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Astronomical photometry is the science of measuring the flux of a celestial object. Since its introduction in the 1970s the CCD has been the principle method of measuring flux to calculate the apparent magnitude of an object. Each CCD image taken must go through a process of cleaning and calibration prior to its use. As the number of research telescopes increases the overall computing resources required for image processing also increases. As data archives increase in size to Petabytes, the data processing challenge requires image processing approaches to evolve to continue to exceed the growing data capture rate. Existing processing techniques are primarily sequential in nature, requiring increasingly powerful servers, faster disks and faster networks to process data. Existing High Performance Computing solutions involving high capacity data centres are both complex in design and expensive to maintain, while providing resources primarily to high profile science projects. This research describes three distributed pipeline architectures, a virtualised cloud based IRAF, the Astronomical Compute Node (ACN), a private cloud based pipeline, and NIMBUS, a globally distributed system. The ACN pipeline processed data at a rate of 4 Terabytes per day demonstrating data compression and upload to a central cloud storage service at a rate faster than data generation. The primary contribution of this research however is NIMBUS, which is rapidly scalable, resilient to failure and capable of processing CCD image data at a rate of hundreds of Terabytes per day. This pipeline is implemented using a decentralised web queue to control the compression of data, uploading of data to distributed web servers, and creating web messages to identify the location of the data. Using distributed web queue messages, images are downloaded by computing resources distributed around the globe. Rigorous experimental evidence is presented verifying the horizontal scalability of the system which has demonstrated a processing rate of 192 Terabytes per day with clear indications that higher processing rates are possible.
Doyle, F. P. (2015). Building a Scalable Global Data Processing Pipeline for Large Astronomical Photometric Datasets. Doctoral Thesis, Technological University Dublin. doi:10.21427/D7GW28