Robust resource allocation in a cluster based imaging system

  • Authors:

  • Jay Smith;Vladimir Shestak;Howard Jay Siegel;Suzy Price;Larry Teklits;Prasanna Sugavanam

  • Affiliations:

  • DigitalGlobe, Longmont, CO 80503, USA and Dept. of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523-1373, USA;Dept. of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523-1373, USA and InfoPrint Solutions Company, Boulder, CO 80301, USA;Dept. of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523-1373, USA and Dept. of Computer Science, Colorado State University, Fort Collins, CO 80523-1373, USA;InfoPrint Solutions Company, Boulder, CO 80301, USA;InfoPrint Solutions Company, Boulder, CO 80301, USA;Dept. of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523-1373, USA

  • Venue:
  • Parallel Computing
  • Year:
  • 2009

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Abstract

Recently there has been an increased demand for imaging systems in support of high-speed digital printing. The required increase in performance in support of such systems can be accomplished through an effective parallel execution of image processing applications in a distributed cluster computing environment. The output of the system must be presented to a raster based display at regular intervals, effectively establishing a hard deadline for the production of each image. Failure to complete a rasterization task before its deadline will result in an interruption of service that is unacceptable. The goal of this research was to derive a metric for measuring robustness in this environment and to design a resource allocation heuristic capable of completing each rasterization task before its assigned deadline, thus, preventing any service interruptions. We present a mathematical model of such a cluster based raster imaging system, derive a robustness metric for evaluating heuristics in this environment, and demonstrate using the metric to make resource allocation decisions. The heuristics are evaluated within a simulation of the studied raster imaging system. We clearly demonstrate the effectiveness of the heuristics by comparing their results with the results of a resource allocation heuristic commonly used in this type of system.