style: changed Dante's title from MSc -> Drs.

1 files changed, 14 insertions, 26 deletions

diff --git a/main.tex b/main.tex
index 018294b..6a5eed4 100644
--- a/main.tex
+++ b/main.tex
@@ -6,36 +6,24 @@
 
 \begin{frame}\frametitle{Motivation}
 	\begin{tcolorbox}[title=Context]
-		Heterogeneous datacenter architectures are common ~\cite{DBLP:conf/date/MilojicicFDR21} due to the end of Dennard's scaling~\cite{DBLP:image/48Microprocessor/Rupp}.
-		Today, computational needs of AI drive managers to diversify datacenters even more~\cite{DBLP:journals/computer/AthavaleBBMMPS24}.
-		In result datacenters become extremely complex and hard to operate.
+		Heterogeneous datacenter architectures are common ~\cite{DBLP:conf/date/MilojicicFDR21} and
+		modern computational needs of AI drive managers to diversify datacenters even more~\cite{DBLP:journals/computer/AthavaleBBMMPS24}.
+		In result datacenters become extremely complex and hard to operate with millions of CPU's, GPU's etc.
 	\end{tcolorbox}
-	\begin{minipage}[t]{0.45\linewidth}
-		\tiny
-		\begin{center}
-			\includegraphics[width=0.7\linewidth]{images/nvidiagpu.png}
-		\end{center}
-		\vspace{-0.2cm}
-		%\textbf{Figure 1.1:} Nvidia Ada Lovelace AD102 Architecture, present in all GPUs from 2022 onwards~\cite{Wikipedia:AdaLovelaceArchi}.
-		%Source: official renderings by Nvidia.
-		%\textbf{Figure 1.1:} 48 years of microprocessor trend data. Legend: \textcolor{Orange}{$\bigblacktriangleup$ Transistors (thousands)}, \textcolor{Blue}{$\lgblkcircle$ Single Thread Performance (SpecINT $\times 10^3$)}, \textcolor{Green}{$\lgblksquare$ Frequency (MHz)}, \textcolor{Maroon}{$\bigblacktriangledown$ Typical Power (Watts)}, $\mdlgblkdiamond$ Number of Logical Cores~\cite{DBLP:image/48Microprocessor/Rupp}.
-	\end{minipage}
-	\hspace{0.5cm}
-	\begin{minipage}[t]{0.45\linewidth}
-		{\centering
-			\includegraphics[width=1.1\linewidth]{images/AthavaleBBMMPS24_cropped.pdf}
-		}
-		\tiny
-		\textbf{Figure 1.2:} Explosive growth in AI computational requirements drives datacenter upgrades (source: NVIDIA Analysis: reproduction with NVIDIA permission by~\cite{DBLP:journals/computer/AthavaleBBMMPS24}).
-	\end{minipage}
+	\begin{center}
+		\includegraphics[width=\linewidth]{images/datacenter_complexity.pdf}
+	\end{center}
+	\tiny
+	\textbf{Figure 1.1:} Society depends on datacenters to keep running, and therefore we cannot afford to let these systems break down or experience significant performance-related issues.
+	With millions of servers in the largest datacenters, real-time management becomes very difficult.
+	Left to right: a Google datacenter, server racks, Ada Lovelace AD102 GPU architecture.
 \end{frame}
 
 \begin{frame}\frametitle{Problem Statement}
-	\begin{tcolorbox}[title=We need effective tools to manage datacenters]
-		To address the increasing datacenter complexity, Datacenter Digital Twins (DCDT) were proposed~\cite{DBLP:journals/computer/AthavaleBBMMPS24}.
-		However, many DCDT's are not useful in practice, because they lack critical features (\emph{e.g.,} predictive analytics) native to the generic Digital Twin definition~\cite{DBLP:usdoe/report/AP26894}.
+	\begin{tcolorbox}[title=We need tools to tackle datacenter complexity]
+		We need Datacenter Digital Twins (DCDT) to be better able to detect and solve issues in this critical infrastructure~\cite{DBLP:journals/computer/AthavaleBBMMPS24}.
+		However, DCDT's are still actively developed, and lack crucial features \emph{e.g.,} predictive analytics~\cite{DBLP:usdoe/report/AP26894} to prevent unexpected job failures.
 	\end{tcolorbox}
-	\input{images/table.tex}
 \end{frame}
 
 \begin{frame}\frametitle{Research Questions}
@@ -77,7 +65,7 @@
 
 
 
-\setcounter{framenumber}{2}
+\setcounter{framenumber}{4}
 \setbeamertemplate{footline}[page number]{
 
 % Unfortunately this must remain here.