TY - GEN
T1 - BrezeFlow
T2 - 57th ACM/IEEE Design Automation Conference, DAC 2020
AU - Hoffman, Alexander
AU - Pathania, Anuj
AU - Kindt, Philipp H.
AU - Chakraborty, Samarjit
AU - Mitra, Tulika
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - Power management is quintessential to the successful deployment of edge devices, such as smartphones, in power-, thermal-, and energy-constrained environments. Governors and schedulers operate system sub-routines for power management at the edge. There exist several tools for debugging power issues in Android applications. However, there exists no tool to identify and classify inevitable misdecisions by power managers, given their often inefficient underlying heuristics. In this work, we introduce the first tool - BrezeFlow - designed for unified (scheduling and frequency scaling) power debugging of CPU power managers on Android edge devices. BrezeFlow enables kernel developers to evaluate designs of their power managers retrospectively with closed-source applications in real-world scenarios based on any user-defined strategy and thereby gain insights for better future governor designs. BrezeFlow detected an average of 815 misdecisions per second for the commonly deployed duo, ondemand governor and Completely Fair Scheduler, on mobile edge devices running popular applications.
AB - Power management is quintessential to the successful deployment of edge devices, such as smartphones, in power-, thermal-, and energy-constrained environments. Governors and schedulers operate system sub-routines for power management at the edge. There exist several tools for debugging power issues in Android applications. However, there exists no tool to identify and classify inevitable misdecisions by power managers, given their often inefficient underlying heuristics. In this work, we introduce the first tool - BrezeFlow - designed for unified (scheduling and frequency scaling) power debugging of CPU power managers on Android edge devices. BrezeFlow enables kernel developers to evaluate designs of their power managers retrospectively with closed-source applications in real-world scenarios based on any user-defined strategy and thereby gain insights for better future governor designs. BrezeFlow detected an average of 815 misdecisions per second for the commonly deployed duo, ondemand governor and Completely Fair Scheduler, on mobile edge devices running popular applications.
UR - https://www.scopus.com/pages/publications/85093920802
U2 - 10.1109/DAC18072.2020.9218542
DO - 10.1109/DAC18072.2020.9218542
M3 - Conference contribution
AN - SCOPUS:85093920802
T3 - Proceedings - Design Automation Conference
BT - 2020 57th ACM/IEEE Design Automation Conference, DAC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 20 July 2020 through 24 July 2020
ER -