International audienceIn this work, we study the robustness of SpeculativeScheduling to data incompleteness. Speculative scheduling hasallowed to incorporate future types of applications into thedesign of HPC schedulers, specifically applications whose runtimeis not perfectly known but can be modeled with probabilitydistributions. Preliminary studies show the importance of spec-ulative scheduling in dealing with stochastic applications whenthe application runtime model is completely known. In this workwe show how one can extract enough information even fromincomplete behavioral data for a given HPC applications sothat speculative scheduling still performs well. Specifically, weshow that for synthetic runtimes who follow usual probabilitydistributions such as truncated normal or exponential, we canextract enough data from as little as 10 previous runs, to bewithin 5% of the solution which has exact information. For realtraces of applications, the performance with 10 data points varieswith 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 importanceof the theoretical results obtained on continuous probabilitydistributions for speculative scheduling. Indeed, we observe thatthe solutions for such distributions are more robust to incompletedata than the solutions for discrete distributions
