A platform for automating battery-driven batch benchmarking and profiling of Android-based mobile devices
| Autor: | Alejandro Zunino, Juan Manuel Toloza, Cristian Mateos, Matías Hirsch |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Battery (electricity)
Profiling (computer programming) 0209 industrial biotechnology Computer science business.industry 020208 electrical & electronic engineering 02 engineering and technology Benchmarking 020901 industrial engineering & automation Hardware and Architecture Modeling and Simulation Embedded system Component (UML) 0202 electrical engineering electronic engineering information engineering Benchmark (computing) Enhanced Data Rates for GSM Evolution Android (operating system) business Mobile device Software |
| Zdroj: | Simulation Modelling Practice and Theory. 109:102266 |
| ISSN: | 1569-190X |
| DOI: | 10.1016/j.simpat.2020.102266 |
| Popis: | In-laboratory mobile device data gathering is useful to support fields of study that rely on data derived from mobile devices as elementary research input. Particularly, Dew Computing, a sub-area of mobile distributed computing, aims at scavenging idle computing resources from mobile devices at the edge. To produce repeatable experiments for developed Dew approaches, simulation of relevant mobile device aspects is an acceptable practice, being battery behavior one of such aspects. Our recently-proposed DewSim simulation toolkit uses a trace-based approach to model battery behavior realistically. However, to generically characterize the impact of different device components – e.g., CPU at different usages – on battery behavior, it is necessary to easily capture battery traces, and run benchmarks to quantify computing capabilities. Considering that traces are captured during long charging or discharging cycles, such data gathering duty is tedious and time-consuming and no tool has been proposed yet to automate it. To fill this gap, we propose a platform that leverages common IoT hardware to control battery state of devices subject to pre-configured profiling/benchmarking plans. The platform has a server-side component to manage benchmark/profiling executions using one out of two possible operation modes (exclusive or shared), and an extensible Android application that implements the benchmark and profiling logic to be run on devices. We conclude that the operation modes represent a clear trade-off between benchmark/profile execution time and IoT hardware cost. From validation experiments, we also conclude that using our platform to run a benchmark does not introduce a considerable performance and energy footprint compared to running the same benchmark as a plain Android application. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect