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@@ -30,6 +30,10 @@ Preventing failure-caused outages in advance could help datacenter operators red
 The high computational demand of \gls{ai} and the end of Dennard's scaling have resulted in the rise of larger and more heterogeneous datacenter architectures~\cite{DBLP:conf/date/MilojicicFDR21}.
 Both events create a need for more careful datacenter management to tackle the unprecedented complexity and ensure availability of all cloud services.
 To address this new problem a concept of a datacenter \gls{dt} was proposed~\cite{DBLP:journals/computer/AthavaleBBMMPS24}.
+% Explain DT here, very briefly
+A \gls{dt} is a virtual model of an intended or actual real-world system that serves as its counterpart for purposes such as simulation, integration, testing, monitoring and maintenance.
+The digital twin replicates the physical system to predict failures, prescribe real-time actions for mitigating unexpected events, observing and evaluating the behaviour of the system~\cite{Wikipedia:article/DigitalTwin}.
+%
 This thesis explores the use of digital twins to cope with increasing operational complexity in datacenters, by enabling both historical and online data to pass through the digital twin's analytical and predictive loops. 
 
 \begin{figure}[t]
@@ -44,25 +48,26 @@ This thesis explores the use of digital twins to cope with increasing operationa
 
 % A digital twin is often called a virtual twin.
 % The communication between a physical entity and the digital twin is referred to as a digital thread.
-A \gls{dt} is a virtual model of an intended or actual real-world system that serves as its counterpart for purposes such as simulation, integration, testing, monitoring and maintenance.
-The digital twin replicates the physical system to predict failures, prescribe real-time actions for mitigating unexpected events, observing and evaluating the behaviour of the system~\cite{Wikipedia:article/DigitalTwin}.
+%
+% A \gls{dt} is a virtual model of an intended or actual real-world system that serves as its counterpart for purposes such as simulation, integration, testing, monitoring and maintenance.
+% The digital twin replicates the physical system to predict failures, prescribe real-time actions for mitigating unexpected events, observing and evaluating the behaviour of the system~\cite{Wikipedia:article/DigitalTwin}.
 
-Most modern \gls{dt} usages are related to prognostics and system health management~\cite{DBLP:conf/cirp/TAO2018169}.
+Much modern \gls{dt} usages are related to prognostics and system health management~\cite{DBLP:conf/cirp/TAO2018169}.
 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.
 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 concept of a \gls{dt} began in 1960s, at the \gls{nasa}~\cite{Nature:article/Görtz2026}.
-\gls{nasa} pioneered the concept in order to debug issues with its spacecraft.
+\gls{nasa} pioneered the concept to debug issues with its spacecraft.
 However, the term ``digital-twin'' 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:simple_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.
 Today, research is focused on bridging the gap between the long-established foundations of \gls{dt}s and new, novel applications in academia and industry, such as the \gls{dcdt}~\cite{DBLP:conf/cirp/TAO2018169, DBLP:journals/computer/AthavaleBBMMPS24}.
 
