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diff --git a/content/conclusion.tex b/content/conclusion.tex index 1df3d95..fa90b3e 100644 --- a/content/conclusion.tex +++ b/content/conclusion.tex @@ -37,7 +37,7 @@ We answer the main research question by addressing each sub-research question. \item \emph{How to validate and evaluate a datacenter digital twin architecture in relation to system requirements?}\\ To answer the last research question we crated a prototype. - During the prototype design, we used state-of-the-practice software, such as \code{Confluent Kafka}, \code{Redis} and \code{PostgreSQL} (see \Cref{ss:implementation_overview}). + During the prototype design, we used state-of-the-practice software, such as \code{Confluent Kafka}, \code{Redis} and \code{PostgreSQL} (see \Cref{ss:detailed_implementation_overview}). However, as it turns out, evaluating \gls{dcdt}s is not a trivial task. Lacking the physical datacenter to experiment with, we came up with a novel digital twin evaluation method. Our method, relies solely on discrete-event simulation to model the physical datacenter, overcoming the problems of real-world experimentation (\eg sustainability, costliness, reproducibility). @@ -48,7 +48,7 @@ We answer the main research question by addressing each sub-research question. \begin{figure}[ht] \centering \includegraphics[width=0.8\textwidth]{images/48_years.pdf} - \caption[48 years of microprocessor trend data.]{48 years of microprocessor trend data. Legend: \textcolor{Orange}{$\blacktriangle$ Transistors (thousands)}, \textcolor{Blue}{$\lgblkcircle$ Single Thread Performance (SpecINT $\times 10^3$)}, \textcolor{Green}{$\lgblksquare$ Frequency (MHz)}, \textcolor{Maroon}{$\blacktriangledown$ Typical Power (Watts)}, $\mdlgblkdiamond$ Number of Logical Cores~\cite{DBLP:image/48Microprocessor/Rupp}.} + \caption[48 years of microprocessor trend data.]{48 years of microprocessor trend data. Legend: \textcolor{Orange}{$\blacktriangle$ Transistors (thousands)}, \textcolor{Blue}{$\lgblkcircle$ Single Thread Performance (SpecINT $\times 10^3$)}, \textcolor{Green}{$\lgblksquare$ Frequency (MHz)}, \textcolor{Maroon}{$\blacktriangledown$ Typical Power (Watts)}, $\mdlgblkdiamond$ Number of Logical Cores. (Source:~\cite{DBLP:image/48Microprocessor/Rupp}.)} \label{fig:rupp_48_years_microprocessor_data} \end{figure} @@ -70,7 +70,7 @@ To achieve the \gls{nasem} goals of digital twinning~\cite{DBLP:usdoe/report/AP2 A \gls{dcdt} must posses predictive capabilities, by definition~\cite{DBLP:usdoe/report/AP26894}. \item \textbf{What Is Missing?}\\ To power the predictions, we envision an \gls{ml}-based inference engine as a necessary component of digital twinning. - The need for \gls{ml} arises naturally in scenarios where large volumes of data, requiring little to no preprocessing meet the demand for estimating future facility behaviour~\cite{Wikipedia:PredictiveModelling,CambridgeUniversityPress:book/Deisenroth}. + The need for \gls{ml} arises naturally in scenarios where large volumes of data, requiring little to no preprocessing meet the demand for estimating future facility behaviour~\cite{Wikipedia:article/PredictiveModelling,CambridgeUniversityPress:book/Deisenroth}. However, currently there are no \gls{dcdt} deployments that model the warehouse using an \gls{ml} approach to predict events (see \Cref{tab:dt_features_comparison}). \item \textbf{The Next Steps}\\ In short, we stipulate \gls{dcdt}s should include \gls{ml} in their \gls{oda} analysis. |