Data collection and restoration for heterogeneous process migration

This study presents a practical solution for data collection and restoration to migrate a process written in high level stack-based languages such as C and Fortran over a network of heterogeneous computers. We study a logical data model which recognizes complex data structures in process address space. Then, novel methods are developed to incorporate the model into a process and to collect and restore data efficiently. We have implemented a prototype software and performed experiments on different programs. Experimental and analytical results show that (I) a user-level process can be migrated across different computing platforms, (2) semantic information of data structures in the process's memory space can be correctly collected and restored, (3) the costs of data collection and restoration depend on the complexity of the logical model representing the process's data structures and the amount of data involved and (4) the implantation of the data collection and restoration mechanisms into the process is not a decisive factor of incurring execution overheads; with appropriate program analysis, we can achieve practically low overhead.