Anatomizing application performance differences on smartphones

By Junxian Huang, Qiang Xu, B. Tiwana, Z. Morley Mao, Ming Zhang, Paramvir Victor Bahl. ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services, 2010

332 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/94e8efe1ed95d3b58a16b5a695789b9d1f701a3a

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

In Electrical and Electronic Engineering

Citations in the corpus

LiveLab (2011, background): “The authors of [21] developed a smartphone software (3GTest) for mobile users to measure and report cellular network performance.”
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.”
A measurement-based study of MultiPath TCP performance over wireless networks (2013, background): “Furthermore, since cellular 3G/4G antennas have state machines for radio resources allocation and management of energy consumption, the state promotion delay (the time duration required to bring the antenna to ready state) is often longer than packet RTTs [11,12] and might significantly impact our s”
Profiling resource usage for mobile applications: a cross-layer approach (2011, background): “, 3gTest [19], LiveLab [28], and [14]) collect traces from real smartphone users, focusing on characterization at only IP and higher layers.”
An in-depth study of LTE: effect of network protocol and application behavior on performance (2013, methodology): “We also compare our re-sults with those presented in [14] and [31].”
A first look at traffic on smartphones (2010, background): “For instance, we can study how much total traf.c a device generates in a day and interaction of its traf.c patterns with radio power management.”
Heterogeneity in Mobile Cloud Computing: Taxonomy and Open Challenges (2014, background): “2) Performance Gain: User perceived performance from online mobile applications is highly influenced by computing performance of server and wireless networks in MCC [91].”
A close examination of performance and power characteristics of 4G LTE networks (2012, methodology): “Using a publicly deployed tool we designed for Android called 4GTest attracting more than 3000 users within 2 months and extensive local experiments, we study the network performance of LTE networks and compare with other types of mobile networks.”