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\begin{frame}\frametitle{Motivation}
	\begin{tcolorbox}[title=Context]
		21\textsuperscript{st} century datacenters are primarily heterogeneous~\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{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 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 such as predictive analytics~\cite{DBLP:usdoe/report/AP26894} to \emph{e.g.,} prevent unexpected job failures.
	\end{tcolorbox}
	\input{images/table.tex}
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\begin{frame}\frametitle{Background}
	\begin{tcolorbox}[title=Predictive Analytics]
		\ipsum[1]
	\end{tcolorbox}
	\begin{center}
		\includegraphics[width=0.7\linewidth]{images/predictive_analytics.pdf}
	\end{center}
	\tiny
	\textbf{Figure 1.2:} Where does our work fit within the field of datacenter digital twinning? \textcolor{LimeGreen}{\faHighlighter~Highlighted areas are the contributions from this thesis.}

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\begin{frame}\frametitle{Research Questions}
	\begin{tcolorbox}[title=Main Research Question, colbacktitle=red!70!black,colback=red!20!white]
		How to enable predictive analytics for datacenters through digital twinning?
	\end{tcolorbox}

	\begin{tcolorbox}[title=Research Question 1]
		How to asses the current state-of-the-art of digital twinning for datacenters?
	\end{tcolorbox}

	\begin{tcolorbox}[title=Research Question 2]
		How to design a datacenter digital twin reference architecture using discrete-event simulation and predictive data analytics?
	\end{tcolorbox}

	\begin{tcolorbox}[title=Research Question 3]
		How to evaluate and validate a datacenter digital twin architecture in relation to system requirements?
	\end{tcolorbox}
\end{frame}

\begin{frame}\frametitle{\textbf{RQ1}: System Model}
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\begin{frame}\frametitle{\textbf{RQ2}: Reference Architecture}
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\begin{frame}\frametitle{\textbf{RQ3}: Experimental Results I}
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\begin{frame}\frametitle{\textbf{RQ3}: Experimental Results II}
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\begin{frame}\frametitle{Key Takeaways}

\end{frame}

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\begin{frame}[allowframebreaks]\frametitle{Extra Slides: References}
	\tiny
	\bibliographystyle{is-plain}
	\bibliography{main.bib}
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\begin{frame}\frametitle{Extra Slides: Societal Impact}
	\begin{tcolorbox}[title=Why is this research important today?]
		Over 3 million jobs in the Netherlands directly depend on cloud services, which are hosted in datacenters~\cite{DBLP:journals/corr/IosupKLVG22}.
		Already the rapid
		expansion of datacenters has increased the presence of service failures across all cloud
		services~\cite{DBLP:conf/acsos/TalluriOVTI21}.
		We need to act now.
	\end{tcolorbox}
	\begin{center}
		\includegraphics[height=10em]{images/manageability.pdf}
	\end{center}
	\tiny
	\textbf{Figure E.1:} Horizontally: the most important research areas in computer science in Netherlands.
	Vertically: qualities we should ensure across all research areas with the most outstanding impact on society.
	Datacenter manageability is a top-priority~\cite{DBLP:journals/corr/IosupKLVG22}.
\end{frame}


\begin{frame}\frametitle{Extra Slides: Why Digital Twinning?}
	\begin{tcolorbox}[title=Definition]
		A DCDT mirrors the structure, context and behaviour of a datacenter~\cite{DBLP:journals/computer/AthavaleBBMMPS24}. The prerequisite to any digital twin is good monitoring and sensing capabilities in the physical entity.
		Datacenters meet this requirement easily because they already connect hundreds of monitoring sensors. 	\end{tcolorbox}

	\begin{center}
		\includegraphics[height=10em]{images/dt_timeline.pdf}
	\end{center}
	\tiny

	\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.
\end{frame}

\begin{frame}\frametitle{Extra Slides: Why not pure simulation?}
	\begin{tcolorbox}[title=Predicting job failures]
		Preventing failure-caused outages in advance
		can reduce huge operational costs, as over 20\% of all reported outages amount to more than 1 million
		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/digital_twin_ms.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}

% 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}.
%Data-driven Machine Learning performs poorly by the cases not covered in the training data.

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