Towards adjusting mobile devices to user’s behaviour

Abstract. Mobile devices are a special class of resource-constrained em-bedded devices. Computing power, memory, the available energy, and network bandwidth are often severely limited. These constrained re-sources 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, load-ing files ideally starts before the user wants to access the file. So-called prefetching strategies optimize system’s operation. Our goal is to ad-just 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 ana-lyze 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