att_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 po-
tentially complex interaction with the application behavior. We de-
sign and implement ARO, the mobile Application ResourceOptimizer,
the first tool that efficiently and accurately exposes the cross-layer
interaction among various layers including radio resource chan-
nel state, transport layer, application layer, and the user interac-
tion 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 applica-
tion 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 con-
tent prefetching behavior.}},
	att_authors={ag1971, ss2864, os1872},
	att_categories={C_NSS.7, C_NSS.18},
	att_copyright_notice={{(c) ACM, 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proc of ACM MobiSys {{, 2011-06-27}}.}},
	att_tags={Smartphone Applications,  Radio Resource Optimization,  Cross- layer Analysis,  RRC state machine,  UMTS,  3G Networks},
	author={Feng Qian and Zhaoguang Wang and Alexandre Gerber and Z. Morley Mao and Subhabrata Sen and Oliver Spatscheck},
	institution={{in Proc. of ACM MobiSys}},
	title={{Profiling Resource Usage for Mobile Applications:
A Cross-layer Approach}},