From 5f37feffea1773ad0a08da3fc5f193cc37f1013b Mon Sep 17 00:00:00 2001 From: mjkwiatkowski Date: Thu, 25 Jun 2026 15:25:18 +0200 Subject: feat: added the evaluation and validation notes, only experiment pictures are now missing --- main.tex | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) (limited to 'main.tex') diff --git a/main.tex b/main.tex index 2881570..2ff5706 100644 --- a/main.tex +++ b/main.tex @@ -158,10 +158,9 @@ %If we had that in, we can reach these kinds of gains. % @Mateusz there is really not a possibility to incorporate CINECA's models, so to address Dante's feedback, I created this experiment. - \begin{tcolorbox}[title=Failure Detection: Main Finding I] - On average, \emph{Sunfish} can detect 14.5\% of unexpected failures in the physical twin. - We show, that digital twinning \emph{can} be used for failure detection. - + \begin{tcolorbox}[title=Validation] + We posit digital twinning can be used for failure detection to the benefit of DC operators. + We validate our system against DyTwin~\cite{DBLP:conf/sc/TaheriBPRHDEWPM24} designed by Milojicic \etal to show we achieve similar results. \end{tcolorbox} \begin{minipage}[b]{0.45\linewidth} \begin{center} @@ -178,8 +177,9 @@ \end{frame} \begin{frame}\frametitle{\textbf{RQ3}: Experimental Results II} - \begin{tcolorbox}[title=Scheduling Optimization: Main Finding II] - Here explain what did you find. + \begin{tcolorbox}[title=Evaluation] + Predictive analytics is core to digital twinning. We evaluate our system against the requirements (extra slides) by predicting an optimal scheduling policy. + During runtime, we make dynamic adjustments to the physical twin, if the scheduling results differ. \end{tcolorbox} \end{frame} -- cgit v1.2.3