-A \gls{dcdt} mirrors the structure, context and behaviour of a datacenter~\cite{DBLP:journals/computer/AthavaleBBMMPS24}.
+A \gls{dcdt} mirrors the structure, context, and behaviour of a datacenter~\cite{DBLP:journals/computer/AthavaleBBMMPS24}.
 The foundation to any digital twin is good monitoring and sensing capabilities in the physical entity.
 Datacenters, meet this requirement easily because they already connect hundreds of monitoring sensors.
-With hundreds of gigabytes of useful information coming from distributed \gls{iot} sensors inside the warehouse, we can gain insight into failure patterns, energy usage, heat dissipation \etc
+With hundreds of gigabytes of useful information coming from distributed \gls{iot} sensors inside the warehouse, we can gain insight into failure patterns, energy usage, heat dissipation, \etc
 What remains challenging is to connect the physical and virtual spaces with a bi-directional connection
 and to use the monitoring insights and data analysis results for autonomous decision-making.
 Crucial to \gls{dcdt} operation are predictive capabilities and the continuous interaction with the real-world datacenter.
@@ -70,7 +75,7 @@ Crucial to \gls{dcdt} operation are predictive capabilities and the continuous i
 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.
-
+%
 Nonetheless, existing \gls{dcdt}'s are still very limited in their capabilities as the definition and scope of a \gls{dcdt} concept is shallow and unclear.
 After all, only recently did the hardware capabilities needed to continuously simulate a datacenter become 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.
@@ -95,9 +100,13 @@ In this work, we address the lack of a unified \gls{dcdt} system model and the a
 We argue that the current state-of-the-art \gls{dcdt}'s lack sufficient predictive capabilities that are essential to real-time facility management of a modern datacenter.
 Because the main purpose of many \gls{dt}s is to forecast the short and long-term facility behaviour, \gls{dcdt} 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.
+%
 For example, preventing compute failures could greatly benefit datacenter operators.
-To enable insights from both historical data and immediate telemetry, we propose that digital twinning can be enhanced by integrating predictive analytics through \gls{oda}.
+
+
+%To enable insights from both historical data and immediate telemetry, we propose that digital twinning can be enhanced by integrating predictive analytics through \gls{oda}.
 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 particular, to enable insights from both historical data and immediate telemetry, we propose that digital twinning can be enhanced by integrating predictive analytics through \gls{oda}.
 Our solution to this problem encompasses different levels of \gls{oda} (\eg in-band analytics, out-of-band analytics) for holistic datacenter modelling.
 Together with a unified \gls{dcdt} system model and a revolutionary evaluation method of \gls{dcdt}'s, we hope to bring the modern vision of \gls{dt}'s to datacenters.
 
@@ -105,7 +114,7 @@ Together with a unified \gls{dcdt} system model and a revolutionary evaluation m
 
 \emph{Main Research Question:} How to enable predictive analytics in datacenters through digital twinning?\\
 
-\noindent We divide the problem of designing a predictive \gls{dcdt} into three research questions:
+\noindent We scope the problem of designing a predictive \gls{dcdt} by proposing three research questions:
 \begin{enumerate}[label=\emph{RQ\textsubscript{\arabic*}}, align=left, itemsep=0pt]
 	% First research question stolen from Capelin by Georgios Andreadis and adapted to my work.
 	\item \emph{How to assess the current state-of-the-art of digital twinning for datacenters?}\\
@@ -128,9 +137,9 @@ Together with a unified \gls{dcdt} system model and a revolutionary evaluation m
 \section{Research Methodology}\label{s:research-methodology}
 To answer \emph{RQ\textsubscript{1}} we conduct a literature review as proposed by \textit{Kitchenham et al.} \cite{DBLP:journals/infsof/KitchenhamPBBTNL10} along with the guidance of the supervisor.
 Firstly, we determine the right review method.
-Secondly, we identify the various works related to \gls{dcdt}'s using different search strings
+Secondly, we identify the various works related to \gls{dcdt}'s using different search strings~
 (\eg ``Datacenter Digital Twinning'', ``ICT Virtual Twin'') and query combinations (``Datacenter \code{AND} Maintenance'').
-To search for the results we use the digital libraries of Google Scholar, DBLP, ACM Digital Library, IEEExplore, Springer \etc
+To search for the results we use the digital libraries of Google Scholar, DBLP, ACM Digital Library, IEEExplore, Springer, \etc
 Thirdly, we select work relevant to our research and organize the details of each article into a table.
 A potential outcome of this could be a system model for \gls{dcdt}'s.
 We envision the literature review supplying us with potential use-cases for the predictive \gls{dcdt}.
@@ -168,16 +177,22 @@ We define the correct experiment setup(s) and perform the experiments on a speci
 		      \item We provide the experiment setup, validation and evaluation of \mysystem for detecting and predicting datacenter failures in real-time as an Open Science artifact.
 	      \end{enumerate}
 \end{enumerate}
+
 \section{Academic Integrity Declaration}\label{s:academic_integrity_declaration}
+
+The statements in this section address an ongoing and seemingly growing problem in academia, that of integrity breaches primarily through the generation of material using AI techniques, such as LLM services.
+
 \subsection{Non-Plagiarism Declaration}\label{ss:plagiarism-declaraion}
 I hereby declare that this thesis is my own independent work and writing.
 The thesis does not contain any material copied from other sources (person, Internet, or \gls{ai}), and has not been submitted for assessment elsewhere.
 I acknowledge that the usage of material from other works or paraphrase of such material without proper citations or credit will be treated as plagiarism.
 I declare that this thesis is free from \gls{ai} generated content and has been written without the help of any \gls{ai} tools.
-To order to adhere to the strictest restrictions on AI-usage in higher education, this work follows the Berkley School of Law Artificial Intelligence Policy, as stated in \url{https://www.law.berkeley.edu/wp-content/uploads/2026/05/AI-Final-Policy-26.pdf}.
+
+To adhere to the current (strictest) restrictions on AI-usage in higher education, this work follows the Berkley School of Law Artificial Intelligence Policy, as stated in \url{https://www.law.berkeley.edu/wp-content/uploads/2026/05/AI-Final-Policy-26.pdf}.
 
