis workstation are described. The target machine for this software is a JPL/Caltech Mark 3fp Hypercube hosted by either a MASSCOMP 5600 or a Sun-3, Sun-4 workstation; however, the design will accommodate other concurrent machines of similar architecture, i.e., local memory, multiple-instruction-multiple-data (MIMD) machines. The CIPE system provides both a multimode user interface and an applications programmer interface, and has been designed around four loosely coupled modules: user interface, host-resident executive, hypercube-resident executive, and application functions. The loose coupling between modules allows modification of a particular module without significantly affecting the other modules in the system. In order to enhance hypercube memory utilization and to allow expansion of image processing capabilities, a specialized program management method, incremental loading, was devised. To minimize data transfer between host and hypercube, a data management method which distributes, redistributes, and tracks data set information was implemented. The data management also allows data sharing among application programs. The CIPE software architecture provides a flexible environment for scientific analysis of complex remote sensing image data, such as planetary data and imaging spectrometry, utilizing state-of-the-art concurrent computation capabilities.

Lee, Meemong↗

Distributed data base systems with special emphasis toward POREL

In the last few years a number of research and advanced development projects have resulted in distributed data base management prototypes. POREL, developed at the University of Stuttgart, is a multiuser, distributed, relational system developed for wide and local area networks of minicomputers and advanced micros. The general objectives of such data base systems and the architecture of POREL are discussed. In addition a comparison of some of the the existing distributed DMBS is included to provide the reader with information about the current state of the art.

Neuhold, E. J.↗

Information Power Grid: Distributed High-Performance Computing and Large-Scale Data Management for Science and Engineering

We use the term "Grid" to refer to distributed, high performance computing and data handling infrastructure that incorporates geographically and organizationally dispersed, heterogeneous resources that are persistent and supported. This infrastructure includes: (1) Tools for constructing collaborative, application oriented Problem Solving Environments / Frameworks (the primary user interfaces for Grids); (2) Programming environments, tools, and services providing various approaches for building applications that use aggregated computing and storage resources, and federated data sources; (3) Comprehensive and consistent set of location independent tools and services for accessing and managing dynamic collections of widely distributed resources: heterogeneous computing systems, storage systems, real-time data sources and instruments, human collaborators, and communications systems; (4) Operational infrastructure including management tools for distributed systems and distributed resources, user services, accounting and auditing, strong and location independent user authentication and authorization, and overall system security services The vision for NASA's Information Power Grid - a computing and data Grid - is that it will provide significant new capabilities to scientists and engineers by facilitating routine construction of information based problem solving environments / frameworks. Such Grids will knit together widely distributed computing, data, instrument, and human resources into just-in-time systems that can address complex and large-scale computing and data analysis problems. Examples of these problems include: (1) Coupled, multidisciplinary simulations too large for single systems (e.g., multi-component NPSS turbomachine simulation); (2) Use of widely distributed, federated data archives (e.g., simultaneous access to metrological, topological, aircraft performance, and flight path scheduling databases supporting a National Air Space Simulation systems}; (3) Coupling large-scale computing and data systems to scientific and engineering instruments (e.g., realtime interaction with experiments through real-time data analysis and interpretation presented to the experimentalist in ways that allow direct interaction with the experiment (instead of just with instrument control); (5) Highly interactive, augmented reality and virtual reality remote collaborations (e.g., Ames / Boeing Remote Help Desk providing field maintenance use of coupled video and NDI to a remote, on-line airframe structures expert who uses this data to index into detailed design databases, and returns 3D internal aircraft geometry to the field); (5) Single computational problems too large for any single system (e.g. the rotocraft reference calculation). Grids also have the potential to provide pools of resources that could be called on in extraordinary / rapid response situations (such as disaster response) because they can provide common interfaces and access mechanisms, standardized management, and uniform user authentication and authorization, for large collections of distributed resources (whether or not they normally function in concert). IPG development and deployment is addressing requirements obtained by analyzing a number of different application areas, in particular from the NASA Aero-Space Technology Enterprise. This analysis has focussed primarily on two types of users: the scientist / design engineer whose primary interest is problem solving (e.g. determining wing aerodynamic characteristics in many different operating environments), and whose primary interface to IPG will be through various sorts of problem solving frameworks. The second type of user is the tool designer: the computational scientists who convert physics and mathematics into code that can simulate the physical world. These are the two primary users of IPG, and they have rather different requirements. The results of the analysis of the needs of these two types of users provides a broad set of requirements that gives rise to a general set of required capabilities. The IPG project is intended to address all of these requirements. In some cases the required computing technology exists, and in some cases it must be researched and developed. The project is using available technology to provide a prototype set of capabilities in a persistent distributed computing testbed. Beyond this, there are required capabilities that are not immediately available, and whose development spans the range from near-term engineering development (one to two years) to much longer term R&D (three to six years). Additional information is contained in the original.

Johnston, William E.↗

Data management

The following tasks were prioritized: software acquisition management plan; space station flight data system architectural study; space station user data system interface; automation of software development process; automation of software testing; distributed data base management; ADA (automated data acquisition) evaluation and transition and planning; network operating system software; fault tolerant computer validation methodology for onboard data management system; systems integration; artificial intelligence/expert systems; space station data network concept; space station standard interface protocols; space station data networks systems; integrated software development facility; and language trade studies.

