test: test commit

2 files changed, 20 insertions, 30 deletions

diff --git a/content/background.tex b/content/background.tex
index 7e89d77..9d0f6ac 100644
--- a/content/background.tex
+++ b/content/background.tex
@@ -23,6 +23,15 @@ A prime example of using probability to find a good machine learning model is Ba
 % Stanford Encyclopedia of Philosophy, Douven 2017
 The process of inference from data to provide the best explanation is called abduction.
 
+
+%What is below here is true, but nonetheless the argumentation should be slightly changed. And a citation is needed.
+However, there has been little effort made to integrate analytics that enable consistent and reliable prediction of datacenter behaviour into a holistic digital twin of a datacenter.
+Nor has the fidelity of failure modeling inside a datacenter simulation increased.
+The failure model is still a linear model.
+Since a datacenter simulator is quite different from a digital twin, we cannot use the same computation methods (not as they are right now, at least) -- we must adapt them.
+The prediciton models are the same ones for the digital twin as the ones used for the datacenter simulator.
+Since a digital twin is not a standalone simulator, a change to how we both predict and model failures is necessary.
+
 \ipsum[1-2]
 
 \input{sources/simulator_comparison.tex}
@@ -34,9 +43,9 @@ The process of inference from data to provide the best explanation is called abd
 % Here talk a bit about different types of data analytics that are performed in a digital twin.
 ``A \emph{digital twin} is a set of virtual information constructs that mimics the structure, context and behaviour of a natural, engineered or social system, is dynamically updated with data from its physical twin, has predictive capability, and informs decisions that realize value''~\cite{DBLP:usdoe/report/AP26894}.
 A crucial characteristic that differentiates digital twinning from simulation and statistical modelling is the \emph{digital thread}: a bi-directional channel that enables continuous interaction between the virtual and physical entities.
-An example \gls{dt} architecture is depicted in Figure \ref{fig:five_dimensional_dt} Section \ref{s:intro} from Tao \etal~\cite{DBLP:conf/cirp/TAO2018169}.
-
 The longer the \gls{dt} is working, the more accurate its predictions, because a holistic twin aggregates historical patterns together with up-to-date monitoring data.
+A generic \gls{dt} architecture is depicted in Figure \ref{fig:five_dimensional_dt} Section \ref{s:intro} from Tao \etal~\cite{DBLP:conf/cirp/TAO2018169}.
+
 % Why has not anyone done this before?
 Digital twinning has only recently become feasible because of the developments in \gls{hpc}.
 Between 2003 and 2011 the compute needed to run a Digital Twin was simply not present.
@@ -56,35 +65,14 @@ As a result, digital twins have become more relevant today than 10 years ago~\ci
 \subsection{Digital Twins across Domains}\label{sss:digital_twins_across_domains}
 
 \subsection{Digital Twins for Datacenters}\label{sss:digital_twins_for_datacenters}
+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
+Moreover, CPU profiling, VM uptime, workload type enable datacenter managers to leverage \gls{oda} to gain meaningful insights into datacenter operation.
+But currently one of the key challenges is to somehow connect the physical and virtual spaces with a bi-directional connection, that aims to use the monitoring insights and data analysis results to make autonomous decisions.
+\gls{dcdt}'s emerged to address this problem.
 
 
-One of the key arguments that speak for a datacenter digital twin is that datacenters already connect hundreds of monitoring sensors and data coming from them.
-Monitoring of server racks, VM's, CPU profiling and all that give us lots of data.
-
-Data analytics, such as ODA can give actual meaningful insights into what we are doing.
-Moreover, advanced technologies have made sensors, IoT give us much information.
-ODA can predict failures, help maintain the equipment, save bills, cut costs.
-But currently one of the key challenges is to somehow connect the physical and virtual spaces.
-The answer to how to do this is a digital twin.
-
-%[citation needed]
-
-%Why predictive analytics? Why predictive behaviour?
-
-%What is below here is true, but nonetheless the argumentation should be slightly changed. And a citation is needed.
-However, there has been little effor made to integrate analytics that enable consistent and relaible prediction of datacenter behaviour into a holistic digital twin of a datacenter.
-Nor has the fidelity of failure modeling inside a datacenter simulation increased.
-The failure model is still a linear model.
-Since a datacenter simulator is quite different from a digital twin, we cannot use the same computation methods (not as they are right now, at least) -- we must adapt them.
-The prediciton models are the same ones for the digital twin as the ones used for the datacenter simulator.
-Since a digital twin is not a standalone simulator, a change to how we both predict and model failures is necessary.
-
-
-Because of judgement born out of experience, evolution of existing datacenters is fairly successful; however the development of a new, modern datacenters is fraught with unexpected problems that results in weight growth, schedule delays and cost overruns.
-Optimal datacenter management is characterized by high service availability and low downtime.
-Achieving this in a 21\textsuperscript{st} century datacenter requires revolutionary changes in the way datacenters are operated and maintained.
-A concept that creates just such a revolutionary change is the \gls{dcdt}.
-
 \input{sources/dt_features_comparison.tex}
 
 ExaDigiT~\cite{DBLP:conf/sc/BrewerMKWBHSGGW24} is an open-source framework for developing digital twins of supercomputers.
@@ -139,3 +127,5 @@ DyTwin~\cite{DBLP:conf/sc/TaheriBPRHDEWPM24} is an adaptive digital twin with vi
 \end{figure}
 
 
+
+
diff --git a/content/intro.tex b/content/intro.tex
index 2a077bc..edf2915 100644
--- a/content/intro.tex
+++ b/content/intro.tex
@@ -30,7 +30,7 @@ To address this new problem a concept of a datacenter \gls{dt} was proposed~\cit
 	\centering
 	\includegraphics[width=0.8\linewidth]{images/simple_dt.pdf}
 	\caption{Elements of the digital twin ecosystem~\cite{DBLP:modsim24/presentation/Iosup2024}.}
-        \label{fig:simple_dt}
+	\label{fig:simple_dt}
 \end{figure}
 
 \section{Context}\label{s:context}