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