A comparative analysis of virtual versus physical process-migration strategies for distributed modeling and simulation of mobile computing networks
Kunji Han, Sumit Ghosh · 1996
This paper focuses on the high level principles that underlie the distributed modeling and accurate simulation of mobile computing networks on a parallel processing testbed. The testbed consists of a network of workstations configured as a loosely-coupled parallel processor and it closely resembles reality. A key issue is the representation of the stationary and mobile entities of the mobile computing network through concurrent and interacting processes in the testbed. The nature of the representation will influence the accuracy and performance of the simulation. This paper first reviews a process representation technique that has been proposed in the literature for modeling railway networks and then analyzes its limitations. This strategy is referred to as Virtual Process Migration (VPM). The paper then proposes a new strategy, termed Physical Process Migration (PPM), that aims to address the limitations of VPM. It details the software techniques underlying both approaches, describes their implementations on a realistic testbed, and then contrasts their performance under different representative scenarios. While VPM is capable of modeling modest to large-scale mobile computing networks on a testbed consisting of a few processors, the number of processors of the testbed in PPM must correspond to the number of stationary and mobile entities of the mobile computing network size that is being modeled. Analysis of the simulation results reveals that both VPM and PPM are highly effective and very useful strategies under different circumstances. For a given number of mobile and stationary entities, simulation under PPM is fast when every mobile entity requires significant computation. On the other hand, VPM exhibits superior performance relative to PPM when the number of data elements exchanged by each mobile entity at each hop is significantly high.