Abstract
Dalvik virtual machine in the Android system creates a profiling barrier between VM-space applications and Linux user-space libraries. It is difficult for existing profiling tools on the Android system to definitively identify whether a bottleneck occurred in the application level, the Linux user-space level, or the Linux kernel level. Information barriers exist between VM-space applications and Linux native analysis tools due to runtime virtual machines' dynamic memory allocation mechanism. Furthermore, traditional vertical profiling tools targeted for Java virtual machines cannot be simply applied on the Dalvik virtual machine due to its unique design. The proposed the Reconfigurable Vertical Profiling Framework bridges the information gap and streamlines the hardware-software co-design process for the Android runtime system.
- B. Alpern, S. Augart, S. M. Blackburn, M. Butrico, A. Cocchi, P. Cheng, J. Dolby, S. Fink, D. Grove, M. Hind, K. S. McKinley, M. Mergen, J. E. B. Moss, T. Ngo, V. Sarkar, and M. Trapp. 2005. The Jikes Research Virtual Machine project: Building an open-source research community. IBM Syst. J. 44, 2, 399--417. Google Scholar
Digital Library
- B. Alpern, C. R. Attanasio, A. Cocchi, D. Lieber, S. Smith, T. Ngo, J. J. Barton, S. F. Hummel, J. C. Shepherd, and M. Mergen. 1999. Implementing jalapeño in Java. In Proceedings of the 14th ACM SIGPLAN Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA). ACM Press, 314--324. Google Scholar
Digital Library
- L. Batyuk, A.-D. Schmidt, H.-G. Schmidt, A. Camtepe, and S. Albayrak. 2009. Developing and benchmarking native Linux applications on Android. In MobileWireless Middleware, Operating Systems, and Applications. J.-M. Bonnin, C. Giannelli, and T. Magedanz, Eds., Springer Berlin, 381--392.Google Scholar
- W. Binder, J. Hulaas, and P. Moret 2006. A quantitative evaluation of the contribution of native code to Java workloads. In Proceedings of the IEEE International Symposium on Workload Characterization. 201--209.Google Scholar
Cross Ref
- W. Binder, J. Hulaas, and P. Moret. 2007. Advanced Java bytecode instrumentation. In Proceedings of the 5th International Symposium on Principles and Practice of Programming in Java. ACM Press, 135. Google Scholar
Digital Library
- C.-W. Chang, C.-Y. Lin, C.-T. King, Y.-F. Chung, and S.-Y. Tseng. 2010. Implementation of JVM tool interface on Dalvik virtual machine. In Proceedings of the International Symposium on VLSI Design Automation and Test (VLSI-DAT). IEEE, 143--146.Google Scholar
Cross Ref
- W. Cohen. 2004. Tuning programs with Oprofile. Wide Open Mag. 53.Google Scholar
- G. Contreras and M. Martonosi. 2005. Power prediction for Intel XScale® processors using performance monitoring unit events. In Proceedings of the International Symposium on Low Power Electronics and Design. ACM Press, 221. Google Scholar
Digital Library
- J. Dongarra, R. Wade, and P. McMahan. Linpack Benchmark -- Java Version. http://www.netlib.org/benchmark/linpackjava/.Google Scholar
- Google. 2011. Dalvik - Code and documentation from Android's VM team - Google Project Hosting. http://code.google.com/p/dalvik/.Google Scholar
- Google. 2010. Using DDMS | Android Developers. http://developer.android.com/guide/developing/debugging/ddms.html.Google Scholar
- Google. 2008. Anatomy & Physiology of an Android - 2008 Google I/O Session Videos and Slides. http://sites.google.com/site/io/anatomy--physiology-of-an-android.Google Scholar
- M. Hauswirth, A. Diwan, P. F. Sweeney, and M. C. Mozer. 2005. Automating vertical profiling. In Proceedings of the 20th Annual ACM SIGPLAN Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA). ACM Press, 281. Google Scholar
Digital Library
- M. Hauswirth, P. F. Sweeney, A. Diwan, and M. Hind. 2004. Vertical profiling: Understanding the behavior of object-priented applications. ACM SIGPLAN Not. 39, 251--269. Google Scholar
Digital Library
- R. Hyndman. 2011. Newtonscradle - Android app to model the physics of Newton's Cradle - Google Project Hosting. http://code.google.com/p/newtonscradle/.Google Scholar
- H. Inoue, and T. Nakatani. 2009. How a Java VM can get more from a hardware performance monitor. ACM SIGPLAN Not. 44, 137--154. Google Scholar
