Rethinking the Mobile Code Offloading Paradigm: From Concept to Practice
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Rethinking the Mobile Code Offloading Paradigm: From Concept to Practice by José I. Benedetto Andrés Neyem Jaime Navón Guillermo Valenzuela. MobileSoft 2017, Buenos Aires.
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Rethinking the Mobile Code Offloading Paradigm: From Concept to Practice
- 1. Rethinking the Mobile Code Offloading Paradigm: From Concept to Practice José I. Benedetto Pontificia Universidad Católica de Chile Engineering School Computer Science Department Andrés Neyem Jaime Navón Guillermo Valenzuela*
- 2. Outline ● Introduction ● Proposed Platform ● Evaluation ● Conclusions & Future Work
- 3. Introduction
- 4. Mobile Trends ● Resource-intensive apps ● Machine Learning from Cloud to Mobile1 ● Advanced hardware Negative impact in battery How can we address this issue? 1 Google I/O 2017, Android Meets TensorFlow: How to Accelerate Your App with AI
- 5. Code Offloading Technique by which resource-intensive tasks are transparently delegated to an off-site resource-rich surrogate.
- 6. Code Offloading Issues in existing proposals: ● Reproducibility ● Scalability ● Reliability None of them is currently being used in real apps
- 7. Proposed Platform: MobiCOP Mobile Computation Offloading Platform
- 8. MobiCOP: Mobile Computation Offloading Platform ● Designed to minimize execution time and power consumption. ● Self-contained library. ● Programming interface inspired by Android native components. ● Predictive decision engine.
- 10. MobiCOP: Decision Engine ● QoS Component ○ Tracks network status ● Code Profiler ○ Predicts execution time
- 11. Evaluation
- 12. Experiments ● Five benchmarks ○ Pure Java Computation (N-Queens) ○ NDK Computation (Chess Game) ○ Large I/O Computation (Video Transcoding) ○ OpenGL Computation (Image Processing) ○ Renderscript Computation (Mandelbrot Fractal) ● Hardware ○ Lenovo A319 dual-core 1.3GHz CPU and 512MB of RAM ○ Server in AWS t2.medium instance running Genymotion for AWS ○ Monsoon Power Monitor
- 13. Results
- 14. Results
- 15. Experiments All good under controlled conditions… …but what about unreliable environments, as in the real mobile context?
- 16. Experiments ● Performance under unreliable network ○ Charles Proxy Software ● Two benchmarks ○ Pure Java Computation ○ Large I/O Computation
- 17. Results
- 18. Results
- 19. Conclusions
- 20. Summary ● We presented a new code offloading platform ○ Can be used without changing the OS ○ Up to 11x speedup and less energy consumption ○ Good performance in unreliable conditions ○ Simple programming interface ● Use Cases ○ Demanding tasks such as Machine Learning, Image Processing, etc ● Future Work ○ Focused on the Elastic Cloud Daemon to improve autonomic computing
- 21. Acknowledgements
- 22. Acknowledgements This work was partially supported by a DCC-UC Research Grant, Graduate School of Engineering UC, AWS Cloud Credits for Research, and the CONICYT-PCHA/National PhD/2016 – Nº 21161015 Grant. Finally, we would like to thank Prof. Oscar Loyola (DUOC-UC) and all the students from the IIC3380 Mobile Platforms course at Pontificia Universidad Católica de Chile who were involved in this research project.
- 23. Contact Info José Benedetto, Andrés Neyem, Jaime Navón, Guillermo Valenzuela {jibenede, aneyem, jnavon, gevalenz}@uc.cl