(Georgia Institute of Technology, 2013)
Shi, Cong; Pandurangan, Pranesh; Ni, Kangqi; Yang, Juyuan; Ammar, Mostafa H.; Naik, Mayur; Zegura, Ellen W.
Offloading computation-intensive components of mobile applications
to the cloud is of great potential to speedup the
execution and reduce the energy consumption for mobile
devices. The gain from computation offloading is typically
counterbalanced by communication costs and delays. It is,
therefore, important to undertake offloading decisions based
on future prediction of Internet access timeliness and quality.
Previous approaches have considered this question under
the assumption that network connectivity is relatively
stable. In this paper, we present IC-Cloud, a computation-offloading
system for mobile environments where Internet
access to remote computation resources is of highly variable
quality and often intermittent. IC-Cloud uses three key
ideas: lightweight connectivity prediction, lightweight execution
prediction and prediction use in a risk controlled manner
tomake offloading decisions. Our connectivity-prediction
method only uses the signal strength and user historical information
to obtain a coarse-grained estimation of the network
access quality. Our execution-prediction mechanism
uses machine learning on dynamic program features to automatically,
efficiently, and accurately predict the execution
time of offloadable tasks, both on the phone and in the cloud.
Acknowledging the uncertainties in these predictions, we
propose a risk-control mechanism to reduce the impact of
inaccurate predictions. We implemented IC-Cloud on Android
and tested the system with different applications in
various types of mobile environment. Results we obtained
from the prototype show speedup and energy consumption
reduction benefits in many computational contexts and intermittent
connectivity environments.