feat: added a slide to argue against pure simulation for predictive analysis

1 files changed, 18 insertions, 1 deletions

diff --git a/main.tex b/main.tex
index 4e034b5..c6011ce 100644
--- a/main.tex
+++ b/main.tex
@@ -115,7 +115,7 @@
 	\end{center}
 	\tiny
 
-	\textbf{Figure E.3:} Due to insufficient technological foundations,
+	\textbf{Figure E.2:} Due to insufficient technological foundations,
 	little work is available on DTs between 2003 and 2018, and it is only with the rapid growth
 	of cloud computing, Internet-of-Things and Big Data analytics that DTs have reemerged~\cite{DBLP:conf/cirp/TAO2018169}.
 	That is why nobody used digital twins to mirror datacenters earlier.
@@ -130,7 +130,24 @@
 		US\$~\cite{DBLP:report/AnnualOutageAnalysis2025}.
 		Only a constant bi-directional interaction (digital twin $\iff$ physical entity) can achieve this.
 	\end{tcolorbox}
+	\begin{center}
+		\includegraphics[height=10em]{images/AthavaleBBMMPS24-3_cropped.pdf}
+	\end{center}
+	\tiny \textbf{Figure E.3:} Real-time control that is tightly-coupled with the IT equipment is a prerequisite for timely predictions within seconds/minutes~\cite{DBLP:journals/computer/AthavaleBBMMPS24}.
 \end{frame}
 
 
+\begin{frame}\frametitle{Extra Slides: Alternatives to OpenDC}
+	\begin{tcolorbox}[title=Why not CFD/ML/Graphs?]
+		\begin{enumerate}
+			\item Computational Fluid Dynamics (CFD) have high computation overhead, unsuitable for real-time simulation of a dynamic datacenter.
+			      Moreover oftentimes a poorly configured CFD model can lead to high error rates~\cite{DBLP:conf/sensys/WangZD0TCWZ20}.
+
+			\item Data-driven Machine Learning performs poorly by the cases not covered in the training data.
+
+
+		\end{enumerate}
+	\end{tcolorbox}
+\end{frame}
+
 \end{document}