Sean Hardman

Enabling earth science through cloud computing

Cloud Computing holds tremendous potential for missions across the National Aeronautics and Space Administration. Several flight missions are already benefiting from an investment in cloud computing for mission critical pipelines and services through faster processing time, higher availability, and drastically lower costs available on cloud systems. However, these processes do not currently extend to general scientific algorithms relevant to earth science missions. The members of the Airborne Cloud Computing Environment task at the Jet Propulsion Laboratory have worked closely with the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) mission to integrate cloud computing into their science data processing pipeline. This paper details the efforts involved in deploying a science data system for the CARVE mission, evaluating and integrating cloud computing solutions with the system and porting their science algorithms for execution in a cloud environment.

PDS4: A model-driven planetary science data architecture for long-term preservation

The goal of the Planetary Data System (PDS) is the digital preservation of scientific data for long-term use by the scientific research community. After two decades of successful operation, the PDS found itself in a new era of big data, international cooperation, distributed nodes, and multiple ways of analysing and interpreting data. A project was formed to develop a disciplined architectural approach that would drive the design and implementation of a scalable data system that could evolve to meet the demands of this new era. PDS4, the next generation system, uses an explicit model-driven architectural approach coupled with modern information technologies and standards to meet these challenges in order to ensure the planetary data assets can be mined for scientific knowledge for years to come.

A Scalable Planetary Science Information Architecture for Big Science Data

Research has shown that the amount of data now available often overwhelms key functions of an information system. This situation necessitates the design of information architectures that scale to meet the challenges. The Planetary Data System, a NASA funded project, has developed an information architecture for the planetary science community that addresses this and other big science data issues noted in a National Research Council report regarding architectures for big data management and analysis and end-to-end data lifecycle management across diverse disciplines. The report identified enabling technology trends including distributed systems, service-oriented architectures, ontologies, models and information representation, scalable database systems, federated data security mechanisms, and technologies for moving big data. This paper will present the PDS4 information architecture, its successful implementation in a multi-discipline big-data environment.

Architecting Scientific Data Systems in the Cloud

Scientists, educators, decision makers, students, and many others utilize scientific data produced by science instruments. They study our universe, make new discoveries in areas such as weather forecasting and cancer research, and shape policy decisions that impact nations fiscally, socially, economically, and in many other ways. Over the past 20 years or so, the data produced by these scientific instruments have increased in volume, complexity, and resolution, causing traditional computing infrastructures to have difficulties in scaling up to deal with them. This reality has led us, and others, to investigate the applicability of cloud computing to address the scalability challenges. NASA’s Jet Propulsion Laboratory (JPL) is at the forefront of transitioning its science applications to the cloud environment. Through the Apache Object Oriented Data Technology (OODT) framework, for NASA’s first software released at the open-source Apache Software Foundation (ASF), engineers at JPL have been able to scale the storage and computational aspects of their scientific data systems to the cloud – thus achieving reduced costs and improved performance. In this chapter, we report on the use of Apache OODT for cloud computing, citing several examples in a number of scientific domains. Experience, specific performance, and numbers are also reported. Directions for future work in the area are also suggested.