Job profiling and queue management in high performance printing

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 become 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 basis. Nevertheless, these strategies are not optimized in terms of ensuring 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 Portable Document Format (PDF) jobs identifying their key components. This work introduces a PDF Profiler tool aimed at extracting information from jobs and some metrics to predict a job ripping cost based on its profile. This information is extremely useful to rasterize jobs in a clever way. The computational cost estimated using the information extracted by the PDF Profiler and the proposed metrics is useful for the print jobs queue management to improve the allocated RIPs load balance, resulting in a higher throughput for the ripping step. Experiments have been carried out in order to evaluate the PDF Profiler, the proposed metrics and their impact in the print jobs queue management.