Global Services for Internet-Scale e-Science
Matt Welsh, Harvard University

Many scientific fields are increasingly computational and dependent
upon diverse sources of digital data. Astronomy, physics, biology,
geology, environmental sciences, and public health are all drawing upon
networked information sources of both archived and real-time digital
data to undertake studies of unprecedented scope. To date, however,
these fields have had to resort to building their own infrastructure
from the ground up to support their research. As an example, the
NSF-funded EarthScope initiative is archiving data from thousands of 
GPS receivers deployed across the North American continent to track the
movement of plates at resolutions of millimeters per year. However, the
infrastructure used for this purpose amounts to a central FTP server
hosted in Colorado from which researchers manually download data sets
for local processing.

We argue that the distributed systems community should be partnering
directly with researchers in the natural sciences to address the pressing
need of a large-scale networked infrastructure for data-intensive
"e-Science" applications. Such an approach could enable significant
scientific investigations that are not possible today due to the necessity
of building significant networking and systems infrastructure for
individual experiments. This vision also differs substantially from the
many Grid efforts, which are primarily focused on providing computational
resources to cycle-intensive simulations.  The problem of locating,
indexing, archiving, and querying vast numbers of scientific data sources
is still largely unaddressed. 

The Internet provides low-level, physical connectivity between computers,
the Web provides a user interface, and Web Service protocols enable
a programmatic structure for building applications. Still, domain
scientists demand much richer interfaces that can enable a meaningful
interchange across different data formats; the ability to name data
sources according to physical, logical, and spatiotemporal attributes;
annotation and tracking of data provenance; and, finally, the ability to
scale to an ever-increasing number of real-time and archived repositories
of digital data that will eventually be tied into such an infrastructure.
Much in the way that search engines have unified access to static
content found on the Web, we envision a common, scalable framework for
data-intensive applications that can unify researcher's access to
science data. 

As an example, consider a study of climactic processes and the effect
of industrial pollutants on the environment. Such a study can integrate
data from ground-, ocean-, and air-based sensors of pollutants and
environmental conditions; satellite imagery; and historical databases
of weather observations. Currently, locating and accessing these diverse
data sources is hampered by the lack of a common data management 
infrastructure. Simply placing archives of data on the Web is not enough
to satisfy the complex process of locating and processing this data.
Supporting a large number of simultaneous applications also raises many 
challenges for resource management and scalability. An environmental 
scientist should not have to become an expert in network protocols and 
distributed systems programming to do her research!

The database community is starting to look at many of these challenges,
although we believe these efforts are hampered by system designs that
call for central processing of streaming data, as well as an SQL-centric
vision for the query interface. We believe that this effort will require
input from experts in databases, languages, theory, AI, systems, 
and networking, as well as forming effective partnerships with the 
natural science communities. In particular, the computer science
research agenda must be informed by the needs of the "hard science"
problems for which this infrastructure will support. We believe that the
space of systems problems here is very rich, and that focusing our 
efforts on supporting large-scale scientific research can reenliven the
distributed systems community with a new purpose that is intimately
linked to the many scientific discoveries that this research will support.