From 604201a150c62285ad5421d1cdd2928668bf0bee Mon Sep 17 00:00:00 2001
From: mjkwiatkowski <mati.rewa@gmail.com>
Date: Thu, 2 Jul 2026 19:02:27 +0200
Subject: feat: last changes

---
 main.tex | 302 +++++++++++++++++++++++++++++++++++----------------------------
 1 file changed, 166 insertions(+), 136 deletions(-)

(limited to 'main.tex')

diff --git a/main.tex b/main.tex
index 444bd07..8908ab0 100644
--- a/main.tex
+++ b/main.tex
@@ -1,119 +1,140 @@
-\documentclass[12pt, handout]{beamer}
+\documentclass[12pt]{beamer}
 \input{style/style.tex}
 \begin{document}
 
 \frame{\titlepage \centering \footnotesize Online slideshow: \url{mjkw.pl/vu/bsc}}
 
-\begin{frame}\frametitle{Motivation}
-	\begin{tcolorbox}[title=Context]
-		21\textsuperscript{st} century datacenters (DC) are mostly heterogeneous~\cite{DBLP:conf/date/MilojicicFDR21} and modern computational needs of AI drive managers to diversify them 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.png}
-	\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.
+\begin{frame}[t]\frametitle{Motivation}
+	\only<1-2>{
+		\vspace{-0.2cm}
+		\begin{tcolorbox}[title=Context]
+			21\textsuperscript{st} century datacenters (DC) are mostly heterogeneous~\cite{DBLP:conf/date/MilojicicFDR21} and modern computational needs of AI drive managers to diversify them 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}
+	}
+	\only<2>{\begin{center}
+			\includegraphics[width=\linewidth]{images/datacenter_complexity.png}
+		\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.
-	\end{tcolorbox}
-
-	\begin{center}
-		\includegraphics[width=0.9\linewidth]{images/predictive_analytics.pdf}
-	\end{center}
-	\tiny
-	\textbf{Figure 1.2:} Datacenter Digital Twin Diagram. 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{Green}{\faHighlighter~Highlighted areas are the contributions from this thesis, which include the autonomous actions resulting from predictive insights \myCircledGreen{A} and the predictive analysis framework (including simple storage capabilities) within \myCircledGreen{E}.}
+\begin{frame}[t]\frametitle{Problem Statement}
+	\only<1-2>{
+		\vspace{-0.2cm}
+		\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.
+		\end{tcolorbox}
+	}
+	\only<2>{
+		\begin{center}
+			\includegraphics[width=0.9\linewidth]{images/predictive_analytics.pdf}
+		\end{center}
+		\tiny
+		\textbf{Figure 1.2:} Datacenter Digital Twin Diagram. 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{Green}{\faHighlighter~Highlighted areas are the contributions from this thesis, which include the autonomous actions resulting from predictive insights \myCircledGreen{A} and the predictive analysis framework (including simple storage capabilities) 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{frame}[t]\frametitle{Research Questions}
+	\only<1-4>{
+		\vspace{-0.2cm}
+		\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}
+	\only<2-4>{\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 reference architecture for a predictive datacenter digital twin using discrete-event simulation?
-	\end{tcolorbox}
+	\only<3-4>{\begin{tcolorbox}[title=Research Question 2]
+			How to design a reference architecture for a predictive datacenter digital twin using discrete-event simulation?
+		\end{tcolorbox}
+	}
 
-	\begin{tcolorbox}[title=Research Question 3]
-		% no "and validate?"
-		How to validate and evaluate a datacenter digital twin architecture in relation to system requirements?
-	\end{tcolorbox}
+	\only<4>{\begin{tcolorbox}[title=Research Question 3]
+			% no "and validate?"
+			How to validate and evaluate 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=Main Finding I]
-		There is little literature on DCDTs.
-		Some systems barely classify as DTs (\emph{e.g.,} Kalibre~\cite{DBLP:conf/sensys/WangZD0TCWZ20}, ChatTwin~\cite{DBLP:conf/sensys/LiW0Z0T23}).
-		Existing deployments specialize in \textcolor{Red}{Cooling and Heat Modelling}, together with \textcolor{Red}{3D visualizations}.
-		Most lack predictive modelling of DC operations.
-	\end{tcolorbox}
-	\vspace{-0.1cm}
-	\input{sources/table.tex}
+\begin{frame}[t]\frametitle{\textbf{RQ1}: Literature Review I}
+	\only<1-2>{
+		\vspace{-0.2cm}
+		\begin{tcolorbox}[title=Main Finding I]
+			There is little literature on DCDTs.
+			Some systems barely classify as DTs (\emph{e.g.,} Kalibre~\cite{DBLP:conf/sensys/WangZD0TCWZ20}, ChatTwin~\cite{DBLP:conf/sensys/LiW0Z0T23}).
+			Existing deployments specialize in \textcolor{Red}{Cooling and Heat Modelling}, together with \textcolor{Red}{3D visualizations}.
+			Most lack predictive modelling of DC operations.
+		\end{tcolorbox}}
+	\only<2>{
+		\vspace{-0.2cm}
+		\input{sources/table.tex}
+
+	}
 	% Research on DTs for datacenters have been separate, siloed efforts focused on either datacenter cooling, network performance, power consumption or visualization efforts.
 	% CFD usually means Navier-Stokes equations.
 	% CFD models take ages to compute.
 \end{frame}
 
