Yogesh Simmhan, Maria Nieto-Santisteban, Roger Barga, Tamas Budavari, Laszlo Dobos, Nolan Li, Michael Shipway, Alexander S. Szalay, Ani Thakar, Jan Vandenberg, Alainna Wonders, Sue Werner, Richard Wilton, Dan Fay, Michael Thomassy, Catharine van Ingen, Jim Heasley, and Conrad Holmberg
Big data presents new challenges to both cluster infrastructure software and parallel application design. We present a set of software services and design principles for data intensive computing with petabyte data sets, named GrayWulf†. These services are intended for deployment on a cluster of commodity servers similar to the well-known Beowulf clusters. We use the Pan-STARRS system currently under development as an example of the architecture and principles in action.
|Published in||Hawaii International Conference on System Sciences (HICSS)|
|Publisher||IEEE Computer Society|
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Yogesh Simmhan, Roger Barga, Catharine van Ingen, Maria Nieto-Santisteban, Laszlo Dobos, Nolan Li, Michael Shipway, Alexander S. Szalay, Sue Werner, and Jim Heasley. GrayWulf: Scalable Software Architecture for Data Intensive Computing, Microsoft Research, 15 September 2008.
Maria Nieto-Santisteban, Yogesh Simmhan, Roger Barga, Laszlo Dobos, Jim Heasley, Conrad Holmberg, Nolan Li, Michael Shipway, Alexander S. Szalay, Catharine van Ingen, and Sue Werner. Pan-STARRS: Learning to Ride the Data Tsunami, December 2008.