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@@ -173,7 +173,7 @@ \end{center} \vspace{-0.3cm} \tiny - \textbf{Figure 1.7:} Experiment 1a. In this experiment we use red and yellow alarms to notify datacenter operators of unexpected failures. + \textbf{Figure 1.7a:} Experiment 1a. In this experiment we use red and yellow alarms to notify datacenter operators of unexpected failures. We use a threshold based on predictions done by the simulator and a statistical distribution. \end{minipage} \hspace{0.6cm} @@ -183,28 +183,35 @@ \end{center} \vspace{-0.3cm} \tiny - \textbf{Figure 1.8:} Experiment 1b. The mean failure detection rate is around 15\%. Even though this seems low, if we look at \textbf{Fig. 1.9} (see Extra Slides), this simply means around 15\% of failures are unexpected. + \textbf{Figure 1.7b:} Experiment 1b. The mean failure detection rate is around 15\%. Even though this seems low, if we look at \textbf{Fig. E.1} (see Extra Slides), this simply means around 15\% of failures are unexpected. \end{minipage} % Explain what the axis are in the figure caption. % Talk about the experimental setup in the figure. % Give more reliable results than just numbers -- do statistical testing, i.e., standard deviation, confidence intervals. \end{frame} -%\begin{frame}\frametitle{\textbf{RQ3}: Experimental Results II} -% \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} -% \hspace{0.2cm} -% \begin{minipage}[b]{0.32\linewidth} -% \begin{center} -% \includegraphics[width=1.1\textwidth]{images/23_Jun_2026_102028.pdf} -% \end{center} -% \vspace{-0.3cm} -% \tiny -% \textbf{Figure 1.9:} Experiment 1 -% \end{minipage} -%\end{frame} +\begin{frame}\frametitle{\textbf{RQ3}: Experimental Results II} + \begin{tcolorbox}[title=Main Finding III] + \emph{Sunfish} is capable of dynamic adjustments to the physical twin at runtime, and can lower the mean number of failed tasks. + \end{tcolorbox} + \hspace{0.2cm} + \begin{minipage}[b]{0.45\linewidth} + \begin{center} + \includegraphics[width=1.1\textwidth]{images/23_Jun_2026_102028.pdf} + \end{center} + \vspace{-0.3cm} + \tiny + \textbf{Figure 1.8a:} Experiment 2a. + \end{minipage} + \begin{minipage}[b]{0.45\linewidth} + \begin{center} + \includegraphics[width=1.1\textwidth]{images/23_Jun_2026_102028.pdf} + \end{center} + \vspace{-0.3cm} + \tiny + \textbf{Figure 1.8b:} Experiment 2b. + \end{minipage} +\end{frame} \begin{frame}\frametitle{Key Takeaways} \begin{tcolorbox}[title=What is the societal context?] @@ -246,11 +253,11 @@ \bibliography{main.bib} \end{frame} -\begin{frame}\frametitle{Technical Setup } +\begin{frame}\frametitle{Extra Slides: Technical Setup } \begin{tcolorbox}[title=What is the simulation workload?] The compute workload is BitBrainsSmall. - The failure traces include user reports from Gmail, WhatsApp and Twitter. - + The failure traces include user reports from Gmail, WhatsApp, Facebook and Twitter. + For predictions we use \texttt{prefabs}~\cite{DBLP:journals/fgcs/VersluisCGLPCUI23}. \end{tcolorbox} \begin{tcolorbox}[title=What is the experiment environment?] A commodity laptop: Framework Laptop 13, with 32GB of DDR5 RAM and an AMD Ryzen 7840U processor and an ArchLinux OS with Linux 7.0.13-arch1-1 kernel. |