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 Datacenter manageability is a top-priority for the digital society.
 Over 3 million jobs in the Netherlands directly depend on cloud services, which are hosted in datacenters~\cite{DBLP:journals/corr/IosupKLVG22}.
-Datacenter digital twinning, a promising management technique can offer unique insight into complex facility behaviour.
-In this thesis we pave the way to more advanced \gls{dcdt}s.
+Datacenter digital twinning, a promising management technique can offer unique insight into complex facility behaviour~\cite{DBLP:journals/computer/AthavaleBBMMPS24}.
+In this thesis we paved the way to more advanced \gls{dcdt}s.
 We contribute to the scientific community a set of findings that we hope will prove helpful in enabling predictive analytics in both existing \gls{dcdt}s and future projects.
 Starting from a thorough investigation into the new, emerging field of datacenter digital twinning, we designed a system capable of incorporating sophisticated data analysis techniques.
 We ended our project with a novel evaluation method used in a set of exhaustive experiments.
-As such, we believe we answer the main research question by addressing each sub-research question.
+We answer the main research question by addressing each sub-research question.
 
 \section{Answers to Research Questions}\label{ss:answers_to_rqs}
 
 \begin{enumerate}[label=\emph{RQ\textsubscript{\arabic*}}]
 	\item \emph{How to asses the current state-of-the-art of digital twinning for datacenters?}\\
 	      In order to answer this research question, we conducted a semi-structured literature review.
-	      Our findings indicate that the field of datacenter digital twinning is still under development.
-	      There exist few existing \gls{dcdt} deployments.
-	      The current efforts in modelling datacenters focus on very specialized parts of the datacenter management, \ie cooling and heat modelling, network mapping.
-	      These standalone systems fail to offer the holistic capabilities envisioned for \gls{dt}s.
-	      The results of the literature survey are in \Cref{tab:dt_features_comparison}.
-	      \Cref{tab:dt_features_comparison} contains systems which we found though a semi-structured literature review process.
+	      Our findings indicate that the field of datacenter digital twinning is still under development, and there exist few \gls{dcdt} deployments.
+	      The current efforts in modelling datacenters focus on very specialized parts of datacenter management, \ie cooling and heat modelling, network mapping.
+	      Many crucial features, inherent to the \gls{dt} definition are still missing from current \gls{dcdt}s.
+	      Present, standalone \gls{dcdt} systems fail to offer the holistic capabilities envisioned by the inventors of \gls{dt}s.
+	      The results of the literature survey are in \Cref{tab:dt_features_comparison}, which contains systems which we found through a semi-structured literature review process.
 	      We first used structured queries, followed by a mix of snowballing and manual search.
 	      As a result, the second contribution to answering research question 2 is a holistic system model that encompasses the features of all the systems from \Cref{tab:dt_features_comparison} (see \Cref{fig:system_model}).
 
@@ -33,13 +32,13 @@ As such, we believe we answer the main research question by addressing each sub-
 	\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 to evaluate our system.
 	      Lacking the physical datacenter to experiment with, we came up with a novel digital twin evaluation method, that uses discrete-event simulation to model the physical datacenter.
-	      Our main findings indicate that \gls{my_system} can reliably differentiate between large host failures and insignificant single host downtime using predictions based on the results from \code{OpenDC}, a state of the art datacenter modelling software.
-	      Moreover, we show that \gls{my_system} can be used to incorporate a predictive analytics system and significantly lower the total number of task failures during a workload.
+	      Our main findings indicate that \gls{my_system} can reliably differentiate between large host failures and insignificant downtime using predictions based on the results from \code{OpenDC}, a state of the art datacenter modelling software.
+	      Moreover, we show that \gls{my_system} can be used to incorporate predictive analytics systems and significantly lower the total number of task failures during a workload.
 \end{enumerate}
 
 \section{Future Work}\label{ss:future_work}
 
-We envision \gls{dcdt}s as systems that encompass features necessary to model the entire datacenter behaviour.
+We envision \gls{dcdt}s as systems that encompass features necessary to model the entire datacenter.
 It came to our attention that with the explosive growth of \gls{ai} and the diversification of datacenters under way, \gls{dt}s will be indispensable in datacenter management.
 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.