Profiling resource usage for mobile applications: a cross-layer approach

Abstract

Despite the popularity of mobile applications, their performance and energy bottlenecks remain hidden due to a lack of visibility into the resource-constrained mobile execution environment with potentially complex interaction with the application behavior. We design and implement ARO, the mobile Application Resource Optimizer, the first tool that efficiently and accurately exposes the cross-layer interaction among various layers including radio resource channel state, transport layer, application layer, and the user interaction layer to enable the discovery of inefficient resource usage for smartphone applications. To realize this, ARO provides three key novel analyses: (i) accurate inference of lower-layer radio resource control states, (ii) quantification of the resource impact of application traffic patterns, and (iii) detection of energy and radio resource bottlenecks by jointly analyzing cross-layer information. We have implemented ARO and demonstrated its benefit on several essential categories of popular Android applications to detect radio resource and energy inefficiencies, such as unacceptably high (46%) energy overhead of periodic audience measurements and inefficient content prefetching behavior.