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On-demand distributed image processing over an adaptive Campus-Grid

Caton, Simon James 2009. On-demand distributed image processing over an adaptive Campus-Grid. PhD Thesis, Cardiff University.

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This thesis explores how scientific applications, which are based upon short jobs (seconds and minutes) can capitalize upon the idle workstations of a Campus-Grid. These resources are donated on a voluntary basis, and consequently, the Campus-Grid is constantly adapting and the availability of workstations changes. Typically, to utilize these resources a Condor system or equivalent would be used. However, such systems are designed with different trade-offs and incentives in mind and therefore do not provide intrinsic support for short jobs. The motivation for creating a provisioning scenario for short jobs is that Image Processing, as well as other areas of scientific analysis, are typically composed of short running jobs, but still require parallel solutions. Much of the literature in this area comments on the challenges of performing such analysis efficiently and effectively even when dedicated resources are in use. The main challenges are: latency and scheduling penalties, granularity and the potential for very short jobs. A volunteer Grid retains these challenges but also adds further challenges. These can be summarized as: unpredictable re source availability and longevity, multiple machine owners and administrators who directly affect the operating environment. Ultimately, this creates the requirement for well conceived and effective fault management strategies. However, these are typically not in place to enable transparent fault-free job administration for the user. This research demonstrates that these challenges are answerable, and that in doing so opportunistically sourced Campus-Grid resources can host disparate applications constituted of short running jobs, of as little as one second in length. This is demonstrated by the significant improvements in performance when the system presented here was compared to a well established Condor system. Here, improvements are increased job efficiency from 60–70% to 95%–100%, up to a 99% reduction in application makespan and up to a 13000% increase in the efficiency of resource utilization. The Condor pool in use is approximately 1,600 workstations distributed across 27 administrative domains of Cardiff University. The application domain of this research is Matlab-based image processing, and the application area used to demonstrate the approach is the analysis of Magnetic Resonance Imagery (MRI). However, the presented approach is generalizable to any application domain with similar characteristics.

Item Type: Thesis (PhD)
Status: Unpublished
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
ISBN: 9781303217852
Date of First Compliant Deposit: 30 March 2016
Last Modified: 09 Jan 2018 22:58

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