feat: added the experimental results plots

14 files changed, 224 insertions, 142 deletions

diff --git a/build/main.aux b/build/main.aux
index 6d4d5f6..92d17be 100644
--- a/build/main.aux
+++ b/build/main.aux
@@ -38,11 +38,9 @@
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@@ -59,16 +57,20 @@
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 \bibcite{DBLP:conf/cirp/TAO2018169}{\hyperlink {beamerbibDBLP:conf/cirp/TAO2018169}{14}}
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diff --git a/build/main.bbl b/build/main.bbl
index 1eb45d2..02b2ccd 100644
--- a/build/main.bbl
+++ b/build/main.bbl
@@ -96,6 +96,12 @@ Fei Tao, Meng Zhang, Yushan Liu, and A.Y.C. Nee.
 \newblock \showISSN{0007-8506}.
 \newblock \ifshowURL {\showURL \path|https://www.sciencedirect.com/science/article/pii/S0007850618300799|}\fi.
 
+\bibitem{DBLP:journals/fgcs/VersluisCGLPCUI23}
+Laurens Versluis, Mehmet {\c{C}}etin, Caspar Greeven, Kristian Laursen, Damian Podareanu, Valeriu Codreanu, Alexandru Uta, and Alexandru Iosup.
+\newblock Less is not more: We need rich datasets to explore.
+\newblock {\em Future Gener. Comput. Syst.}, 142:\penalty0 117--130, 2023.
+\newblock \ifshowURL {\showURL \path|https://doi.org/10.1016/j.future.2022.12.022|}\fi.
+
 \bibitem{DBLP:conf/sensys/WangZD0TCWZ20}
 Ruihang Wang, Xin Zhou, Linsen Dong, Yonggang Wen, Rui Tan, Li~Chen, Guan Wang, and Feng Zeng.
 \newblock Kalibre: Knowledge-based neural surrogate model calibration for data center digital twins.
diff --git a/build/main.blg b/build/main.blg
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-Output written on build/main.pdf (17 pages, 42269818 bytes).
+ 128i,16n,123p,1334b,973s stack positions out of 10000i,1000n,20000p,200000b,200000s
+</usr/share/texmf-dist/fonts/type1/public/fontawesome5/FontAwesome5Free-Solid.pfb></usr/share/texmf-dist/fonts/type1/public/inconsolata/Inconsolata-zi4r.pfb></usr/share/texmf-dist/fonts/type1/public/stix/stix-mathfrak.pfb></usr/share/texmf-dist/fonts/type1/public/stix/stix-mathit.pfb></usr/share/texmf-dist/fonts/type1/public/stix/stix-mathsf.pfb></usr/share/texmf-dist/fonts/type1/public/stix/stix-mathtt.pfb></usr/share/texmf-dist/fonts/type1/urw/helvetic/uhvb8a.pfb></usr/share/texmf-dist/fonts/type1/urw/helvetic/uhvr8a.pfb></usr/share/texmf-dist/fonts/type1/urw/helvetic/uhvro8a.pfb>
+Output written on build/main.pdf (17 pages, 42284012 bytes).
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+ 578 PDF objects out of 1000 (max. 8388607)
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diff --git a/build/main.nav b/build/main.nav
index 26cc246..b5fd25b 100644
--- a/build/main.nav
+++ b/build/main.nav
@@ -20,14 +20,14 @@
 \headcommand {\beamer@framepages {10}{10}}
 \headcommand {\slideentry {0}{0}{11}{11/11}{}{0}}
 \headcommand {\beamer@framepages {11}{11}}
-\headcommand {\slideentry {0}{0}{12}{12/12}{}{0}}
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-\headcommand {\slideentry {0}{0}{12}{12/15}{}{0}}
-\headcommand {\beamer@framepages {12}{15}}
+\headcommand {\slideentry {0}{0}{11}{11/12}{}{0}}
+\headcommand {\beamer@framepages {11}{12}}
+\headcommand {\slideentry {0}{0}{11}{11/13}{}{0}}
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+\headcommand {\slideentry {0}{0}{11}{11/14}{}{0}}
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 \headcommand {\slideentry {0}{0}{13}{16/16}{}{0}}
 \headcommand {\beamer@framepages {16}{16}}
 \headcommand {\slideentry {0}{0}{14}{17/17}{}{0}}
@@ -36,4 +36,4 @@
 \headcommand {\beamer@subsectionpages {1}{17}}
 \headcommand {\beamer@sectionpages {1}{17}}
 \headcommand {\beamer@documentpages {17}}
-\headcommand {\gdef \inserttotalframenumber {11}}
+\headcommand {\gdef \inserttotalframenumber {10}}
diff --git a/build/main.pdf b/build/main.pdf
index 9e4966b..1b4e575 100644
--- a/build/main.pdf
+++ b/build/main.pdf
diff --git a/build/main.synctex.gz b/build/main.synctex.gz
index 7f89046..dda369f 100644
--- a/build/main.synctex.gz
+++ b/build/main.synctex.gz
diff --git a/images/25_Jun_2026_152341.pdf b/images/25_Jun_2026_152341.pdf
new file mode 100644
index 0000000..e659026
--- /dev/null
+++ b/images/25_Jun_2026_152341.pdf
diff --git a/images/25_Jun_2026_161052.pdf b/images/25_Jun_2026_161052.pdf
new file mode 100644
index 0000000..aa95c65
--- /dev/null
+++ b/images/25_Jun_2026_161052.pdf
diff --git a/indent.log b/indent.log
index cc4ae90..eba8fbd 100644
--- a/indent.log
+++ b/indent.log
@@ -1,6 +1,6 @@
 INFO:  latexindent version 3.24.7, 2025-08-15, a script to indent .tex files
        latexindent lives here: /usr/share/texmf-dist/scripts/latexindent/
-       Thu Jun 25 15:25:01 2026
+       Thu Jun 25 21:58:58 2026
        Reading input from STDIN
 INFO:  Processing switches:
 INFO:  Directory for backup files and log file indent.log:
diff --git a/main.bib b/main.bib
index dcc9980..5aebe34 100644
--- a/main.bib
+++ b/main.bib
@@ -695,3 +695,19 @@
                         }",
         note = "[Online; accessed 10-June-2026]",
 }
+
+@article{DBLP:journals/fgcs/VersluisCGLPCUI23,
+        author = {Laurens Versluis and Mehmet {\c{C}}etin and Caspar Greeven and
+                  Kristian Laursen and Damian Podareanu and Valeriu Codreanu and
+                  Alexandru Uta and Alexandru Iosup},
+        title = {Less is not more: We need rich datasets to explore},
+        journal = {Future Gener. Comput. Syst.},
+        volume = {142},
+        pages = {117--130},
+        year = {2023},
+        url = {https://doi.org/10.1016/j.future.2022.12.022},
+        doi = {10.1016/J.FUTURE.2022.12.022},
+        timestamp = {Sat, 13 May 2023 01:06:24 +0200},
+        biburl = {https://dblp.org/rec/journals/fgcs/VersluisCGLPCUI23.bib},
+        bibsource = {dblp computer science bibliography, https://dblp.org},
+}
diff --git a/main.tex b/main.tex
index 2ff5706..700c64f 100644
--- a/main.tex
+++ b/main.tex
@@ -55,7 +55,7 @@
 \end{frame}
 
