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\frame{\titlepage \centering \footnotesize Online slides: \url{https://www.overleaf.com/read/tknhxmqfgtdy\#87413e}}

\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=DCDT's lack predictive analytics]
		We need Datacenter Digital Twins (DCDT) to be better able to detect and solve issues in critical ICT 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 failures.
		With predictive analysis (\emph{e.g.,} regression) DCDT's could save millions of lost \$USD~\cite{DBLP:conf/acsos/TalluriOVTI21}.
	\end{tcolorbox}

	\begin{center}
		\includegraphics[width=0.9\linewidth]{images/predictive_analytics.pdf}
	\end{center}
	\tiny
	\textbf{Figure 1.2:} Where does our work fit within the field of datacenter digital twinning?
	There are 5 core elements to any Digital Twin: \myCircled{A} The Digital $\rightarrow$ Physical Twin link, \myCircled{B} the Physical Twin (\emph{e.g.,} the datacenter), \myCircled{C} the Physical $\rightarrow$ Digital Twin link, \myCircled{D} the Digital Twin, \myCircled{E} the features necessary to any Digital Twin.
	\textcolor{ForestGreen}{\faHighlighter~Highlighted areas are the contributions from this thesis, which include the autonomous actions resulting from predictive insights \myCircledGreen{A} and the predictive analysis itself within \myCircledGreen{E}.}
\end{frame}

\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}: Literature Review I}
	\begin{tcolorbox}[title=Results]
		This is a dummy sentence meant to make the tcolorbox have more than 2 lines of text width so that I am able to show the text and the table spacing better.
		I hope it fits its purpose well.
	\end{tcolorbox}
	\input{images/table.tex}
\end{frame}

\begin{frame}\frametitle{\textbf{RQ1}: Literature Review II}
	% Mandatory: split the figure into 2: top and bottom, and that way you can fill in the entire slide nicely.
	\begin{minipage}[b]{0.45\linewidth}
		\begin{center}
			\includegraphics[width=1.15\textwidth]{images/system_model.pdf}
		\end{center}
		\tiny
		\textbf{Figure 1.3:} To answer \textbf{RQ1} we designed a generic datacenter digital twin system model based on a comprehensive literature review and findings from \textbf{Table 1.1}.
	\end{minipage}
	% Consider splitting the figure into 2 a.k.a. top and bottom.
	% Data Lake -> Data Storage
\end{frame}

\begin{frame}\frametitle{\textbf{RQ2}: Reference Architecture}
\end{frame}

\begin{frame}\frametitle{\textbf{RQ3}: Experimental Results I}
	\begin{tcolorbox}[title=Main Finding I]
		Here explain what did you find.
	\end{tcolorbox}
	Here goes the figure that backs up claim in Main Finding I.
	Evidence for Main Finding I.
	% 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=Main Finding II]
		Here explain what did you find.
	\end{tcolorbox}
	Here goes the figure that backs up claim in Main Finding II.
\end{frame}

\begin{frame}\frametitle{Key Takeaways}
	\begin{tcolorbox}[title=What is the societal context?]

	\end{tcolorbox}

	\begin{tcolorbox}[title=What problem did we solve?]

	\end{tcolorbox}

	\begin{tcolorbox}[title=How did we solve this problem?]

	\end{tcolorbox}

	\begin{tcolorbox}[colbacktitle=red!70!black, colback=red!20!white,title=What did we find?]

	\end{tcolorbox}

	\begin{tcolorbox}[title=What do we see in future work?]

	\end{tcolorbox}

\end{frame}

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\begin{frame}[allowframebreaks]\frametitle{Extra Slides: References}
	\tiny
	\bibliographystyle{is-plain}
	\bibliography{main.bib}
\end{frame}

\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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