Computation partitioning for mobile cloud computing in a big data environment

Abstract

The growth of Mobile cloud computing (MCC) is challenged by the need to adapt to the resources and environment available for mobile clients while working with the dynamic changes of network bandwidth. In this paper, we propose a model of computation partitioning for stateful data in the dynamic environment that will improve performance. First, we constructed a model of stateful data streaming and studied the method of computation partitioning in a dynamic environment. We developed a definition of direction and calculation of the segmentation scheme, including single frame data flow, task scheduling and executing efficiency. Second, we proposed a computation partitioning method for Single frame data flow. We determined the data parameters of the application model, the computation partitioning scheme, and the task and work order data stream model. Finally, our research verified the effectiveness of single frame data in the application of the data stream. We used a mobile cloud computing platform prototype system for face recognition to verify the effectiveness of the method.