Digital Library
- JSERV. 2010. 0×bench - Comprehensive Benchmark Suite for Android - Google Project Hosting. http://code.google.com/p/0xbench/.Google Scholar
- S. Khan, S. Khan, S. H. K. Banuri, M. Nauman, and M. Alam. 2009. Analysis of Dalvik virtual machine and class path library. Tech. rep. Security Engineering Research Group, Institute of Management Sciences, Peshawar, Pakistan.Google Scholar
- J. Maebe, D. Buytaert, L. Eeckhout, and K. De Bosschere. 2006. Javana: A system for building customized Java program analysis tools. ACM SIGPLAN Not. 41, 10, 153--168. Google Scholar
Digital Library
- H. Mousa, and C. Krintz. 2005. HPS: Hybrid profiling support. In Proceedings of the 14th International Conference on Parallel Architectures and Compilation Techniques (PACT'05). IEEE, 38--47. Google Scholar
Digital Library
- H. Mousa, K. Doshi, T. Sherwood, and E. Ould-Ahmed-Vall. 2010. VrtProf: Vertical profiling for system virtualization. In Proceedings of the 43rd Hawaii International Conference on System Sciences (HICSS). IEEE, 1--10. Google Scholar
Digital Library
- H. Mousa, C. Krintz, L. Youseff, and R. Wolski. 2007. VIProf: Vertically integrated full-system performance profiler. In Proceedings of the IEEE International Parallel and Distributed Processing Symposium. (IPDPS'07). IEEE, 1--6.Google Scholar
- D. Nicolaescu and A. Veidenbaum. 2005. Understanding and comparing the performance of optimized JVMs. In Proceedings of the Conference on Innovative Architecture for Future Generation High-Performance Processors and Systems. IEEE. Google Scholar
Digital Library
- Oracle. 2002. JVM(TM) Tool Interface 1.0.38. JVM Tool Interface. http://download.oracle.com/javase/1.5.0/docs/guide/jvmti/jvmti.html.Google Scholar
- Oracle. 2010. Java SE - Java Platform Debugger Architecture Home. http://java.sun.com/javase/technologies/core/toolsapis/jpda/.Google Scholar
- K. Paul and T. K. Kundu. 2010. Android on mobile devices: An energy perspective. In Proceedings of the IEEE 10th International Conference on Computer and Information Technology (CIT). 2421--2426. Google Scholar
Digital Library
- R. Pozo and B. Miller. 2004. Java SciMark 2.0. http://math.nist.gov/scimark2/.Google Scholar
- F. T. Schneider, M. Payer, and T. R. Gross. 2007. Online optimizations driven by hardware performance monitoring. ACM SIGPLAN Not. 42, 6, 373--382. Google Scholar
Digital Library
- L. Shannon and P. Chow. 2004. Using reconfigurability to achieve real-time profiling for hardware/software codesign. In Proceedings of the ACM/SIGDA 12th International Symposium on Field Programmable Gate Arrays. ACM, 190--199. Google Scholar
Digital Library
- P. F. Sweeney, M. Hauswirth, B. Cahoon, P. Cheng, A. Diwan, D. Grove, and M. Hind. 2004. Using hardware performance monitors to understand the behavior of Java applications. In Proceedings of the 3rd Conference on Virtual Machine Research And Technology Symposium. Vol. 3, USENIX Association, 5. Google Scholar
Digital Library
Index Terms
Reconfigurable vertical profiling framework for the android runtime system
Recommendations
Efficient hardware-based nonintrusive dynamic application profiling
Application profiling—the process of monitoring an application to determine the frequency of execution within specific regions—is an essential step within the design process for many software and hardware systems. Profiling is often a critical step ...
A Profiling System for Android Wear
MobiCASE'16: Proceedings of the 8th EAI International Conference on Mobile Computing, Applications and ServicesIn this poster paper, we describe the design and implementation of our system of profiling the behavior of Android Wear. The Android Wear device contains many kinds of sensors such as an accelerometer, a gyrometer, and a microphone. Therefore, profiling ...
Android: Changing the Mobile Landscape
The mobile phone landscape changed last year with the introduction of smart phones running Android, a platform marketed by Google. Android phones are the first credible threat to the iPhone market. Not only did Google target the same consumers as iPhone,...






Comments