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Job profiling in high performance printing
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Document Engineering archive
Proceedings of the 9th ACM symposium on Document engineering table of contents
Munich, Germany
SESSION: Document presentation (I) -- formatting, printing and layout table of contents
Pages 109-118  
Year of Publication: 2009
ISBN:978-1-60558-575-8
Authors
Thiago Nunes  PUCRS, Porto Alegre, Brazil
Fabio Giannetti  HP Laboratories, Palo Alto, CA, USA
Mariana Kolberg  PUCRS, Porto Alegre, Brazil
Rafael Nemetz  PUCRS, Porto Alegre, Brazil
Alexis Cabeda  HP Brazil, Porto Alegre, Brazil
Luiz Gustavo Fernandes  PUCRS, Porto Alegre, Brazil
Sponsors
SIGDOC: ACM Special Interest Group for Design of Communications
SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

Digital presses have consistently improved their speed in the past ten years. Meanwhile, the need for document personalization and customization has increased. As a consequence of these two facts, the traditional RIP (Raster Image Processing) process has became a highly demanding computational step in the print workflow. Print Service Providers (PSP) are now using multiple RIP engines and parallelization strategies to speed up the whole ripping process which is currently based on a per-page base. Nevertheless, these strategies are not optimized in terms of assuring the best Return On Investment (ROI) for the RIP engines. Depending on the input document jobs characteristics, the ripping step may not achieve the print-engine speed creating a unwanted bottleneck. The aim of this paper is to present a way to improve the ROI of PSPs proposing a profiling strategy which enables the optimal usage of RIPs for specific jobs features ensuring that jobs are always consumed at least at engine speed. The profiling strategy is based on a per-page analysis of input PDF jobs identifying their key components. This work introduces a profiler tool to extract information from jobs and some metrics to predict a job ripping cost based on its profile. This information is extremely useful during the job splitting step, since jobs can be split in a clever way. This improves the load balance of the allocated RIPs engines and makes the overall process faster. Finally, experimental results are presented in order to evaluate both, the profiler and the proposed metrics.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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