From 9c07f07c50ef6a93ac72a593ac087b01de7ec125 Mon Sep 17 00:00:00 2001
From: mjkwiatkowski <mati.rewa@gmail.com>
Date: Tue, 2 Jun 2026 13:32:49 +0200
Subject: feat: added a new figure

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 content/intro.tex | 88 ++++++++++++++++++++++++++++++++++++++++---------------
 1 file changed, 64 insertions(+), 24 deletions(-)

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@@ -1,7 +1,7 @@
 \chapter{Introduction}\label{s:intro}
-Presently, computer and network ecosystems play a crucial part in the digital industry.
-The transport, education and government sectors largely depend on server-side services, which are hosted in datacenters~\cite{DBLP:journals/corr/IosupKLVG22}.
-To address the recent rise in demand for computation, due to the Artificial Intelligence revolution, managers expand datacenters with new components and more heterogeneous architectures (\eg GPUs, NPUs)~\cite{DBLP:conf/date/MilojicicFDR21}.
+Presently, computer and network systems play a crucial part in the digital industry.
+The transport, education and government sectors largely depend on digital services, which are hosted in datacenters~\cite{DBLP:journals/corr/IosupKLVG22}.
+To address the recent rise in demand for computation, due to the advancements in Artificial Intelligence, managers expand datacenters with new components and more heterogeneous architectures (\eg GPUs, NPUs)~\cite{DBLP:conf/date/MilojicicFDR21}.
 However, in return datacenter complexity increases significantly.
 To make better operational decisions despite the massive scale, promising technologies arise such as Datacenter Digital Twins~\cite{DBLP:journals/computer/AthavaleBBMMPS24}.
 
@@ -11,21 +11,20 @@ Over 3 million jobs in the Netherlands directly depend on cloud services, which
 Since many public services continue to move online (\eg online administration and taxation, education), the fraction of Dutch professionals who depend on the cloud for work will exceed 35\% by 2025~\cite{DBLP:journals/corr/IosupKLVG22}.
 
 % What is changing?
-In the modern \gls{ai} economy datacenters need diverse and scalable server architectures, because inference-based workloads require more heterogenous server components (GPUs, TPUs, NPUs \etc) to perform well.
+In the modern \gls{ai} economy datacenters need diverse and scalable server architectures, because inference-based workloads require more heterogeneous server components (GPUs, TPUs, NPUs \etc) to perform well.
 Nowadays, datacenter operators try to meet customer expectations by adding more specialized hardware~\cite{DBLP:conf/date/MilojicicFDR21}, at a cost of increased system complexity.
 In return, operating a modern datacenter warehouse with thousands of diversified servers presents a difficult challenge that requires fast and well-informed decisions from on-site engineers.
 
-The \gls{ai} computational requirements are expected to increase in the future~\cite{DBLP:journals/computer/AthavaleBBMMPS24}.
+The computational requirements of \gls{ai} are expected to increase in the future~\cite{DBLP:journals/computer/AthavaleBBMMPS24}.
 Datacenter complexity will continue to grow, and it will become more difficult to manage.
-Future servers will include more specialized hardware, which, while improving datacenter performance, will exhibit behaviour that is harder to predict.
+Future servers will include even more specialized hardware, which, while improving datacenter performance, will exhibit behaviour that is harder to predict.
 Already the rapid expansion of datacenters has increased the presence of service failures across all cloud services~\cite{DBLP:conf/acsos/TalluriOVTI21}.
 Preventing failure-caused outages in advance could help datacenter operators reduce operational costs, as over 20\% of all reported outages amount to more than 1 million US\$~\cite{DBLP:report/AnnualOutageAnalysis2025}.
 %Moreover, datacenter outages can have catastrophic consequences, cite Fabian.
 
-In short, the high computational demand and the end of Dennard's scaling have resulted in the rise of larger and more heterogeneous datacenter architectures~\cite{DBLP:conf/date/MilojicicFDR21}.
+In short, 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.
-Specific goals that can help satisfy these needs involve maximising the monitoring insights to help make better informed decisions and minimizing the downtime caused by maintenance and hardware failures.
-To address these problems a concept of a datacenter \gls{dt} was proposed~\cite{DBLP:journals/computer/AthavaleBBMMPS24}.
+To address this new problem a concept of a datacenter \gls{dt} was proposed~\cite{DBLP:journals/computer/AthavaleBBMMPS24}.
 
 \begin{figure}
 	\centering
@@ -41,10 +40,10 @@ To address these problems a concept of a datacenter \gls{dt} was proposed~\cite{
 
 % 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 digital model of an intended or actual real-world system that serves as a digital counterpart of it 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 profile of the system~\cite{WIKI:page/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{WIKI:page/DigitalTwin}.
 
