Profiling resource usage for mobile applications: a cross-layer approach
By Feng Qian, Zhaoguang Wang, Alexandre Gerber, Z. Morley Mao, Subhabrata Sen, Oliver Spatscheck. ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services, 2011
299 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/34164c085d4b74bb2e3a61bc6ed8f6bd59e878cd
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
- Subhabrata Sen
- Oliver Spatscheck
- Feng Qian
- David Chu
- Aman Kansal
- Hojung Cha
- Alexandre Gerber
- Deepak Ganesan
In Electrical and Electronic Engineering
- Z. Morley Mao
- Feng Qian
- Subhabrata Sen
- Oliver Spatscheck
- Junxian Huang
- Alexandre Gerber
- Aman Kansal
- Hojung Cha
Citations in the corpus
- An in-depth study of LTE: effect of network protocol and application behavior on performance (2013, background): “A delayed FIN or RESET packet will incur additional radio-on time and one additional off-on switch if the delay is longer than 10 seconds, leading to waste of device energy [26].”
- Fast app launching for mobile devices using predictive user context (2012, background): “Unfortunately, interacting with apps can be surprisingly sluggish.”
- A variegated look at 5G in the wild: performance, power, and QoE implications (2021, background): “Prior studies have built power models for 3G [47, 48, 55, 65] and 4G/LTE [26, 31, 42].”
- Where is the energy spent inside my app?: fine grained energy accounting on smartphones with Eprof (2012, background): “, for specific applications on mobile systems [56, 57], for a specific protocol [58, 59], via offloading [39, 40], and via delaying communication [60].”
- A close examination of performance and power characteristics of 4G LTE networks (2012, methodology): “ARO [28] employs an approach similar to [35] but focusing only on radio power usage.”
- AppScope: Application Energy Metering Framework for Android Smartphone Using Kernel Activity Monitoring (2012, background): “The state switches to the low power state, FACH, when data communication starts, and remains in the high power state, DCH, while data is being sent or received [23].”
- 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.”