-\begin{frame}\frametitle{\textbf{RQ1}: Literature Review II}
+\begin{frame}[t]\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{tcolorbox}[title=A holistic DCDT system model]
-		We propose a holistic model of datacenter digital twinning that can be mapped to each system from \textbf{Table 1.1}. Within this model (see \textbf{Fig. 1.3}) we introduce a concept of the \emph{Digital Thread}: a bridge between the DCDT and the physical DC equipment.
-	\end{tcolorbox}
-	\begin{center}
-		\vspace{-0.1cm}
-		\includegraphics[width=0.8\textwidth]{images/system_model.pdf}
-	\end{center}
-	% The reason why the cooling system is in the graph is because of the fact that 40\% of total energy consumed in DCs comes from cooling~\cite{DBLP:conf/noms/ZhangZLZWC22}.
-	% It has come to the point where datacenters are being build in the Pan-Arctic region, such as Finland,Russia,Sweden etc. with Iceland leading in number of DCs https://www.datacentermap.com/iceland/
-	% The SmarDC digital twin is purely to get more training data for AI models.
-	% Not really a digital twin per se.
-
-	\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}. The \emph{Infrastructure Model} simulates the structure of the DC and the \emph{Operations Model} simulates the behaviour of the DC.
-	\emph{Note:} Federation is not included explicitly but is covered by the model.
-	% Consider splitting the figure into 2 a.k.a. top and bottom.
-	% By the AIAA definition, the DT mimicks the structure and behaviour.
-	% Data Lake -> Data Storage
-	% Use cases of DT's found by Brewer et al.: augmented reality, forensic analysis and diagnostics, predictive modelling, failure detection, operational optimization, ``what-if''' scenarios and virtual prototyping.
+	\only<1-2>{\vspace{-0.2cm}
+		\begin{tcolorbox}[title=A holistic DCDT system model]
+			We propose a holistic model of datacenter digital twinning that can be mapped to each system from \textbf{Table 1.1}. Within this model (see \textbf{Fig. 1.3}) we introduce a concept of the \emph{Digital Thread}: a bridge between the DCDT and the physical DC equipment.
+		\end{tcolorbox}
+	}
+	\only<2>{\begin{center}
+			\vspace{-0.1cm}
+			\includegraphics[width=0.8\textwidth]{images/system_model.pdf}
+		\end{center}
+		% The reason why the cooling system is in the graph is because of the fact that 40\% of total energy consumed in DCs comes from cooling~\cite{DBLP:conf/noms/ZhangZLZWC22}.
+		% It has come to the point where datacenters are being build in the Pan-Arctic region, such as Finland,Russia,Sweden etc. with Iceland leading in number of DCs https://www.datacentermap.com/iceland/
+		% The SmarDC digital twin is purely to get more training data for AI models.
+		% Not really a digital twin per se.
+		\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}. The \emph{Infrastructure Model} simulates the structure of the DC and the \emph{Operations Model} simulates the behaviour of the DC.
+		\emph{Note:} Federation is not included explicitly but is covered by the model.
+		% Consider splitting the figure into 2 a.k.a. top and bottom.
+		% By the AIAA definition, the DT mimicks the structure and behaviour.
+		% Data Lake -> Data Storage
+		% Use cases of DT's found by Brewer et al.: augmented reality, forensic analysis and diagnostics, predictive modelling, failure detection, operational optimization, ``what-if''' scenarios and virtual prototyping.
+	}
 \end{frame}
 