 \begin{frame}\frametitle{\textbf{RQ1}: Literature Review I}
-	\begin{tcolorbox}[title=Main Finding]
+	\begin{tcolorbox}[title=Main Finding I]
 		The literature on DCDTs is scarce.
 		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}.
@@ -105,7 +105,7 @@
 		The architecture was designed with the \emph{AtLarge Design Process}~\cite{DBLP:conf/icdcs/IosupVTETBFMT19}.
 		\vspace{0.2cm}
 	\end{minipage}
-	\hspace{0.8cm}
+	\hspace{0.6cm}
 	\begin{minipage}[b]{0.45\linewidth}
 		\begin{center}
 			\includegraphics[width=1.17\linewidth]{images/implementation.png}
@@ -123,7 +123,8 @@
 	% Consider adding this minipage directly to the ``draw.io'' diagram
 \end{frame}
 % You should skip \hfill completely or in favour of \hspace very minimally.
-\begin{frame}\frametitle{\textbf{RQ3}: Experimental Setup}
+\begin{frame}\frametitle{\textbf{RQ3}: Experimental Setup I}
+	\hspace{-0.3cm}
 	\begin{minipage}[b]{0.45\linewidth}
 		\begin{tcolorbox}[title=Problem, colbacktitle=red!70!black,colback=red!20!white]
 			We cannot just go and test digital twins on large systems, because we do not have large systems at hand.
@@ -150,6 +151,7 @@
 	\end{minipage}
 \end{frame}
 
