RT journal article
T1 Multi-Signal Multifractal Detrended Fluctuation Analysis for Uncertain Systems —Application to the Energy Consumption of Software Programs in Microcontrollers
A1 Torre Macías, Juan Carlos de la
A1 Pavón Domínguez, Pablo
A1 Dorronsoro Díaz, Bernabé
A1 Galindo Riaño, Pedro Luis
A1 Ruiz Villalobos, Patricia
A2 Ingeniería Informática
A2 Ingeniería Mecánica y Diseño Industrial
K1 multifractal analysis
K1 uncertainty
K1 energy consumption
K1 software characterization
K1 green computing
AB Uncertain systems are those wherein some variability is observed, meaning that different observations of the system will produce different measurements. Studying such systems demands the use of statistical methods over multiple measurements, which allows overcoming the uncertainty, based on the premise that a single measurement is not representative of the system’s behavior. In such cases, the current multifractal detrended fluctuation analysis (MFDFA) method cannot offer confident conclusions. This work presents multi-signal MFDFA (MS-MFDFA), a novel methodology for accurately characterizing uncertain systems using the MFDFA algorithm, which enables overcoming the uncertainty of the system by simultaneously considering a large set of signals. As a case study, we consider the problem of characterizing software (Sw) consumption. The difficulty of the problem mainly comes from the complexity of the interactions between Sw and hardware (Hw), as well as from the high uncertainty level of the consumption measurements, which are affected by concurrent Sw services, the Hw, and external factors such as ambient temperature. We apply MS-MFDFA to generate a signature of the Sw consumption profile, regardless of the execution time, the consumption levels, and uncertainty. Multiple consumption signals (or time series) are built from different Sw runs, obtaining a high frequency sampling of the instant input current for each of them while running the Sw. A benchmark of eight Sw programs for analysis is also proposed. Moreover, a fully functional application to automatically perform MS-MFDFA analysis has been made freely available. The results showed that the proposed methodology is a suitable approximation for the multifractal analysis of a large number of time series obtained from uncertain systems. Moreover, analysis of the multifractal properties showed that this approach was able to differentiate between the eight Sw programs studied, showing differences in the temporal scaling range where multifractal behavior is found.
PB MDPI
SN 2504-3110
YR 2023
FD 2023-10-30
LK http://hdl.handle.net/10498/31665
UL http://hdl.handle.net/10498/31665
LA eng
NO This publication was supported by the projects eFracWare [TED2021-131880B-I00] funded by the Spanish MCIN and the European Union “NextGenerationEU”/PRTR on MCIN/AEI/10.13039/501100011033, and the project eMob [PID2022-137858OB-I00] funded by the Spanish MCIN, and the Agency and the European Regional Development on MCIN/AEI/10.13039/501100011033/FEDER, UE. This work was also supported by Junta de Andalucía and ERDF under contract P18-2399 (GENIUS), ERDF [OPTIMALE – FEDER-UCA18-108393], and the Spanish Ministerio de Ciencia, Innovación y Universidades and the ERDF [NEMOVISION PID2019-109465RB-I00].J.C. de la Torre would like to acknowledge the Spanish Ministerio de Ciencia, Innovación y Universidades for the support through FPU grant [FPU17/00563]. B. Dorronsoro and P. Ruiz acknowledge “ayuda de recualificación” funding by Ministerio de Universidades and the European Union-NextGenerationEU.
DS Repositorio Institucional de la Universidad de Cádiz
RD 05-nov-2026