Mobile cloud application framework and offloading strategies

Mobile Cloud computing has shown its capability to support mobile devices for

provisioning computing, storage and communication resources. A distributed mobile

cloud service system called "POEM" is presented to manage the mobile cloud resource

and compose mobile cloud applications. POEM considers resource management not

only between mobile devices and clouds, but also among mobile devices. It implements

both computation offloading and service composition features. The proposed POEM

solution is demonstrated by using OSGi and XMPP techniques.

Offloading is one major type of collaborations between mobile device and cloud

to achieve less execution time and less energy consumption. Offloading decisions for

mobile cloud collaboration involve many decision factors. One of important decision

factors is the network unavailability. This report presents an offloading decision model

that takes network unavailability into consideration. The application execution time

and energy consumption in both ideal network and network with some unavailability

are analyzed. Based on the presented theoretical model, an application partition

algorithm and a decision module are presented to produce an offloading decision that

is resistant to network unavailability.

Existing offloading models mainly focus on the one-to-one offloading relation. To

address the multi-factor and multi-site offloading mobile cloud application scenarios,

a multi-factor multi-site risk-based offloading model is presented, which abstracts the

offloading impact factors as for offloading benefit and offloading risk. The offloading

decision is made based on a comprehensive offloading risk evaluation. This presented

model is generic and expendable. Four offloading impact factors are presented to show

the construction and operation of the presented offloading model, which can be easily

extended to incorporate more factors to make offloading decision more comprehensive.

The overall offloading benefits and risks are aggregated based on the mobile cloud

users' preference.

The offloading topology may change during the whole application life. A set of

algorithms are presented to address the service topology reconfiguration problem in

several mobile cloud representative application scenarios, i.e., they are modeled as

finite horizon scenarios, infinite horizon scenarios, and large state space scenarios to

represent ad hoc, long-term, and large-scale mobile cloud service composition scenarios,

respectively.