Adaptive mobile computation offloading for data stream applications

In recent times mobile applications are becoming increasingly rich in terms of the functionalities they provide to the end users. Such applications might be very popular among users but there execution may result in draining of many of the device end resources. Mobile Cloud Computing (MCC) provides a better way of executing such applications by offloading certain parts of the application to cloud. At the first place, computation offloading looks quite promising in terms of saving device end resources but eventually turn out to be expensive if carried out in a static manner. Changes in device end resources and environment variables may have huge impacts on the efficiency of offloading techniques and may even reduce the quality of service for applications involving the use of real time information. In order to overcome this problem, we propose an adaptive computation offloading framework for data stream applications wherein applications are partitioned dynamically followed by being offloaded depending upon the device end resources, network conditions and cloud resources. We also propose an algorithm that depicts the work flow of our computation model. The proposed model is simulated using the CloudSim simulator. In the end, we illustrate the working of our proposed system along with the simulated results.