 \subsection{Preventing Reference Fraud}\label{ss:plagiarism_references}
-I hereby declare that all the references in this thesis refer to genuine scientific work published in peer-reviewed journals or other sources of reliable and safe online information (\eg Wikipedia articles) and have been used in accordance to the article authors' wishes.
+I hereby declare that all the references in this thesis refer to genuine scientific work published in peer-reviewed journals or other sources of reliable and safe online information~ (\eg Wikipedia articles) and have been used in accordance to the article authors' wishes.
+
 Additionally, under the guidance of the supervisor this work adheres to the strictest rules for referencing and to prove the originality of all references, each \BibTeX citation contains a \texttt{note} field with the following comment: \emph{This BibTeX citation comes from:} followed by the URL leading directly to the citation source.
 In case of citations not formatted in \BibTeX, the same format follows but with adequate reference-style name (\eg APA, Chicago, MLA).
 
@@ -194,11 +209,15 @@ This work addresses the four grand societal challenges related to this goal: \be
 	\item sustainability
 	\item usability
 \end{enumerate*}~\cite{DBLP:journals/corr/IosupKLVG22}.
-\mysystem addresses (1) directly by making large-scale datacenter management easier.
+
+\mysystem addresses goal (1) directly by making large-scale datacenter management easier.
+
 We address (2) by ensuring our work adheres to the \gls{fair} principles of Open Science.
 Moreover, in this thesis we try to make \gls{dcdt} systems more understandable to the broader scientific community by providing a unified system model.
 Additionally, we contribute to responsible software design by adhering to best software engineering practices in the design of the prototype.
-(3) is addressed indirectly, as the consequences of the insights provided by a holistic, \gls{oda} powered \gls{dcdt} can help datacenter managers make decisions that are more sustainable in the future.
+
+Goal (3) is addressed indirectly, as the consequences of the insights provided by a holistic, \gls{oda} powered \gls{dcdt} can help datacenter managers make decisions that are more sustainable in the future.
+
 We contribute to (4) by helping predict unexpected failures and lowering operational costs, ensuring datacenters can continue to be usable in the future.
 We believe this work has a strong societal impact due to addressing the four grand societal challenges described by Iosup \etal and we hope through this work we can advance the scientific research community towards a more sustainable future.
 Abiding the FAIR data principles, the entire source code of the prototype and related work has been made available at the \url{https://github.com/M-J-Kwiatkowski/opendc} and the \url{https://github.com/M-J-Kwiatkowski/sunfish} repositories.