Profiling Resource Usage for Mobile Applications: A Cross-layer Approach Feng Qian University of Michigan Zhaoguang Wang University of Michigan Alexandre Gerber AT&T Labs Research Z. Morley Mao University of Michigan Subhabrata Sen AT&T Labs Research Oliver Spatscheck AT&T Labs Research 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. Categories and Subject Descriptors C.2.1 [Computer-Communication Networks]: Network Architecture and Design – Wireless Communication; C.4 [Performance of Systems]: Measurement Techniques General Terms Algorithms, Design, Measurement, Performance Keywords Smartphone Applications, Radio Resource Optimization, Crosslayer Analysis, RRC state machine, UMTS, 3G Networks 1. INTRODUCTION Increasingly ubiquitous cellular data network coverage gives an enormous impetus to the…