From 6972313a119d094f3f592c2df3715c00d22a3cae Mon Sep 17 00:00:00 2001
From: mjkwiatkowski <mati.rewa@gmail.com>
Date: Tue, 14 Jul 2026 13:20:43 +0200
Subject: fix: fixed a problem with the zathura file viewer

---
 content/background.tex | 73 +++++++++++++++++++++++++++++++++++++-------------
 1 file changed, 54 insertions(+), 19 deletions(-)

(limited to 'content/background.tex')

diff --git a/content/background.tex b/content/background.tex
index 8e63f53..5ab53dc 100644
--- a/content/background.tex
+++ b/content/background.tex
@@ -1,18 +1,17 @@
 \chapter{Background}\label{s:background}
-
 \section{Overview}\label{ss:background_overview}
 \begin{mynote}
 	The contribution in this chapter is three-fold:
 
 	\begin{enumerate}[label=\emph{C\textsubscript{\arabic*}}, itemsep=0.2pt]
 		\item We provide a brief overview on datacenter simulation (\Cref{sss:simulation}), compute failures (\Cref{sss:failures}), and digital twinning (\Cref{sss:what_is_digital_twinning}).
-		\item We survey the state-of-the-art concerning datacenter digital twinning (\Cref{sss:digital_twins_for_datacenters}).
+		\item We survey the state-of-the-art concerning datacenter digital twinning (\Cref{ss:digital_twins_for_datacenters}).
 		\item We construct a system model for existing datacenter digital twins (\Cref{ss:system_model_for_dcdts})
 	\end{enumerate}
 \end{mynote}
 
 \section{Datacenters}\label{ss:datacenters}
-In this section we provide a short background on datacenter simulation and hardware failures.
+In this section we provide a short background on datacenter simulation and compute failures.
 We find it useful to provide a brief introduction to both topics so as to ensure reader's fullest understanding of subsequent chapters.
 Since datacenters are important building blocks of the digital society, reliable warehouse management is a key priority for datacenter operators.
 Incorrect management decisions can lead to missed \gls{sla}s~\cite{DBLP:journals/corr/IosupKLVG22} and even large financial penalties~\cite{DBLP:report/AnnualOutageAnalysis2025}.
@@ -41,7 +40,7 @@ Further details about {OpenDC} can be referred to in the linked literature \cite
 
 \subsection{Compute Failures}\label{sss:failures}
 A failure is defined as ``an event that makes a system fail to operate according to its specifications``~\cite{DBLP:journals/jpdc/JavadiKIE13}.
-We distinguish 2 failure types: \begin{enumerate*}
+We distinguish 2 failure types: \begin{enumerate*}[label=(\arabic*)]
 	\item software failures
 	\item hardware failures.
 \end{enumerate*}
@@ -60,12 +59,6 @@ In summary OpenDC enables experimentation with failures that enables insights th
 However, the fidelity of failure modeling inside a datacenter simulation is still insufficient to predict in failures in real-time, as they happen in a physical datacenter.
 Since a datacenter simulator is quite different from a digital twin, we cannot use the same computation methods from simulation to predict real-time failures.
 Digital twinning is an improvement upon pure simulation.
-
-\section{Digital Twinning}\label{ss:digital-twinning}
-In this section we explore how the datacenter management can be improved using a novel modelling technique, digital twinning.
-We present the generic, field-agnostic \gls{dt} definition and investigate how \emph{datacenter} digital twinning applies the definition in practice.
-
-\subsection{What is Digital Twinning?}\label{sss:what_is_digital_twinning}
 \begin{figure}[t]
 	\centering
 	\includegraphics[width=0.95\linewidth]{images/five_dimensional_dt.pdf}
@@ -73,11 +66,17 @@ We present the generic, field-agnostic \gls{dt} definition and investigate how \
 	%Fei Tao is a renowned figure with over 62k citations. He is a figure of authority on digital twins.%
 	\label{fig:five_dimensional_dt}
 \end{figure}
+\section{Digital Twinning}\label{ss:digital-twinning}
+In this section we explore how the datacenter management can be improved using a novel modelling technique, digital twinning.
+We present the generic, field-agnostic \gls{dt} definition and investigate how \emph{datacenter} digital twinning applies the definition in practice.
+
+\subsection{What is Digital Twinning?}\label{sss:what_is_digital_twinning}
+
 % Here talk a bit about different types of data analytics that are performed in a digital twin.
 ``A \emph{digital twin} is a set of virtual information constructs that mimics the structure, context and behaviour of a natural, engineered or social system, is dynamically updated with data from its physical twin, has predictive capability, and informs decisions that realize value''~\cite{DBLP:usdoe/report/AP26894}.
 A crucial characteristic that differentiates digital twinning from simulation and statistical modelling is the \emph{digital thread}: a bi-directional channel that enables continuous interaction between the virtual and physical entities.
 The longer the \gls{dt} is working, the more accurate its predictions, because a holistic twin aggregates historical patterns together with up-to-date monitoring data.
-A generic \gls{dt} architecture is depicted in Figure \ref{fig:five_dimensional_dt} Section \ref{s:intro} from Tao \etal~\cite{DBLP:conf/cirp/TAO2018169}.
+A generic \gls{dt} architecture is depicted in Figure \ref{fig:five_dimensional_dt} from Tao \etal~\cite{DBLP:conf/cirp/TAO2018169}.
 
