Merge branch 'master' of https://git.overleaf.com/685bda73a26c47dd9a5fbbb0

2 files changed, 8 insertions, 3 deletions

diff --git a/content/conclusion.tex b/content/conclusion.tex
index b7694c8..fa90b3e 100644
--- a/content/conclusion.tex
+++ b/content/conclusion.tex
@@ -48,7 +48,7 @@ We answer the main research question by addressing each sub-research question.
 \begin{figure}[ht]
 	\centering
 	\includegraphics[width=0.8\textwidth]{images/48_years.pdf}
-	\caption[48 years of microprocessor trend data.]{48 years of microprocessor trend data. Legend: \textcolor{Orange}{$\blacktriangle$ Transistors (thousands)}, \textcolor{Blue}{$\lgblkcircle$ Single Thread Performance (SpecINT $\times 10^3$)}, \textcolor{Green}{$\lgblksquare$ Frequency (MHz)}, \textcolor{Maroon}{$\blacktriangledown$ Typical Power (Watts)}, $\mdlgblkdiamond$ Number of Logical Cores~\cite{DBLP:image/48Microprocessor/Rupp}.}
+	\caption[48 years of microprocessor trend data.]{48 years of microprocessor trend data. Legend: \textcolor{Orange}{$\blacktriangle$ Transistors (thousands)}, \textcolor{Blue}{$\lgblkcircle$ Single Thread Performance (SpecINT $\times 10^3$)}, \textcolor{Green}{$\lgblksquare$ Frequency (MHz)}, \textcolor{Maroon}{$\blacktriangledown$ Typical Power (Watts)}, $\mdlgblkdiamond$ Number of Logical Cores. (Source:~\cite{DBLP:image/48Microprocessor/Rupp}.)}
 	\label{fig:rupp_48_years_microprocessor_data}
 \end{figure}
 
diff --git a/content/intro.tex b/content/intro.tex
index 645d77a..11170ca 100644
--- a/content/intro.tex
+++ b/content/intro.tex
@@ -17,16 +17,20 @@ In the modern \gls{ai} economy, datacenters need diverse and scalable server arc
 %Nowadays, datacenter operators try to meet AI-customer expectations by adding more specialized hardware~\cite{DBLP:conf/date/MilojicicFDR21}, at the 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 computational requirements of \gls{ai} 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.
+\Cref{fig:explosive_growth_in_ai_computational_requirements} presents the speed and scale which drives the compute requirements of \gls{ai} models.
+From 2012 to 2024, the compute power needed to train the models grew by $10^{7}$, a trend that is expected to continue~\cite{DBLP:journals/computer/AthavaleBBMMPS24}.
 Because of this, datacenter complexity will continue to grow, and it will become more difficult to manage~\cite{DBLP:conf/icdcs/IosupUVAEHTBT18}.
 Future servers and software related services to them 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 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}.
+%In short, the 
+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}.
+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]
 	\hspace{-1.75cm}
@@ -34,6 +38,7 @@ To address this new problem a concept of a datacenter \gls{dt} was proposed~\cit
 	\includegraphics[width=0.8\linewidth]{images/ai_timeline.pdf}
 
 	\caption[Explosive growth in AI computational requirements.]{Explosive growth in AI computational requirements drives datacenter upgrades (source: NVIDIA Analysis: reproduction with NVIDIA permission by~\cite{DBLP:journals/computer/AthavaleBBMMPS24}). \emph{X}-axis presents the year, and the \emph{Y}-axis presents the training compute requirement (in petaFLOPs). The plot shows the one of the reasons behind why the complexity of datacenters is rising.}
+    \label{fig:explosive_growth_in_ai_computational_requirements}
 \end{figure}
 \section{Context}\label{s:context}