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@@ -50,7 +50,7 @@
\begin{tcolorbox}[title=Research Question 3]
% no "and validate?"
- How to evaluate a datacenter digital twin architecture in relation to system requirements?
+ How to validate and evaluate a datacenter digital twin architecture in relation to system requirements?
\end{tcolorbox}
\end{frame}
@@ -99,7 +99,7 @@
% Change to Datacenter (Physical Twin)
\includegraphics[width=1.15\textwidth]{images/ref_architecture.pdf}
\end{center}
- \vspace{-0.2cm}
+ \vspace{-0.15cm}
\tiny
\textbf{Figure 1.4:} The predictive datacenter digital twin reference architecture.
The architecture was designed with the \emph{AtLarge Design Process}~\cite{DBLP:conf/icdcs/IosupVTETBFMT19}.
@@ -108,13 +108,13 @@
\hspace{0.8cm}
\begin{minipage}[b]{0.45\linewidth}
\begin{center}
- \includegraphics[width=1.15\linewidth]{images/implementation.png}
+ \includegraphics[width=1.17\linewidth]{images/implementation.png}
\end{center}
\vspace{-0.2cm}
\tiny
\textbf{Figure 1.5:} The prototype 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}.
- \vspace{0.2cm}
+ \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.
@@ -199,7 +199,7 @@
Our contributions are: a thorough literature survey with a system model, a DCDT reference architecture, and prototype-based experiments via a novel evaluation method.
\end{tcolorbox}
- \begin{tcolorbox}[colbacktitle=red!70!black, colback=red!20!white,title=What did we find?]
+ \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.
\end{tcolorbox}
% Mandatory to mention here the future work that you see happening.