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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-fault-prone BSP
machines. The fault occurrences are fail-stop and fully dynamic,
i.e., they are allowed 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 a 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 backtracking operations to robustly stored instances of
the computation, in case of locally unrecoverable situations).
It adopts an adaptive balancing scheme of the workload among the
currently live processors of the BSP machine. Our strategy is
efficient in the sense that, compared with an optimal off-line
adversarial computation under the same sequence of fault
occurrences, it achieves an O(log n loglog n)^2 multiplicative
factor times the optimal work (namely, this measure is in the
sense of the "competitive ratio" of on-line analysis). In
addition, our scheme is modular, integrated, and considers many
implementation points.
We comment that, to our knowledge, no previous work on robust
parallel com-putations has considered fully dynamic faults in
the BSP model, or in general distributed memory systems.
Furthermore, this is the first time an efficient Las Vegas
simulation in this area is achieved. |
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