Love, G.↗

Information Power Grid: Distributed High-Performance Computing and Large-Scale Data Management for Science and Engineering

The term "Grid" refers to distributed, high performance computing and data handling infrastructure that incorporates geographically and organizationally dispersed, heterogeneous resources that are persistent and supported. The vision for NASN's Information Power Grid - a computing and data Grid - is that it will provide significant new capabilities to scientists and engineers by facilitating routine construction of information based problem solving environments / frameworks that will knit together widely distributed computing, data, instrument, and human resources into just-in-time systems that can address complex and large-scale computing and data analysis problems. IPG development and deployment is addressing requirements obtained by analyzing a number of different application areas, in particular from the NASA Aero-Space Technology Enterprise. This analysis has focussed primarily on two types of users: The scientist / design engineer whose primary interest is problem solving (e.g., determining wing aerodynamic characteristics in many different operating environments), and whose primary interface to IPG will be through various sorts of problem solving frameworks. The second type of user if the tool designer: The computational scientists who convert physics and mathematics into code that can simulate the physical world. These are the two primary users of IPG, and they have rather different requirements. This paper describes the current state of IPG (the operational testbed), the set of capabilities being put into place for the operational prototype IPG, as well as some of the longer term R&D tasks.

Johnston, William E.↗

NASA's Earth Science Data Systems

NASA's Earth Science Data Systems (ESDS) Program has evolved over the last two decades, and currently has several core and community components. Core components provide the basic operational capabilities to process, archive, manage and distribute data from NASA missions. Community components provide a path for peer-reviewed research in Earth Science Informatics to feed into the evolution of the core components. The Earth Observing System Data and Information System (EOSDIS) is a core component consisting of twelve Distributed Active Archive Centers (DAACs) and eight Science Investigator-led Processing Systems spread across the U.S. The presentation covers how the ESDS Program continues to evolve and benefits from as well as contributes to advances in Earth Science Informatics.

Data Systems↗

Health-Enabled Smart Sensor Fusion Technology

A process was designed to fuse data from multiple sensors in order to make a more accurate estimation of the environment and overall health in an intelligent rocket test facility (IRTF), to provide reliable, high-confidence measurements for a variety of propulsion test articles. The object of the technology is to provide sensor fusion based on a distributed architecture. Specifically, the fusion technology is intended to succeed in providing health condition monitoring capability at the intelligent transceiver, such as RF signal strength, battery reading, computing resource monitoring, and sensor data reading. The technology also provides analytic and diagnostic intelligence at the intelligent transceiver, enhancing the IEEE 1451.x-based standard for sensor data management and distributions, as well as providing appropriate communications protocols to enable complex interactions to support timely and high-quality flow of information among the system elements.

Wang, Ray↗

A National Virtual Specimen Database for Early Cancer Detection

Access to biospecimens is essential for enabling cancer biomarker discovery. The National Cancer Institute's (NCI) Early Detection Research Network (EDRN) comprises and integrates a large number of laboratories into a network in order to establish a collaborative scientific environment to discover and validate disease markers. The diversity of both the institutions and the collaborative focus has created the need for establishing cross-disciplinary teams focused on integrating expertise in biomedical research, computational and biostatistics, and computer science. Given the collaborative design of the network, the EDRN needed an informatics infrastructure. The Fred Hutchinson Cancer Research Center, the National Cancer Institute,and NASA's Jet Propulsion Laboratory (JPL) teamed up to build an informatics infrastructure creating a collaborative, science-driven research environment despite the geographic and morphology differences of the information systems that existed within the diverse network. EDRN investigators identified the need to share biospecimen data captured across the country managed in disparate databases. As a result, the informatics team initiated an effort to create a virtual tissue database whereby scientists could search and locate details about specimens located at collaborating laboratories. Each database, however, was locally implemented and integrated into collection processes and methods unique to each institution. This meant that efforts to integrate databases needed to be done in a manner that did not require redesign or re-implementation of existing system

distributed↗

A Virtual Bioinformatics Knowledge Environment for Early Cancer Detection

Discovery of disease biomarkers for cancer is a leading focus of early detection. The National Cancer Institute created a network of collaborating institutions focused on the discovery and validation of cancer biomarkers called the Early Detection Research Network (EDRN). Informatics plays a key role in enabling a virtual knowledge environment that provides scientists real time access to distributed data sets located at research institutions across the nation. The distributed and heterogeneous nature of the collaboration makes data sharing across institutions very difficult. EDRN has developed a comprehensive informatics effort focused on developing a national infrastructure enabling seamless access, sharing and discovery of science data resources across all EDRN sites. This paper will discuss the EDRN knowledge system architecture, its objectives and its accomplishments.

knowledge systems↗

Lunar laser ranging data identification and management

Activity under the subject grant during the first half of fiscal year 1979 at the University of Texas at Austin is reported. Raw lunar laser ranging data submitted by McDonald Observatory, Fort Davis, Texas and by the Australian Division of National Mapping at Orroral Valley, Australia were processed. This processing includes the filtering of signal events from noise photons, normal point formation, data archive management, and data distribution. System-wide program maintenance and up-grade carried out wherever and whenever necessary. Lunar laser ranging data is being transmitted from Austin to Paris for the extraction of earth rotation information during the EROLD campaign.

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