Automated Extraction of Stateful Power Models for Cyber Foraging Systems: Unterschied zwischen den Versionen
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|kurzfassung=Mobile devices are strongly resource-constrained in terms of computing and battery capacity. Cyber-foraging systems circumvent these constraints by offloading a task to a more powerful system in close proximity. Offloading itself induces additional workload and thus additional power consumption on the mobile device. Therefore, offloading systems must decide whether to offload or to execute locally. Power models, which estimate the power consumption for a given workload can be helpful to make an informed decision. | |||
Recent research has shown that various hardware components such as wireless network interface cards (WNIC), cellular network interface cards or GPS modules have power states, that is, the power consumption behavior of a hardware component depends on the current state. Power models that consider power states | |||
(stateful power models) can be modeled as Power State Machines (PSM). For systems with multiple power states, stateful models proved to be more accurate than models that do not consider power states (stateless models). | |||
Manually generating PSMs is time-consuming and limits the practicability of PSMs. Therefore, in this thesis, we explore the possibility of automatically generating PSMs. The contribution of this thesis is twofold: (1) We introduce an automated measurementbased profiling approach (2) and we introduce a step-based approach, which, provided with profiling data, automatically extracts PSMs along with tail states and state transitions. | |||
We evaluate the automated PSM extraction in a case study on an offloading speech recognition system. We compare the power consumption prediction accuracy of the generated PSM with the prediction accuracy of a stateless regression based model. | |||
Because we measure the power consumption of the whole system, we use along with all WiFi power models the same CPU power model in order to predict the power consumption of the whole system. We find that a slightly adapted version of the | |||
generated PSM predicts the power consumption with a mean error of approx. 3% and an error of approx. 2% in the best case. In contrast, the regression model produces a mean error of | |||
approx. 19% and an error of approx. 18% in the best case. | |||
}} | }} | ||
Aktuelle Version vom 4. April 2018, 18:18 Uhr
| Vortragende(r) | Nadim Hammoud | |
|---|---|---|
| Vortragstyp | Bachelorarbeit | |
| Betreuer(in) | Christian Stier | |
| Termin | Fr 27. April 2018 | |
| Vortragsmodus | ||
| Kurzfassung | Mobile devices are strongly resource-constrained in terms of computing and battery capacity. Cyber-foraging systems circumvent these constraints by offloading a task to a more powerful system in close proximity. Offloading itself induces additional workload and thus additional power consumption on the mobile device. Therefore, offloading systems must decide whether to offload or to execute locally. Power models, which estimate the power consumption for a given workload can be helpful to make an informed decision.
Recent research has shown that various hardware components such as wireless network interface cards (WNIC), cellular network interface cards or GPS modules have power states, that is, the power consumption behavior of a hardware component depends on the current state. Power models that consider power states (stateful power models) can be modeled as Power State Machines (PSM). For systems with multiple power states, stateful models proved to be more accurate than models that do not consider power states (stateless models). Manually generating PSMs is time-consuming and limits the practicability of PSMs. Therefore, in this thesis, we explore the possibility of automatically generating PSMs. The contribution of this thesis is twofold: (1) We introduce an automated measurementbased profiling approach (2) and we introduce a step-based approach, which, provided with profiling data, automatically extracts PSMs along with tail states and state transitions. We evaluate the automated PSM extraction in a case study on an offloading speech recognition system. We compare the power consumption prediction accuracy of the generated PSM with the prediction accuracy of a stateless regression based model. Because we measure the power consumption of the whole system, we use along with all WiFi power models the same CPU power model in order to predict the power consumption of the whole system. We find that a slightly adapted version of the generated PSM predicts the power consumption with a mean error of approx. 3% and an error of approx. 2% in the best case. In contrast, the regression model produces a mean error of approx. 19% and an error of approx. 18% in the best case. | |