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A machine learning based approach to mobile cloud offloading

Modern mobile devices are resource limited. We can utilize cloud computing to ensure optimum utilization of mobile device resources. Energy consumption and device latency can be reduced significantly by computation offloading. To ensure optimum utilization of mobile device resources and environment resources such as bandwidth and latency, computation offloading must be done in a strategic way. We have extended our work on a previous paper by proposing a dynamic algorithm based on machine learning which considers network topologies and device resources and makes a decision to offload computation to the cloud. The algorithm adopts with changes in environment and device parameters at run-time. We have designed a framework that resides both in the device, and in the cloud. The device framework monitors device and network parameters and based on user activity decides whether or not to offload computation to the cloud. When the device and network parameters crosses a certain threshold then it sends data to the cloud server using python sockets. The cloud framework receives the data and runs the app in the cloud. After executing the app in the cloud, the framework retrieves the output and sends it back to the local device. The device framework then gives the data to the app and the app completes its execution. We have performed a simulation of the system in a cloud server, and showed that energy can be saved by computation offloading. We also present a financial estimation to calculate the cost of computation offloading.