 % Why has not anyone done this before?
 Digital twinning has only recently become feasible because of the developments in \gls{hpc}.
@@ -93,10 +92,10 @@ A crucial part any of any \gls{dt} is \emph{predictive modelling}, which drives
 	\label{fig:predictive_analytics}
 \end{figure}
 Predictive modelling uses statistics to predict outcomes.
-When deployed commercially, for example in datacenters, predictive modelling is often referred to as predictive analytics~\cite{Wikipedia:PredictiveModelling}.
-Almost any statistical model can be used for prediction purposes, but nowadays predictive analysis is synonymous with machine learning.
+When deployed commercially, for example in datacenters, predictive modelling is often referred to as predictive analytics~\cite{Wikipedia:article/PredictiveModelling}.
+Almost any statistical model can be used for prediction purposes, but nowadays predictive analysis is synonymous with \gls{ml}.
 A primary example of popular analysis type is linear regression.
-However, any modelling technique, \eg \emph{discrete-event simulation} can be used to make the predictions.
+However, any modelling technique (\eg \emph{discrete-event simulation}) can be used to make the predictions.
 
 Predictive analysis belongs to a larger domain of \gls{oda}.
 \gls{oda} is the ``use of operational data instrumentation, analysis, integration, and archiving, towards effective design, commissioning, and optimization of datacenter operations'' \cite{DBLP:conf/icppw/BourassaJBCJVS19}.
@@ -108,15 +107,53 @@ There exist several \gls{oda} frameworks, for example OMNI \cite{DBLP:conf/icppw
 
 A major limitation of predictive analytics is that history cannot always predict the future.
 Using historical data to predict outcomes works only under the assumption that there are certain long lasting patterns in the system.
-Additionally, no matter how extensive is the training data, there is always the possibility of new variables that have not been considered or even defined, yet are critical to the outcome of the prediction~\cite{Wikipedia:PredictiveModelling}.
+Additionally, no matter how extensive is the training data, there is always the possibility of new variables that have not been considered or even defined, yet are critical to the outcome of the prediction~\cite{Wikipedia:article/PredictiveModelling}.
 
-\subsection{Digital Twins for Datacenters}\label{sss:digital_twins_for_datacenters}
+\section{Literature Survey of Digital Twins for Datacenters}\label{ss:digital_twins_for_datacenters}
 In this section, we survey the work related to datacenter digital twinning.
 We summarize our results in Table \ref{tab:dt_features_comparison} to compare and contrast the features of existing datacenter digital twins.
 We select only the digital twins that adhere closest to the \gls{nasem} definition~\cite{DBLP:usdoe/report/AP26894}.
 
