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| author | mjkwiatkowski <mati.rewa@gmail.com> | 2026-06-22 08:54:23 +0200 |
|---|---|---|
| committer | mjkwiatkowski <mati.rewa@gmail.com> | 2026-06-22 08:54:23 +0200 |
| commit | f062b0967b126c80af7968aebd7db3b0e5779f7a (patch) | |
| tree | e79f5c6d1e280aceba0e59492390369a76cef500 /main.tex | |
| parent | 862effeee0e9ccdc4f94354a0f20df9a99c8823e (diff) | |
feat: added an additional slide for experimental setup
Diffstat (limited to 'main.tex')
| -rw-r--r-- | main.tex | 26 |
1 files changed, 23 insertions, 3 deletions
@@ -103,14 +103,34 @@ \end{center} \vspace{-0.2cm} \tiny - \textbf{Figure 1.4:} The predictive datacenter digital twin architecture. The time-series data flows initially to the \texttt{Kibana} dashboard, \texttt{PostgreSQL} database and \texttt{Redis} cache, as suggested in~\cite{DBLP:conf/sc/TaheriBPRHDEWPM24}. - \end{minipage} + \textbf{Figure 1.4:} The predictive datacenter digital twin architecture. \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. % Citing ExaDigit: [CFD] they are also more computationally expensive, generally making real-time operation unfeasible. % Consider adding this minipage directly to the ``draw.io'' diagram \end{frame} +\begin{frame}\frametitle{\textbf{RQ3}: Experimental Setup} + \begin{minipage}[b]{0.45\linewidth} + \begin{center} + \includegraphics[width=1.2\linewidth]{images/predictive_analyticsv2.pdf} + \end{center} + \vspace{-0.3cm} + \tiny + \textbf{Figure 1.5:} Evaluating DCDTs is difficult. To answer \textbf{RQ3} we provide a novel way to evaluate datacenter digital twins through discrete-event simulation. + \end{minipage} + \hfill + \begin{minipage}[b]{0.45\linewidth} + \begin{center} + \includegraphics[width=0.7\linewidth]{images/scrs.jpg} + \end{center} + \vspace{-0.2cm} + \tiny + \textbf{Figure 1.6:} The software stack used to implement \emph{Sunfish}. + The time-series data flows initially to the \texttt{Grafana} dashboard, \texttt{PostgreSQL} database and \texttt{Redis} cache, as suggested in~\cite{DBLP:conf/sc/TaheriBPRHDEWPM24}. + \end{minipage} +\end{frame} + \begin{frame}\frametitle{\textbf{RQ3}: Experimental Results I} \begin{tcolorbox}[title=Main Finding I] On average, \emph{Sunfish} achieves 12.17\% less failures per task than baseline (OpenDC). @@ -162,7 +182,7 @@ \end{frame} -\setcounter{framenumber}{3} +\setcounter{framenumber}{4} \setbeamertemplate{footline}[page number]{ % Unfortunately this must remain here. |
