Identifying parameters for Code Offloading as a

practical solution to optimize the energy

consumption of a smartphone in real life context?

Presentation ON

Outlines

Background

Methodology

Result and Discussion

Conclusion

One of the biggest problems for future growth of

Smartphones is battery life.

Background

One popular technique :

- Remote Execution of code i.e. Mobile Code Offloading

Background

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

Code Profiler:

- The code profiler is in charge of determining what to offload.

Background

Code offloading components:

System Profiler:

- Monitoring multiple parameters of the smartphone i.e. energy

to execute the code.

Background

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

Surrogate Platform:

- Remote server located in the cloud, which contains the

environment to execute the intermediate code sent by the

mobile device.

Background

Code offloading architecture:

Background

But why it is hard to implement in real life?

Methodology

Parameters for identifying the limitation of

code offloading:

Inaccurate code profiling:

- Difficult to estimate the running cost of a piece of

code considered for offloading.

Methodology

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

Scalability of the system:

- when the system handles heavy loads of computational

requests its difficult to maintain a certain quality of

responsiveness.

Methodology

Methodology

Characterization of Google App Engine (GAE):

Methodology

Start Quickly, Build Faster:

Methodology

Automatic Scaling:

Methodology

Automated Security Scanning:

Methodology

Tasks Queue:

Characterization of different code offloading

frameworks:

Result and Discussion

Result and Discussion

Comparison among different code offloading frameworks

and GAE (Google App Engine):

0

1

2

3

4

Code ProfilingAccuracy

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;

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.