In this paper we present an efficient general simulation strategy for computations designed for fully operational BSP machines of n ideal processors, on n-processor dynamic-fauhprone BSP machines. The fault occurrences are fail-stop and fully dynamic, i.e., they are ahowed to happen on-line at any point of the computation, subject to the constraint that the total number of faulty processors may never exceed a known fraction. The computational paradigm can be exploited for robust computations over virtual parallel settings with volatile underlying infrastructure, such as a Network of Workstations (where workstations may be taken out of the virtual parallel machine by their owner). Our simulation strategy is Las Vegas (i.e. it may never fail, due to a BACKTRACKING process to robustly stored instances of the computation). It adopts an adaptive balancing scheme of the work load among the currently live processors of the BSP machine. Moreover, the storage schemes adopted in this work achieve space optimality, which is very crucial in the BSP cost model, since space overhead is interpreted into communication overhead, when a fraction of the work load has to migrate to a currently live processor. Our strategy is efficient in the sense that, compared to an optimal off-line adversarial computation under the same sequence of fault occurrences, it achieves an 0 ((log n . loglog n)“) multiplicative factor times the optimal work (namely, this measure is in the sense of “competitive ratio” of on-line analysis). In addition, our scheme is modular, integrated, and considers many implementation points. We comment that, up to our knowledge, no previous work on robust parallel computations has considered fully dynamic faults in the BSP model, or in general distributed memory systems. Furthermore, this is the first time where an efficient Las Vegas simulation in this area is achieved.