We propose a hybrid, long-lived network connection mechanism that alternates
between polling and persistent operation modes based on the activity profile of
network-enabled printing devices. Our Machine Learning method is able to predict
“printing peaks”, proactively alternating the connection mode back and forth from
polling (for those moments of low activity) to persistent model (for high demanding
usage scenarios). This strategy combines the advantages of low consumption, low cost
polling connections, and at the same time provides low response times offered by
persistent connections. Our solution minimizes costs and maximizes network usage and
performance, respecting the availability requirement thanks to its prediction capability.
Besides the fact that our solution was thought for printers, it can also be applied for other
devices that require optimized connection management.
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INC, HP, "AUTOMATIC PRINTER PERSISTENT CONNECTION MANAGEMENT BY MEANS OF MACHINE LEARNING", Technical Disclosure Commons, (August 20, 2019)