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Bibkey Lernen, Wissen & Adaptivität (LWA 2010) -- Workshop Proceedings
Author Fricke, Peter and Jungermann, Felix and Morik, Katharina and Piatkowski, Nico and Spinczyk, Olaf and Stolpe, Marco
Ls8autor Jungermann, Felix
Morik, Katharina
Piatkowski, Nico
Stolpe, Marco
Editor http://www.kde.cs.uni-kassel.de/conf/lwa10/papers/kdml14.pdf
Booktitle Lernen, Wissen & Adaptivität (LWA 2010) -- Workshop Proceedings
Pages 51--58
Institution Mobile devices are a special class of resource-constrained embedded devices. Computing power, memory, the available energy, and network bandwidth are often severely limited. These constrained resources require extensive optimization of a mobile system compared to larger systems. Any needless operation has to be avoided. Time-consuming operations have to be started early on. For instance, loading files ideally starts before the user wants to access the file. So-called prefetching strategies optimize system's operation. Our goal is to adjust such strategies on the basis of logged system data. Optimization is then achieved by predicting an application's behavior based on facts learned from earlier runs on the same system. In this paper, we analyze system-calls on operating system level and compare two paradigms, namely server-based and device-based learning. The results could be used to optimize the runtime behaviour of mobile devices.
Abstract Mobile devices are a special class of resource-constrained embedded devices. Computing power, memory, the available energy, and network bandwidth are often severely limited. These constrained resources require extensive optimization of a mobile system compared to larger systems. Any needless operation has to be avoided. Time-consuming operations have to be started early on. For instance, loading files ideally starts before the user wants to access the file. So-called prefetching strategies optimize system's operation. Our goal is to adjust such strategies on the basis of logged system data. Optimization is then achieved by predicting an application's behavior based on facts learned from earlier runs on the same system. In this paper, we analyze system-calls on operating system level and compare two paradigms, namely server-based and device-based learning. The results could be used to optimize the runtime behaviour of mobile devices.
Note Resubmission of Fricke/etal/2010a
Year 2010
Url http://www.kde.cs.uni-kassel.de/conf/lwa10/papers/kdml14.pdf



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