To offer a low-cost and high-performance distributed computing platform, we have studied the user-PC computing (UPC) system based on the master-worker model. The UPC system uses idling resources of personal computers (PCs) for daily usage by users as the workers, to run the requested jobs or application programs that may need various environments on Docker containers. In this paper, we implement a job migration function in the UPC system to speed up the completion by dynamically changing the assigned worker. It adopts Checkpoint-Restore in Userspace (CRIU) to save the data at the job running into image files and Podman to manage the Docker containers. To verify the function, we conduct extensive measurements with nine jobs and four PCs that have different features. The results show that any job was successfully migrated between different PCs, and the migration from a slow PC to a faster PC reduced the total CPU time.