Where is the energy spent inside my app?: fine grained energy accounting on smartphones with Eprof
By Abhinav Pathak, Y. C. Hu, Ming Zhang. European Conference on Computer Systems, 2012
732 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/d5b5382c657cc0282520508922ae98f90fbee5b8
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Ion Stoica
- Ramesh Govindan
- Zhong Ming
- Tian Guo
- William G. J. Halfond
- Sasu Tarkoma
- E. Lagerspetz
- Adam J. Oliner
In Electrical and Electronic Engineering
- Zhong Ming
- Tian Guo
- Ding Li
- William G. J. Halfond
- Ramesh Govindan
- Ion Stoica
- Anand Padmanabha Iyer
- Shuai Hao
Citations in the corpus
- Carat: collaborative energy diagnosis for mobile devices (2013, background): “Some prior work has aimed to understand energy use by employing a combination of hardware, OS, and app source code or binary instrumentation [11, 23, 32, 44].”
- Estimating mobile application energy consumption using program analysis (2013, background): “A complementary approach [16], [17] explicitly models the state transitions between hardware power states of smartphone hardware components (CPU, WiFi, GPS etc.”
- AppInsight: Mobile App Performance Monitoring in the Wild (2012, unknown): “Several researchers [17, 14] have studied energy consumption of mobile apps and have collected execution traces for that purpose.”
- Measuring energy consumption for short code paths using RAPL (2012, methodology): “Such models are used to schedule applications and resources [11], to adapt application behavior to externally specified energy constraints [4], and to account energy usage to the respective software components [12].”
- Privacy Protection for Preventing Data Over-Collection in Smart City (2016, background): “Furthermore, these solutions need to run tools or other apps to monitor and detect the data over-collection behaviors, which consumes more energy of smartphones [10].”
- Cloud-Based or On-Device: An Empirical Study of Mobile Deep Inference (2017, background): “To better understand the performance and power characteristics of modern mobile applications, researchers have developed a number of performance monitor tools, such as 3GTest [29], 4GTest [30], AppInsight [31], eprof [32], and Trepn [24] over the past decade.”