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. The learned model predicts if a system-call is going to open a file fully, partially, or just for changing its rights.
%0 Conference Paper
%1 Fricke2010
%A Fricke, Peter
%A Jungermann, Felix
%A Morik, Katharina
%A Piatkowski, Nico
%A Spinczyk, Olaf
%A Stolpe., Marco
%B Proceedings of LWA2010 - Workshop-Woche: Lernen, Wissen & Adaptivitaet
%C Kassel, Germany
%D 2010
%E Atzmüller, Martin
%E Benz, Dominik
%E Hotho, Andreas
%E Stumme, Gerd
%K users personalization adaptive behaviour mobiledevices useroriented mobile
%T Towards Adjusting Mobile Devices to User's Behaviour
%U http://www.kde.cs.uni-kassel.de/conf/lwa10/papers/kdml14.pdf
%X 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. The learned model predicts if a system-call is going to open a file fully, partially, or just for changing its rights.
@inproceedings{Fricke2010,
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. The learned model predicts if a system-call is going to open a file fully, partially, or just for changing its rights.},
added-at = {2011-01-11T13:32:49.000+0100},
address = {Kassel, Germany},
author = {Fricke, Peter and Jungermann, Felix and Morik, Katharina and Piatkowski, Nico and Spinczyk, Olaf and Stolpe., Marco},
biburl = {https://www.bibsonomy.org/bibtex/21edc1c16ecd61b623d51c4662a38e636/enitsirhc},
booktitle = {Proceedings of LWA2010 - Workshop-Woche: Lernen, Wissen {\&} Adaptivitaet},
crossref = {lwa2010},
editor = {Atzmüller, Martin and Benz, Dominik and Hotho, Andreas and Stumme, Gerd},
end = {2010-10-05 14:30:00},
interhash = {fe251b55ce5825ca882755e2ec4ff14b},
intrahash = {1edc1c16ecd61b623d51c4662a38e636},
keywords = {users personalization adaptive behaviour mobiledevices useroriented mobile},
room = {0446},
session = {joint5},
start = {2010-10-05 14:00:00},
timestamp = {2011-01-11T13:32:49.000+0100},
title = {Towards Adjusting Mobile Devices to User's Behaviour},
track = {kdml},
url = {http://www.kde.cs.uni-kassel.de/conf/lwa10/papers/kdml14.pdf},
year = 2010
}