Abstract
The growth of mobile cloud computing (MCC) is challenged by the need to adapt to the resources and environment that are available to mobile clients while addressing the dynamic changes in network bandwidth. Big data can be handled via MCC. In this paper, we propose a model of computation partitioning for stateful data in the dynamic environment that will improve the performance. First, we constructed a model of stateful data streaming and investigated 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. We also defined the problem for a multiframe data flow calculation segmentation decision that is optimized for dynamic conditions and provided an analysis. 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. We followed the scheduling method to provide the optimal calculation for data frame execution time after computation partitioning and the best computation partitioning method. Third, we explored a calculation segmentation method for single-frame data flow based on multiframe data using multiframe data optimization adjustment and prediction of future changes in network bandwidth. We were able to demonstrate that the calculation method for multiframe data in a changing network bandwidth environment is more efficient than the calculation method with the limitation of calculations for single-frame data. Finally, our research verified the effectiveness of single-frame data in the application of the data stream and analyzed the performance of the method to optimize the adjustment of multiframe data. We used a MCC platform prototype system for face recognition to verify the effectiveness of the method.
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