Rethinking the Mobile Code Offloading Paradigm: From Concept to Practice

May 23, 20170 likes118 views

Rethinking the Mobile Code Offloading Paradigm: From Concept to Practice by José I. Benedetto Andrés Neyem Jaime Navón Guillermo Valenzuela. MobileSoft 2017, Buenos Aires.

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*

Outline
● Introduction
● Proposed Platform
● Evaluation
● Conclusions & Future Work

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

Code Offloading
Technique by which resource-intensive
tasks are transparently delegated to an
off-site resource-rich surrogate.

Code Offloading
Issues in existing proposals:
● Reproducibility
● Scalability
● Reliability
None of them is currently being used in real apps

Proposed Platform: MobiCOP
Mobile Computation Offloading Platform

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.

MobiCOP: Decision Engine
● QoS Component
○ Tracks network status
● Code Profiler
○ Predicts execution time

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

Experiments
All good under controlled conditions…
…but what about unreliable environments, as in the real mobile context?

Experiments
● Performance under unreliable network
○ Charles Proxy Software
● Two benchmarks
○ Pure Java Computation
○ Large I/O Computation

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

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.

Contact Info
José Benedetto, Andrés Neyem, Jaime Navón, Guillermo Valenzuela
{jibenede, aneyem, jnavon, gevalenz}@uc.cl

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