Making Speculative Scheduling Robust to Incomplete Data
Résumé
In this work, we study the robustness of Speculative
Scheduling to data incompleteness. Speculative scheduling has
allowed to incorporate future types of applications into the
design of HPC schedulers, specifically applications whose runtime
is not perfectly known but can be modeled with probability
distributions. Preliminary studies show the importance of spec-
ulative scheduling in dealing with stochastic applications when
the application runtime model is completely known. In this work
we show how one can extract enough information even from
incomplete behavioral data for a given HPC applications so
that speculative scheduling still performs well. Specifically, we
show that for synthetic runtimes who follow usual probability
distributions such as truncated normal or exponential, we can
extract enough data from as little as 10 previous runs, to be
within 5% of the solution which has exact information. For real
traces of applications, the performance with 10 data points varies
with the applications (within 20% of the full-knowledge solution),
but converges fast (5% with 100 previous samples).
Finally a side effect of this study is to show the importance
of the theoretical results obtained on continuous probability
distributions for speculative scheduling. Indeed, we observe that
the solutions for such distributions are more robust to incomplete
data than the solutions for discrete distributions.
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