Appistry Enterprise Application Fabric at GeoEye: Implementing a Next-Generation Platform for Geospatial Intelligence
This case study profiles GeoEye – the leading provider of map-accurate commercial satellite imagery to the U.S. military and intelligence community – with a focus on why GeoEye chose Appistry Enterprise Application Fabric (Appistry EAF) to both reduce operational cost and complexity and enhance the company's agility in meeting the evolving needs of its customers.
About GeoEye
Formed as a result of the ORBIMAGE acquisition of Space Imaging in early 2006, GeoEye is the largest satellite commercial remote sensing company in the world. With three earth imaging satellites currently in orbit, GeoEye provides low, medium and high resolution images to governmental intelligence, national security and military organizations, as well as to commercial customers. In particular, the National Geospatial-Intelligence Agency (NGA) purchases approximately $60 million worth of images and products from GeoEye on an annual basis, and awarded GeoEye a staggering $500 million "NextView" contract for the development of a nextgeneration high-resolution remote sensing satellite. This satellite, known as GeoEye- 1, is set to launch from Vandenberg AFB, California in spring 2007 and will provide unprecedented levels of image resolution. From an orbit of 423 miles above Earth, GeoEye-1 will have a ground resolution of 0.41-meters, or about 16 inches. It will be the world's highest resolution commercial earth-imaging satellite and be able to collect some 700,000 sq. km. of imagery each day in panchromatic mode. That is equivalent to an area the size of Texas.
The Challenge: Reduce Infrastructure Cost & Complexity without Sacrificing Reliability, Enhance Competitive Agility
Reducing Cost & Complexity
Geospatial intelligence applications can generally be characterized as tackling intensive computations that involve unrelenting volumes of data. GeoEye's core, saleable asset is the imagery it gathers from the satellites it owns and operates. GeoEye takes massive amounts of raw image data from the satellites and processes it through a series of compute-intensive "ingest" applications that perform a variety of processing steps, such as image sharpening, compression and geocorrection. In order to handle increased data volume created by the launch of a higher resolution satellite and/or algorithm enhancements made to ingest applications without impacting the timely delivery of images to downstream applications, GeoEye must rely on its technology-based solutions to keep pace.
In the past GeoEye and others in its industry have lived by the motto that "big problems require big machines." That means that GeoEye has turned to high-end multi-processor supercomputers to run its mission-critical applications. However, this approach presents GeoEye with significant cost and complexity issues:
- Escalating infrastructure costs: With an initial price tag in the millions, and recurring maintenance fees in the hundreds of thousands, purchasing highend multi-processor servers to meet increasing requirements requires enormous up-front and ongoing infrastructure investments.
- Hardware obsolescence: GeoEye's applications are typically deployed longer than the hardware they are originally architected to run on, so system obsolescence—and the potential need to re-architect a deployed application when vendor support for the hardware platform ends—is a real concern.
- Complexity and risks of application development for multi-processor environments: Structuring applications to take advantage of symmetric multiprocessing (SMP) servers requires specialized development skills and prolonged development efforts.
- Platform rigidity: Expensive, high-end servers require accurate, upfront forecasting of capability and scalability needs, leaving little room for flexing to meet changing requirements as the business evolves.
Enhancing Competitive Agility
In addition to the challenge of reducing the cost and complexity of running its ingest applications, GeoEye faces constant pressure to "do more." This pressure is internally driven by the desire to differentiate itself from competitors and create new revenue streams, and is also driven by the general sense in the intelligence community that whatever is currently being done to "protect and defend" is not enough.
Once GeoEye's images are ingested, they are "exploited" by downstream applications that turn the images into actionable information. As with improving ingest applications, the high-end server approach creates barriers to GeoEye's ability to innovate new exploitation applications. Developers are forced to conceive of and architect applications within the constraints of the infrastructure's limitations, stifling the company's competitive agility.
