From 49a67e13524a6e5ec289606e2055e350c7415263 Mon Sep 17 00:00:00 2001
From: mjkwiatkowski <mati.rewa@gmail.com>
Date: Tue, 2 Jun 2026 10:38:09 +0200
Subject: feat: changed rq1

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 content/intro.tex | 33 ++++++++++++++++++++++-----------
 1 file changed, 22 insertions(+), 11 deletions(-)

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@@ -48,7 +48,7 @@ Most of modern \gls{dt} usages are related to prognostics and system health mana
 For example, in aerospace engineering, the \gls{dt} analyzes operational data (\eg temperature, vibration) to predict when a airplane component is likely to fail.
 The \gls{dt} can reliably manage the health of the physical entity by detecting fatigue cracks on aircraft wings or damage to the wind turbine blades~\cite{DBLP:journal/IJAE/Teugel2011}.
 This allows maintenance to be scheduled proactively, reducing unplanned downtime and preventing catastrophic failures.
-A forecast of future maintenance and virtual health management are the prime purpose of many \gls{dt}s used in practice~\cite{DBLP:conf/AIAA/Teugel2012}.
+Forecasting future maintenance and managing the physical health of an object or facility are the prime purpose of many \gls{dt}s used in practice~\cite{DBLP:conf/AIAA/Teugel2012}.
 
 The first mention of a \gls{dt} dates back to 2003, when Dr. Michael Grieves of Dassault Syst\'emes introduced the 3 core components of a \gls{dt}: the virtual entity, physical entity and the two-way connection (see Figure \ref{fig:five_dimensional_dt}).
 Due to insufficient technological foundations, little work is available on \gls{dt}s between 2003 and 2018, and it is only with the rapid growth of cloud computing, \gls{iot} and Big Data analytics that \gls{dt}s have re-emerged.
@@ -56,24 +56,35 @@ Today, research is focused on bridging the gap between the long-established foun
 
 A \gls{dcdt} mirrors the structure, context and behaviour of a datacenter~\cite{DBLP:journals/computer/AthavaleBBMMPS24}.
 Crucial to \gls{dcdt} operation are predictive capabilities and the continuous interaction with the real-world datacenter.
-There already exist digital twin deployments.
+There already exist \gls{dcdt} deployments.
 For example, ExaDigiT~\cite{DBLP:conf/sc/BrewerMKWBHSGGW24} is a framework for digital twin development of supercomputers.
 It has been demonstrated at the Frontier supercomputer and it facilitates virtual prototyping and system optimization.
-Quick and correct decision-making in a 21\textsuperscript{st} century datacenter is a hard task.
-Oftentimes unexpected events such as \eg service failures or hardware faults result in a downtime that disturbs the users and produces unfulfilled \gls{sla}~\cite{DBLP:conf/acsos/TalluriOVTI21}.
-However, predicting datacenter behaviour quickly and reliably is a non-trivial problem that remains insufficiently unaddressed in the existing \gls{dcdt} architectures ~\cite{DBLP:conf/wosp/SumanCNTMI24, DBLP:journals/computer/AthavaleBBMMPS24}.
 
-\section{Problem statement}\label{s:problem-statement}
-
-In this work we argue that the current state-of-the-art Datacenter Digital Twins lack sufficient predictive capabilities that are essential to real-time facility management of a modern datacenter.
+Nonetheless, existing \gls{dcdt}'s are still very limited in their capabilities.
+The concept of a novel \gls{dcdt} is still under development.
+It is only recently that the hardware capabilities needed to simulate a datacenter continuously became available~\cite{DBLP:conf/cirp/TAO2018169}.
+Many \gls{dcdt} frameworks still lack critical data analysis components, fault detection mechanisms, profiling techniques \etc~\cite{DBLP:conf/wosp/SumanCNTMI24}, rendering them unusable in large-scale systems.
+Such limitations gravely reduce the applicability of \gls{dcdt}'s in real world scenarios~\cite{DBLP:journals/corr/IosupKLVG22}.
+In practice, datacenters exhibit hundreds unexpected events every day,such as \eg service failures or hardware faults.
+Downtime, which is the result of failures, disturbs the users and produces unfulfilled \gls{sla}~\cite{DBLP:conf/acsos/TalluriOVTI21}.
+Predicting datacenter behaviour quickly and reliably is a non-trivial problem that remains insufficiently unaddressed in the existing \gls{dcdt} architectures ~\cite{DBLP:conf/wosp/SumanCNTMI24, DBLP:journals/computer/AthavaleBBMMPS24}.
+We envision \gls{dcdt}'s as systems indispensable in future datacenters, actively interacting with the real-world facility, lowering operational costs and predicting hardware failure and software faults.
+
+In this work, we address the lack of a unified \gls{dcdt} definition and the absence of predictive capabilities in existing \gls{dcdt} system designs.
+A \gls{dt} without predictive capabilities cannot maintain the health of the datacenter effectively.
+We posit that including holistic predictive analysis in \gls{dcdt} design can aid in efficient datacenter management and prevent missing \gls{sla}'s.
+We argue that the current state-of-the-art Datacenter Digital Twins lack sufficient predictive capabilities that are essential to real-time facility management of a modern datacenter.
 We propose that digital twinning can be enhanced by integrating predictive analytics through \gls{oda}.
 
+\section{Problem statement}\label{s:problem-statement}
+
 \section{Research Questions}\label{s:research-questions}
 
 \begin{enumerate}[label=\textbf{RQ\arabic*.}, align=left]
-	\item \textbf{How to define 5 \gls{dcdt} use-cases and their functional and non-functional requirements?}
-	\item \textbf{How to design a \gls{dcdt} system model using discrete-event simulation and operational data analysis?}
-	\item \textbf{How to validate if the \gls{dcdt} system meets the functional and non-functional requirements?}
+	\item \textbf{How to define a \gls{dcdt}?}
+	\item \textbf{How to design a \gls{dcdt} using discrete-event simulation and predictive data analysis?}
+	\item \textbf{How to evaluate and validate a predictive analytics system of a \gls{dcdt}}?
+
 \end{enumerate}
 
 \section{Research Methodology}\label{s:research-methodology}
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