-Most of modern \gls{dt} usages are related to prognostics and system health management~\cite{DBLP:conf/cirp/TAO2018169}.
+Most 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.
@@ -60,35 +59,61 @@ 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.
-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}.
+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.
 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.
+\gls{dcdt}'s are urgently needed, because 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.
+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} and deployments~\cite{DBLP:conf/sc/BrewerMKWBHSGGW24}.
+
+
+\section{Problem statement}\label{s:problem-statement}
+
 
+
+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.
+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.
 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}
 
+\textbf{Main Research Question} How to define, design and evaluate a predictive \gls{dcdt}?\\
+
+\noindent We divide the problem of designing a predictive \gls{dcdt} into three research questions:
 \begin{enumerate}[label=\textbf{RQ\arabic*.}, align=left]
-	\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}}?
+	\item \textbf{How to define a \gls{dcdt}?}\\
+	      There is currently a lack of a unified definition of what constitutes a \gls{dcdt}, and the differences between a \gls{dcdt} and a generic \gls{dt}.
+	      It is necessary that we establish a common definition of a \gls{dcdt} in the research community.
+	      We must develop a holistic \gls{dcdt} model that factors in the necessary components of a \gls{dt}.
+	      This is very challenging, because the \gls{dcdt} definition must address many kinds of operational and technical requirements, compatible with the existing background on \gls{dt}'s.
+	\item \textbf{How to design a \gls{dcdt} system model using discrete-event simulation and predictive data analysis?}
+	      Existing \gls{dcdt} frameworks lack the necessary predictive capabilities to prevent unplanned behaviour in datacenters.
+	      In this work, we aim to explore the design space of a predictive \gls{dcdt} and the different design trade-offs.
+	      Through discrete-event simulation, we aim provide the foundation for the system model to interact with a physical datacenter.
+	      This is a very challenging task, because there are many functional and non-functional requirements of a \gls{dcdt} that need careful consideration.
+	      The architecture must comply with the generic \gls{dt} model and address the non-trivial challenges in operating a modern datacenter.
+	\item \textbf{How to evaluate and validate a \gls{dcdt} model in relation to system requirements}?
+	      To understand the operation of the proposed system and whether it meets its design goals we need to measure it's performance.
+	      This is a challenging and non-trivial task that requires a careful design of a set of experiments that realistically show datacenter digital twin workings.
 
 \end{enumerate}
 
 \section{Research Methodology}\label{s:research-methodology}
 
+To answer the first research question we will conduct a standard literature review proposed by \textit{Kitchenham et al.} \cite{DBLP:journals/infsof/KitchenhamPBBTNL10} along with the guidance of the supervisor. Firstly, we will need to plan the review. This includes specifying the review research questions and determining the right review method. Secondly, we will need to conduct the review and find the 5 potential digital twin use-cases. This includes selecting the articles, reading and reporting the findings. Lastly, we will format the final report and based on the found use-cases, we will formulate the functional and non-functional requirements for the system model.
+
+To answer the second research question we will follow the \textit{AtLarge Design Process} \cite{DBLP:conf/icdcs/IosupVTETBFMT19}. This means, that under the guidance of the supervisor we will adopt the steps from the design process itself. First, after finishing the literature review, we will brainstorm the functional and non-functional requirements. Later, we will specify the pragmatic and innovative design possibilities. A possible outcome might be a digital twin reference architecture. Lastly, we will need to ensure that the design is scientific and testable and can be evaluated with comprehensive experiments.
+
+\par Afterwards, we will need to implement a prototype of the designed system model. An example approach would start with exporting the collected monitoring data from the ODA time-series database. The ODAbler \cite{DBLP:conf/wosp/SumanCNTMI24} framework enables the interaction between a python client, that acts as a user interface, with a server, that acts as a data centre. Upon creating the request, monitoring data is forwarded through Telegraf and Kafka to InfluxDB.
+
+In order to create a data centre digital twin we will need to ensure continuous communication between the time-series database and the data centre simulator. Later will need to interpret the exported traces, and choose the right simulator configuration. The current approach to this problem is unknown, therefore we will need to spent time designing the right software program. The last task will involve running the simulation, continuously within the feedback loop and providing the client with a visual user interface that provides interpreted results.
+
+To answer the third research question, we will need to design comprehensive experiments that validate our system model design. The approach to this problem is still unknown, therefore we will need to spent time designing the right experiments. We will need to define the correct experiment setup and perform the experiments on a specified hardware.
+
 \section{Thesis Contributions}\label{s:thesis-contributions}
 
 \section{Plagiarism Declaration}\label{s:plagiarism-declaraion}
@@ -97,4 +122,19 @@ The thesis does not contain any material copied from other sources (person, Inte
 
 \section{Societal Impact}\label{s:societal-impact}
 
+\section{Open Science}\label{s:open-science}
+Abiding the FAIR data principles, the entire source code of the prototype and related work has been made available at the \url{https://git.denounce.ai/opendc.git} repository.
+The reuse and reproduction of experiments is explained in a detailed guide at the root of the repository, along with the necessary dependencies and experimental setup.
+
 \section{Thesis Structure}\label{s:thesis-structure}
+The remainder of the thesis is structured as depicted in Figure \ref{fig:thesis_structure}.
+In Chapter \ref{s:background}, we describe the relevant background information.
+In Chapter \ref{s:design}, we present the design of \gls{dcdt}.
+In Chapter \ref{s:evaluation} we evaluate a prototype of the system and validate it against the set of functional and non-functional requirements.
+In Chapter \ref{s:conclusion} we conclude the thesis with a summary of contributions and potential future work.
+\begin{figure}
+	\centering
+	\includegraphics[width=\linewidth]{images/thesis_structure.pdf}
+	\caption{Structure of this thesis, with suggested reading flows.}
+	\label{fig:thesis_structure}
+\end{figure}
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