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\documentclass[12pt, a4paper]{article}
\usepackage{palatino, enumitem, parskip, xspace}
\usepackage[dvipsnames]{xcolor}
\newcommand{\eg}{\emph{e.g.,}\xspace}
\newcommand{\todo}[1]{\textcolor{Blue}{\textbf{TODO(#1)}}}
\newcommand{\etal}{\emph{et~al.}\xspace}
\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}
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