Heterogeneous multi-core processor is the mainstream solution for modern embedded systems now. Good online mapping or scheduling approaches play important roles in improving their advantages of high performance and low power consumption. To deal with the problem of dynamic mapping and scheduling of applications on heterogeneous multi-core processing systems, a dynamic mapping and scheduling solution was proposed to effectively determine remapping time in order to maximize the system performance by using the machine learning based detection technology of quickly and accurately evaluating program performance and program behavior phase change. In this solution, by carefully selecting the static and dynamic features of processing cores and programs to running to effectively detect the difference in computing power and workload running behaviors brought by heterogeneous processing, a more accurate prediction model was built. At the same time, by introducing phase detection technology, the number of online mapping computations was reduced as much as possible, so as to provide more efficient scheduling scheme. Finally, the effectiveness of the proposed scheduling scheme was verified on the SPLASH-2 dataset. Experimental results showed that, compared to the Completely Fair Scheduler (CFS) of Linux, the proposed method achieved about 52% computing performance gains and 9.4% improvement on CPU resource utilization rate. It shows that the proposed method has excellent performance in system computing performance and processor resource utilization, and can effectively improve the dynamic mapping and scheduling effect of applications of heterogeneous multi-core systems.

Heterogeneous Multi-core Processors (HMPs) platform has become the mainstream solution for modern embedded system design, and online mapping or scheduling plays a vital role in making full use of the advantages of high performance and low power consumption. Aiming at the dynamic mapping problem of application tasks in HMPs, a mapping and scheduling approach based on machine learning prediction model was proposed. On the one hand, a machine learning model was constructed to predict and evaluate the performance of different mapping strategies rapidly and efficiently, so as to provide support for online scheduling. On the other hand, the machine learning model was integrated with genetic algorithm to find out the optimal resource allocation strategy efficiently. Finally, an Motion-Join Photographic Experts Group (M-JPEG) decoder was used to verify the effectiveness of the proposed approach. The experimental results show that, compared with the Round Robin Scheduler (RRS) and sampling scheduling approaches, the proposed online mapping/scheduling approach has reduced the average execution time by about 19% and 28% respectively.