From 90fe47fe94b455bbe02b3b017dd993af2ff24af1 Mon Sep 17 00:00:00 2001
From: mjkwiatkowski <mati.rewa@gmail.com>
Date: Sun, 28 Jun 2026 19:03:19 +0200
Subject: feat: a couple extra slides

---
 main.tex | 39 +++++++++++++++++++++++++++++++--------
 1 file changed, 31 insertions(+), 8 deletions(-)

(limited to 'main.tex')

diff --git a/main.tex b/main.tex
index d1b8bc0..444bd07 100644
--- a/main.tex
+++ b/main.tex
@@ -53,7 +53,7 @@
 
 \begin{frame}\frametitle{\textbf{RQ1}: Literature Review I}
 	\begin{tcolorbox}[title=Main Finding I]
-		The literature on DCDTs is sparse.
+		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.
@@ -81,6 +81,7 @@
 
 	\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
@@ -98,6 +99,7 @@
 		\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}
@@ -108,8 +110,8 @@
 		\end{center}
 		\vspace{-0.2cm}
 		\tiny
-		\textbf{Figure 1.5:} The prototype -- \emph{Sunfish}, and its components based on \textbf{Figure 1.4}.
-		The time-series data flows first to the \texttt{Grafana} dashboard, \texttt{PostgreSQL} database and \texttt{Redis} cache~\cite{DBLP:conf/sc/TaheriBPRHDEWPM24}.
+		\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}
 
@@ -262,8 +264,8 @@
 \begin{frame}\frametitle{Extra Slides: Technical Setup }
 	\begin{tcolorbox}[title=What is the simulation workload?]
 		The compute workload is BitBrainsSmall.
-		The failure traces include user reports from Gmail, WhatsApp, Facebook and Twitter.
-		For predictions we use \texttt{prefabs}~\cite{DBLP:journals/fgcs/VersluisCGLPCUI23}.
+		The failure traces include Gmail, WhatsApp, Facebook and Twitter.
+		For predictions we use different statistical distributions~\cite{DBLP:journals/fgcs/VersluisCGLPCUI23}.
 	\end{tcolorbox}
 	\begin{tcolorbox}[title=What is the experiment environment?]    A commodity laptop: Framework Laptop 13, with 32GB of DDR5 RAM and an AMD Ryzen 7840U processor and an ArchLinux OS with Linux 7.0.13-arch1-1 kernel.
 
@@ -282,6 +284,18 @@
 
 \end{frame}
 
+\begin{frame}\frametitle{Extra Slides: Experiment 1}
+	\begin{tcolorbox}[title=Clarification]
+		In experiment 1 we are able to differentiate between severe failures, that down more than some threshold $\tau$ hosts.
+		$\tau$ is determined using predictions based on potential distribution of failures, modeled with \textasciitilde\emph{N(1.5, 1.5)}.
+	\end{tcolorbox}
+
+	\begin{center}
+		\includegraphics[width=0.58\linewidth]{images/alarms_vs_failures.pdf}
+	\end{center}
+	\tiny
+	\textbf{Figure E.1:} The comparison between failures experienced and alarms raised.
+\end{frame}
 
 \begin{frame}\frametitle{Extra Slides: Why Digital Twinning?}
 	\begin{tcolorbox}[title=Definition]
@@ -312,11 +326,20 @@
 	\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: Experiment 2}
+	\begin{tcolorbox}[title=Statistical Distributions]
+		Different failure distributions were used in order to predict the true failure distribution.
+		\textbf{Table E.1} summarizes the distributions.
+	\end{tcolorbox}
+	\begin{center}
+		\includegraphics[width=\linewidth]{images/failure_models_table.png}
+	\end{center}
+	\tiny
+	\textbf{Table E.1:} Different failure models used throughout this  project.
+	All failure models come from Javadi \etal (for a more thorough overview see~\cite{DBLP:journals/jpdc/JavadiKIE13})
+\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.
-
 \end{document}
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