 \begin{frame}\frametitle{\textbf{RQ2}: Reference Architecture}
 	% Make Kafka logos clearly defined --> add a legend with icons?
-	\hspace{-0.3cm}
-	\begin{minipage}[b]{0.45\linewidth}
-		\begin{center}
-			% Change to Datacenter (Physical Twin)
-			\includegraphics[width=1.15\textwidth]{images/ref_architecture.pdf}
-		\end{center}
-		\vspace{-0.15cm}
-		\tiny
-		\textbf{Figure 1.4:} The predictive datacenter digital twin reference architecture.
-		We call the system \emph{Sunfish}.
-		The architecture was designed with the \emph{AtLarge Design Process}~\cite{DBLP:conf/icdcs/IosupVTETBFMT19} over several iterations in the past months.
-		\vspace{0.2cm}
-	\end{minipage}
-	\hspace{0.6cm}
-	\begin{minipage}[b]{0.45\linewidth}
-		\begin{center}
-			\includegraphics[width=1.17\linewidth]{images/implementation.png}
-		\end{center}
-		\vspace{-0.2cm}
-		\tiny
-		\textbf{Figure 1.5:} The prototype and its components based on the architecture.
-		The time-series data flows first to the \texttt{Grafana} dashboard, \texttt{PostgreSQL} database and \texttt{Redis} cache as advised in ~\cite{DBLP:conf/sc/TaheriBPRHDEWPM24}.
-		\vspace{0.1cm}
-	\end{minipage}
+	\only<1-2>{\hspace{-0.3cm}
+		\begin{minipage}[b]{0.45\linewidth}
+			\begin{center}
+				% Change to Datacenter (Physical Twin)
+				\includegraphics[width=1.15\textwidth]{images/ref_architecture.pdf}
+			\end{center}
+			\vspace{-0.15cm}
+			\tiny
+			\textbf{Figure 1.4:} The predictive datacenter digital twin reference architecture.
+			We call the system \emph{Sunfish}.
+			The architecture was designed with the \emph{AtLarge Design Process}~\cite{DBLP:conf/icdcs/IosupVTETBFMT19} over several iterations in the past months.
+			\vspace{0.2cm}
+		\end{minipage}
+		\hspace{0.6cm}
+	}
+	\only<2>{\begin{minipage}[b]{0.45\linewidth}
+			\begin{center}
+				\includegraphics[width=1.17\linewidth]{images/implementation.png}
+			\end{center}
+			\vspace{-0.2cm}
+			\tiny
+			\textbf{Figure 1.5:} The prototype and its components based on the architecture.
+			The time-series data flows first to the \texttt{Grafana} dashboard, \texttt{PostgreSQL} database and \texttt{Redis} cache as advised in ~\cite{DBLP:conf/sc/TaheriBPRHDEWPM24}.
+			\vspace{0.1cm}
+		\end{minipage}
+	}
 
