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\begin{document}
\begin{center}
	\Large My BSc Defence Script
\end{center}
\begin{enumerate}[label=\textbf{Slide \arabic*.}]
	\item \textbf{Introduction}\\
	      Good morning everyone, my name is Mateusz and today I will present to you my project \emph{Sunfish: Enabling Predictive Analytics For Datacenters Through Digital Twinning}.

	      At a top level, my project is about trying to ease datacenter management by trying to pave the way to predicting unexpected events.

	\item \textbf{Societal Impact}\\
	      As you know and as you will likely see in the upcoming presentations today, datacenters are important.
	      But, I would like to shortly mention this myself.



	      A single GPU is already very complex.
	      Within a Google Datacenter, there are hundreds of server racks, with tens of such GPUs.
	      This begs the question: How are we going to manage this large of a datacenter, that has so many \emph{layers of complexity}?

	      We cannot let these systems go down or experience big failures, because \eg in Netherlands over 3 million professionals depend daily on the cloud.
	      \todo{Read the slide box.}
	      As such, we must do something to manage datacenters well.


	\item \textbf{Problem Statement}\\
	      Digital Twinning pairs complex objects (like datacenters) via a two-way connection with their virtual representation.
	      \todo{Give example about the airplane from aviation.}
	      \emph{It a method to manage complex systems.}

	      However, in digital twinning, specifically datacenter digital twinning a lot of elements are still shifting about and there are a lot of ways to create the virtual models and there seems to not be a fully functioning DCDT out there (\emph{that meets the official NASM definition}).

	      DCDT's lack mandatory features one of which is predictive analytics.
	      Predictive Analytics is a type of ODA that draws insights into the future based on current data, \eg telling when a host failure might happen before it does (\emph{and yet it is NOT present in existing DCDTs}).

	\item \textbf{Research Questions}\\
	      We wish to enable the development of predictive analysis components for DCDT's by designing a predictive DCDT.
	      We ask the following research questions. \todo{Read from slide boxes.}

	\item \textbf{Literature Survey}\\
	\item \textbf{System Model}\\

	\item \textbf{Reference Architecture and Prototype}\\
	      A reference architecture.
	      The design is more specific than the reference architecture.

	\item \textbf{Novel Evaluation Method}\\
	      In order to evaluate a prototype, we propose a novel approach.
	      Many researchers do not have a real facility to experiment with.
	      We propose to use a second simulator to act as the real datacenter.

	      \todo{Say in order to not cram content into the presentation, we omit the technical setup, and include it in extra slides.}


	\item \textbf{Experiment 1: Red and Yellow Alarms}\\
	      For Experiment 1 we copy the idea of Milojicic \etal for different ways a DCDT can notify the datacenter.

	      Imagine a scenario: a datacenter will soon run a workload.
	      We want to detect and differentiate between failures that are big and unexpected and failures we anticipated would occur.

	      To achieve this: the DCDT runs the workload using the simulator.
	      We cannot know what kind of failures we can expect, so we use a statistical distribution to approximate what might occur in practice.
	      In result, we get a picture of what kind of problems we might expect.

	      We now use the real-time feedback loop to notify the DC operators that what is happening in reality is different from simulation.
	      If we get within 80\% of the predicted threshold for number of failures we send a yellow alarm.
	      If we get within 90\% we send a red alarm.
	\item \textbf{Experiment 2: Conceptual Experiment}\\
	\item \textbf{Key Takeaways}\\
	      \todo{Read from the slide.}
\end{enumerate}
\end{document}
