The blessing mobile cloud has explored the wisdom of application development and deployment with techniques such as code offloading. While code offloading has been widely considered for reducing energy consumption and increasing responsiveness of mobile devices, the technique still faces many challenges pertaining to practical usage. Advances in mobile hardware and operating systems have made mobile a first-class development platform. However, developers are still constrained by the inherent resource limitations of mobile devices. And they are facing a hesitation with which way is the best to develop an application to reduce the energy consumption of a smartphone. Deciding to offload requires a careful consideration of the costs and benefits of a range of possible program partitions. This cost-benefit analysis depends on external factors, such as network conditions and the resources availability, as well as internal app properties, such as component dependencies, data representations, and code complexity. Thus, benefiting from offload requires some assistance from developers. In this article, I adopt a systemic approach for analysing the components of a generic code offloading architecture. Based on theoretical analysis, I identify the parameters to describe the key limitations for code offloading in practice and then propose solutions to mitigate these limitations. In this paper I characterize the architecture of Google App Engine to reach a solution that reduces the amount of developer effort required to improve the performance as well as reduce the power consumption of smartphone.

1. Identifying parameters for Code Offloading as a
practical solution to optimize the energy
consumption of a smartphone in real life context?
Presentation ON

2. Outlines
 Background
 Methodology
 Result and Discussion
 Conclusion

3.  One of the biggest problems for future growth of
Smartphones is battery life.
Background

4.  One popular technique :
- Remote Execution of code i.e. Mobile Code Offloading
Background

5.  What is Code Offloading:
- Type of binding between mobile and cloud to move a
computational task from one place to another that Increase
performance of the mobile apps.
Background

6.  Code Profiler:
- The code profiler is in charge of determining what to offload.
Background
Code offloading components:

7.  System Profiler:
- Monitoring multiple parameters of the smartphone i.e. energy
to execute the code.
Background

8.  Decision Engine:
- Decide when to offload to the cloud by measuring whether
or not the handset obtains a concrete benefit from
offloading to the cloud.
Background

9.  Surrogate Platform:
- Remote server located in the cloud, which contains the
environment to execute the intermediate code sent by the
mobile device.
Background

10.  Code offloading architecture:
Background

11. But why it is hard to implement in real life?

12. Methodology
 Parameters for identifying the limitation of
code offloading:

13.  Inaccurate code profiling:
- Difficult to estimate the running cost of a piece of
code considered for offloading.
Methodology

14.  Dynamic configuration:
- This is an evident issue as the computational capabilities
of the latest smartphones are comparable to some servers
running in the cloud.
Methodology

15.  Scalability of the system:
- when the system handles heavy loads of computational
requests its difficult to maintain a certain quality of
responsiveness.
Methodology

16. Methodology
 Characterization of Google App Engine (GAE):

17. Methodology
 Start Quickly, Build Faster:

18. Methodology
 Automatic Scaling:

19. Methodology
 Automated Security Scanning:

20. Methodology
 Tasks Queue:

21.  Characterization of different code offloading
frameworks:
Result and Discussion

22. Result and Discussion
 Comparison among different code offloading frameworks
and GAE (Google App Engine):
0
1
2
3
4
Code Profiling
Accuracy
Acceleration Scalability Dynamic Integration
MAUI
CloneCloud
ThinkAir
COMET
EMCO
GAE
0.0-1.0 Low ; 1.1-2.0 Medium; and 2.1-3.0 High;

23. Conclusion
 My research has explored :
 A deep knowledge in the code offloading
architecture and existing different frameworks,
 What are the parameters to find out the
limitation while code offloading is working, and
 The characterization of Google App Engine as
a solution to overcome those limitation.
So that a developer can come with a suggestion on
which way he/she should choose to develop
mobile Apps to optimize the energy consumption of
a smartphone.