+\subsection{Methodology}\label{sss:method}
+The aim of this survey is to search and organize the field of \gls{dcdt}s.
+In this subsection, we describe the methods for collecting relevant scientific articles and present the design of the system model for generic \gls{dcdt}s.
+
+\begin{enumerate}[label=\textbf{\arabic*.}, align=left]
+	\item \textbf{Review Strategy}\\
+	      The most common methods for conducting literature surveys are \begin{enumerate*}[label=(\arabic*)]
+		      \item random traversal of the related literature,
+		      \item snowballing~\cite{ACM:article/Webster2002},
+		      \item systematic literature survey as proposed by Kitchenham \etal~\cite{DBLP:journals/infsof/KitchenhamPBBTNL10}
+	      \end{enumerate*}~\cite{DBLP:conf/wosp/SumanCNTMI24}
+	      Random traversal encompasses surveying the field by following suggestions from portals like Google Scholar and randomly querying the different databases.
+	      It is an unstructured way to conduct the literature review, and requires little effort.
+	      Snowballing is similar to random traversal, but it is more structured.
+	      The surveyor follows references from the relevant articles, and there is a depth limit~\cite{DBLP:conf/wosp/SumanCNTMI24}.
+	      Systematic literature survey is a rigorous, fully-structured process to searching for literature, and it follows the method devised by Barbara Kitchenham~\cite{DBLP:journals/infsof/KitchenhamPBBTNL10}.
+	      In our work, to scope down the project we chose a mix of (1) and (2), with some elements of (3) instead of following solely the systematic literature review process of Kitchenham \etal.
+	      Therefore, our literature cannot be regarded as systematic, instead we can refer to it as comprehensive or semi-structured.
+	\item \textbf{Analysis of Selected Material}\\
+	      We borrow the process of Suman \etal conducted during his MSc thesis for a literature survey of \gls{oda}~\cite{DBLP:conf/wosp/SumanCNTMI24}.
+	      The process can be described as follows.
+	      \begin{enumerate*}[label=(\arabic*)]
+		      \item first, search given queries followed by a manual inspection of the context of the article
+		      \item then scan each article, by reading over the abstract, introduction and conclusion and decide whether it applies to \gls{dcdt}s.
+		      \item after selection, extract the details of the \gls{dcdt} from the publication by reading carefully over the article.
+		      \item lastly, interpret the functionality of the \gls{dcdt}s and systematically organize them.
+	      \end{enumerate*}
+	\item \textbf{Design of the System Model}\\
+	      Based on the findings of the literature survey, we create a conceptual model of the \gls{dcdt} field.
+	      We decided to create a system model, as the field of \gls{dcdt}s is still under development, and does not include many digital  twin deployments.
+	      An alternative to a system model would be a taxonomy.
+	      To create the system model, we first gathered the functionality present in all the \gls{dcdt}s.
+	      For each \gls{dcdt} feature in every article, we evaluated whether this feature is present in other deployments and how important it is for the \gls{dt}.
+	      The result of this process is a set of \gls{dcdt} features that belong to the largest proportion of all \gls{dcdt}s.
+	      Afterwards, for each of the features, we decide how it is interconnected.
+\end{enumerate}
+
 \input{sources/dt_features_comparison.tex}
 
+\subsection{Advanced Digital Twins for Datacenters}\label{sss:advanced_dts}
 ExaDigiT~\cite{DBLP:conf/sc/BrewerMKWBHSGGW24} is an open-source framework for developing digital twins of supercomputers.
 It consists of 3 modules:
 \begin{enumerate*}[label=(\arabic*)]
@@ -183,7 +220,7 @@ Kalibre takes the best of both \gls{ml} and \gls{cfd} approaches and achieves su
 % Documentation: https://learn.microsoft.com/en-us/azure/digital-twins/
 % Moreover, NVIDIA is doing too as well https://www.nvidia.com/en-sg/omniverse/
 
-\section{System Model for Datacenter Digital Twinning}
+\subsection{System Model for Datacenter Digital Twinning}
 \label{ss:system_model_for_dcdts}
 
 \begin{figure}[t]
@@ -210,13 +247,11 @@ Kalibre takes the best of both \gls{ml} and \gls{cfd} approaches and achieves su
 %Include something about data-preprocessing in the pipeline.
 %See the article by Fei Tao
 
-
 %Moreover, a crucial parallel between the work of Zhao \etal and ExaDigiT is the concept of multiple models within a single digital twin.
 %Brewer \etal argue ExaDigiT is compromised of 5 ``smaller'' twins too.
 %In Zhang \etal the digital twin can communicate with different other digital twins, as in the work of Taheri \etal.
 %To do this, the working program has an API, with a specific API endpoint to communicate with other Digital Twins.
 
-
 %In your work, consider adding such an endpoint, albeit explain in future work that you envision \emph{implementing} this endpoint in the future.
 
 \section{Discussion}\label{ss:background_discussion}
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