Abstract: The development of cloud and mobile technology leads to Mobile Cloud Computing (MCC). MCC has become a major service structure now a days. The limitations in the battery power of mobile can be overcome with the help of cloud technology which is having infinite amount of resources. Offloading is a method for improving the capabilities of resource limited smartphones by augmenting with cloud resources. The mobile applications can be partitioned into two in such a way that heavier parts are executed at the cloud and the rest is executed in the mobile itself. This study designs a system for offloading and partitioning architecture which will take into consideration of all the contextual information related to a mobile. The decision of offloading and partitioning is taken considering the current connectivity, memory status, battery charge, etc. The evaluation results reveal that this algorithm gives performance improvement, less overhead to the mobile side and the prediction accuracy of context aware decision engine. A light weight partition algorithm is used for splitting the application. The results shows significant improvement in time and energy consumed.
N.M. Dhanya and G. Kousalya, 2016. Context Aware Offloading Decision and Partitioning in Mobile Cloud Computing. Asian Journal of Information Technology, 15: 2177-2185.