Dan Fay

TL;DR: In this paper, the authors propose a cloud-based infrastructure for the geospatial sciences that can support discovery, access and utilization of data and data processing so as to relieve scientists and engineers of IT tasks and focus on scientific discoveries.

TL;DR: The architecture for a three tier commodity component cluster designed for a range of data intensive computations operating on petascale data sets named GrayWulf, a balanced system in terms of IO performance and memory size, according to Amdahl's Laws is presented.

TL;DR: The challenges of scheduling workflows on GrayWulf are examined, algorithms to improve performance are presented, and early results from applying Trident to schedule data loading workflows are presented for an actual e-Science project.

TL;DR: Two of the experiences over the last year are presented — the Terapixel Project, using workflow, high-performance computing and non-structured query language data processing to render the largest astronomical image for the WorldWide Telescope, and MODISAzure, a science pipeline for image processing, deployed using the Azure Cloud infrastructure.

TL;DR: The development of a client for the NASA Spatial Web Portal using Microsoft Bing Maps AJAX APIs is introduced, developed based on Microsoft's AJAX plug-ins for the Bing Maps with a) Bing Maps serving as the background map for the SWP, and b) Bing maps controlling visualization of service data.