+
 \begin{frame}\frametitle{\textbf{RQ3}: Experimental Results I}
 	% You have some model, and this can be based on multiple traces.
 	%Get insight from CINECA --> you get a probability of certain hosts failing.
@@ -157,32 +159,52 @@
 	%If you incorporate that? If you can make the case that because of our new digital twin we can incorporate such models, anomaly/failure detection, from CINECA.
 	%If we had that in, we can reach these kinds of gains.
 	% @Mateusz there is really not a possibility to incorporate CINECA's models, so to address Dante's feedback, I created this experiment.
-
-	\begin{tcolorbox}[title=Validation]
+	% If a single host crashes for the entire workload, that's not really that bad.
+	% If a lot of hosts suddenly crash but for a really short time, that's terrible.
+	% 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 validate our system against DyTwin~\cite{DBLP:conf/sc/TaheriBPRHDEWPM24} designed by Milojicic \etal to show we achieve similar results.
+		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.
 	\end{tcolorbox}
+	\hspace{-0.2cm}
 	\begin{minipage}[b]{0.45\linewidth}
 		\begin{center}
-			\includegraphics[width=1.1\textwidth]{images/23_Jun_2026_102028.pdf}
+			\includegraphics[width=1.1\textwidth]{images/25_Jun_2026_152341.pdf}
 		\end{center}
 		\vspace{-0.3cm}
 		\tiny
-		\textbf{Figure 1.5:} Experiment 1 Setup: The Digital Twin estimates the failures based on the Normal Distribution \emph{N\textasciitilde($\mu$,$\sigma$)} with $\mu = 1.5$ and $\sigma = 0.5$.
-		``Real'' OpenDC failures come from a WhatsApp user reports.
+		\textbf{Figure 1.7:} 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.
+	\end{minipage}
+	\hspace{0.6cm}
+	\begin{minipage}[b]{0.45\linewidth}
+		\begin{center}
+			\includegraphics[width=1.1\textwidth]{images/25_Jun_2026_161052.pdf}
+		\end{center}
+		\vspace{-0.3cm}
+		\tiny
+		\textbf{Figure 1.8:} Experiment 1b. The mean failure detection rate is around 15\%. Even though this seems low, if we look at \textbf{Fig. 1.9} (see extra slides), this simply means around 15\% 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.
 \end{frame}
 
-\begin{frame}\frametitle{\textbf{RQ3}: Experimental Results II}
-	\begin{tcolorbox}[title=Evaluation]
-		Predictive analytics is core to digital twinning. We evaluate our system against the requirements (extra slides) by predicting an optimal scheduling policy.
-		During runtime, we make dynamic adjustments to the physical twin, if the scheduling results differ.
-	\end{tcolorbox}
-
-\end{frame}
+%\begin{frame}\frametitle{\textbf{RQ3}: Experimental Results II}
+%	\begin{tcolorbox}[title=Evaluation]
+%		Predictive analytics is core to digital twinning. We evaluate our system against the requirements (extra slides) by predicting an optimal scheduling policy.
+%		During runtime, we make dynamic adjustments to the physical twin, if the scheduling results differ.
+%	\end{tcolorbox}
+%	\hspace{0.2cm}
+%	\begin{minipage}[b]{0.32\linewidth}
+%		\begin{center}
+%			\includegraphics[width=1.1\textwidth]{images/23_Jun_2026_102028.pdf}
+%		\end{center}
+%		\vspace{-0.3cm}
+%		\tiny
+%		\textbf{Figure 1.9:} Experiment 1
+%	\end{minipage}
+%\end{frame}
 
 \begin{frame}\frametitle{Key Takeaways}
 	\begin{tcolorbox}[title=What is the societal context?]
@@ -200,14 +222,15 @@
 	\end{tcolorbox}
 
 	\begin{tcolorbox}[title=What did we find?]
-		\emph{Sunfish} is able to detect around 20\% of unexpected failures based on discrete-event predictions, and can predict the most efficient scheduling policies for given workloads.
+		\emph{Sunfish} can reliably detect unexpected failures based on discrete-event predictions, and can serve as a foundation for additional research and future work.
 	\end{tcolorbox}
 	% Mandatory to mention here the future work that you see happening.
 	% Not enough space for another tcolorbox.
 \end{frame}
 
-\setcounter{framenumber}{5}
-\setbeamertemplate{footline}[page number]{
+\setcounter{framenumber}{3}
+\setbeamertemplate{footline}[page number]{}
+
 
 % Unfortunately this must remain here.
 \setbeamercolor{frametitle}{fg=Brown,bg=Brown!20}
@@ -217,13 +240,36 @@
 		\usebeamerfont{frametitle}\insertframetitle\hfill
 	\end{beamercolorbox}
 }
-
 \begin{frame}[allowframebreaks]\frametitle{Extra Slides: References}
 	\tiny
 	\bibliographystyle{is-plain}
 	\bibliography{main.bib}
 \end{frame}
 
+\begin{frame}\frametitle{Technical Setup }
+	\begin{tcolorbox}[title=What is the simulation workload?]
+		The compute workload is BitBrainsSmall.
+		The failure traces include user reports from Gmail, WhatsApp and Twitter.
+
+	\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.
+
+	\end{tcolorbox}
+
+
+	\begin{tcolorbox}[title=How did we adjust OpenDC (Physical Twin)?]
+		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}.
+
+	\end{tcolorbox}
+	\begin{tcolorbox}[title=Which metrics do we measure?]
+		Timestamps, host names, uptime, downtime, CPU utilization \emph{etc.}
+	\end{tcolorbox}
+
+\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.
@@ -253,6 +299,9 @@
 	\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.