Robust I/O Performance in River
The key problem with cluster applications that perform parallel I/O is
that they are very sensitive: in a quiescent system, performance is
excellent, but a small perturbation on a single workstation leads to a
large-scale performance hit. We would like to move applications away
from this meta-stable performance point into a more stable regime, where
small perturbations lead to small (or no) performance degradation. To
assist applications in this direction, we propose a system known as
"River". By defining a higher level, more flexible interface, and a
dynamic, environment-aware system underneath, we plan to provide robust
parallel I/O to a range of interesting applications (decision support,
scientific, etc.). The River mantra is "move the I/O to the
computation", and draws on work from both the parallel I/O and task
queue literature.
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