 	% We decided to use discrete-event simulation, as opposed to computational fluid dynamics because of the high overheads of development time needed for CFD.
 	% CFD simply takes too long to run, making it unfeasible for real-time analytics and simulation.
@@ -162,27 +183,31 @@
 	% Failures that are more intensive are worse than failures with long duration.
 	\begin{tcolorbox}[title=Main Finding II]
 		We posit digital twinning can be used for failure detection to the benefit of DC operators.
-		We replicate an experiment from DyTwin~\cite{DBLP:conf/sc/TaheriBPRHDEWPM24} designed by Milojicic \etal to show our system can reliably detect \emph{unexpected} host failures.
+		We replicate an experiment from DyTwin~\cite{DBLP:conf/sc/TaheriBPRHDEWPM24} designed by Milojicic \etal to show \emph{Sunfish} can reliably detect \emph{unexpected} host failures.
 	\end{tcolorbox}
-	\hspace{-0.2cm}
-	\begin{minipage}[b]{0.45\linewidth}
-		\begin{center}
-			\includegraphics[width=1.1\textwidth]{images/red_yellow_alarms.pdf}
-		\end{center}
-		\vspace{-0.3cm}
-		\tiny
-		\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}
-	\begin{minipage}[b]{0.45\linewidth}
-		\begin{center}
-			\includegraphics[width=1.1\textwidth]{images/failure_detecton_rate.pdf}
-		\end{center}
-		\vspace{-0.3cm}
-		\tiny
-		\textbf{Figure 1.7b:} Experiment 1b. The mean failure detection rate is around 12\%. Even though this seems low, if we look at \textbf{Fig. E.1} (see Extra Slides), this simply means around 12\% of failures are unexpected.
-	\end{minipage}
+	\only<1-2>{
+		\hspace{-0.2cm}
+		\begin{minipage}[b]{0.45\linewidth}
+			\vspace{0.1cm}
+			\begin{center}
+				\includegraphics[width=1.1\textwidth]{images/red_yellow_alarms.pdf}
+			\end{center}
+			\vspace{-0.3cm}
+			\tiny
+			\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}
+	}
+	\only<2>{\begin{minipage}[b]{0.45\linewidth}
+			\begin{center}
+				\includegraphics[width=1.1\textwidth]{images/failure_detecton_rate.pdf}
+			\end{center}
+			\vspace{-0.3cm}
+			\tiny
+			\textbf{Figure 1.7b:} Experiment 1b. The mean failure detection rate is around 12\%. Even though this seems low, if we look at \textbf{Fig. E.1} (see Extra Slides), this simply means around 12\% 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.
@@ -199,33 +224,38 @@
 	\begin{tcolorbox}[title=Main Finding III]
 		Predicting failures in advance is really difficult. \emph{Sunfish} is capable of dynamic adjustments to the physical twin at runtime, and can slightly lower the  number of failed tasks.
 	\end{tcolorbox}
-	\hspace{-0.2cm}
-	\begin{minipage}[b]{0.45\linewidth}
-		\begin{center}
-			\includegraphics[width=1.1\textwidth]{images/failure_likelihood.pdf}
-		\end{center}
-		\vspace{-0.3cm}
-		\tiny
-		\textbf{Figure 1.8a:} Experiment 2a.  The figure shows which failure distribution is the most likely to be the true failure distribution while the simulation is running.
-		This figure shows the difficulty of predictive analytics.
-	\end{minipage}
-	\hspace{0.5cm}
-	\begin{minipage}[b]{0.45\linewidth}
-		\vspace{-0.1cm}
-		\begin{center}
-			\includegraphics[width=1.1\textwidth]{images/conceptual_experiment.pdf}
-		\end{center}
-		\vspace{-0.3cm}
-		\tiny
-		\textbf{Figure 1.8b:} Experiment 2b. With perfect precognition (\emph{i.e.,} knowing on which day, what failures might happen) we could lower the mean number of failures.
-		This experiment is a proof of concept (results are indication-only).
-	\end{minipage}
+	\only<1-2>{
+		\hspace{-0.2cm}
+		\begin{minipage}[b]{0.45\linewidth}
+			\vspace{0.2cm}
+			\begin{center}
+				\includegraphics[width=1.1\textwidth]{images/failure_likelihood.pdf}
+			\end{center}
+			\vspace{-0.3cm}
+			\tiny
+			\textbf{Figure 1.8a:} Experiment 2a.  The figure shows which failure distribution is the most likely to be the true failure distribution while the simulation is running.
+			This figure shows the difficulty of predictive analytics.
+		\end{minipage}
+		\hspace{0.5cm}
+	}
+	\only<2>{\begin{minipage}[b]{0.45\linewidth}
+			\vspace{-0.1cm}
+			\begin{center}
+				\includegraphics[width=1.1\textwidth]{images/conceptual_experiment.pdf}
+			\end{center}
+			\vspace{-0.3cm}
+			\tiny
+			\textbf{Figure 1.8b:} Experiment 2b. With perfect precognition (\emph{i.e.,} knowing on which day, what failures might happen) we could lower the mean number of failures.
+			This experiment is a proof of concept (results are indication-only).
+		\end{minipage}
+	}
 \end{frame}
 
 \begin{frame}\frametitle{Key Takeaways}
+	\vspace{-0.2cm}
 	\begin{tcolorbox}[title=Societal Context]
 		Datacenter manageability is a top-priority for the digital society.
-		Over 3 million jobs in the Netherlands directly depend on cloud services, which are hosted in datacenters~\cite{DBLP:journals/corr/IosupKLVG22}.
+		Over 3 million jobs in the Netherlands directly depend on cloud services, which 1are hosted in datacenters~\cite{DBLP:journals/corr/IosupKLVG22}.
 	\end{tcolorbox}
 
 	\begin{tcolorbox}[title=Problem Statement]
@@ -244,7 +274,7 @@
 	% Not enough space for another tcolorbox.
 \end{frame}
 
-\setcounter{framenumber}{4}
+\setcounter{framenumber}{2}
 \setbeamertemplate{footline}[page number]{}
 
 % Unfortunately this must remain here.
@@ -272,7 +302,7 @@
 	\end{tcolorbox}
 
 
-	\begin{tcolorbox}[title=How did we adjust OpenDC (Physical Twin)?]
+	\begin{tcolorbox}[title=How did we adjust OpenDC (Physical Twin)?]1
 		We use a SURF~\cite{DBLP:journals/fgcs/VersluisCGLPCUI23} datacenter topology with 277 hosts.
 		We wrote a custom Kotlin \texttt{ComputeMonitor} to export live-metrics into Kafka, and a custom Kotlin \texttt{HTTPClient} to talk to the digital twin.
 		We add a new scheduling mechanism, the \texttt{SmartScheduler}.
-- 
cgit v1.2.3

