feat: added a new remote at denounce.ai

2 files changed, 31 insertions, 10 deletions

diff --git a/content/background.tex b/content/background.tex
index 8adcae5..4884995 100644
--- a/content/background.tex
+++ b/content/background.tex
@@ -2,7 +2,6 @@
 
 \section{Overview}\label{s:background_overview}
 
-
 \section{Datacenters}\label{ss:datacenters}
 
 \subsection{Computing Infrastructure}\label{sss:failures}
@@ -36,7 +35,6 @@ Since a digital twin is not a standalone simulator, a change to how we both pred
 
 \input{sources/simulator_comparison.tex}
 \section{Digital Twinning}\label{ss:digital-twinning}
-
 % To fix: remove the \gls commands for ExaDigiT.
 % This is getting silly.
 \subsection{What is Digital Twinning?}\label{sss:what_is_digital_twinning}
@@ -65,13 +63,10 @@ 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.
 
+In this section, we survey the work related to datacenter digital twinning.
+We summarize our results in Table \ref{tab:dt_features_comparison} to compare and contrast the features of existing datacenter digital twins.
+We select only the digital twins that adhere closest to the \gls{nasem} definition~\cite{DBLP:usdoe/report/AP26894}.
 
 \input{sources/dt_features_comparison.tex}
 
@@ -115,6 +110,17 @@ DyTwin~\cite{DBLP:conf/sc/TaheriBPRHDEWPM24} is an adaptive digital twin with vi
 % Documentation: https://learn.microsoft.com/en-us/azure/digital-twins/
 % Moreover, NVIDIA is doing too as well https://www.nvidia.com/en-sg/omniverse/
 
+Many \gls{dcdt}'s model the cooling systems inside the warehouse, because in a typical datacenter cooling accounts for more than 40\% of total electricity usage~\cite{DBLP:conf/AppliedEnergy/Zhao20}.
+Since the cooling subsystem is mainly airflow-based, \gls{dt} designers often opt for a \gls{cfd} approach to model the facility.
+The reason why a digital twin might be needed for a cooling subsystem is primarily because of inefficient operational strategy.
+The cooling system parameters are often set constant, regardless of outdoor temperature \etc~\cite{DBLP:conf/AppliedEnergy/Zhao20}.
+%Zhang \etal argues that their system is akin to an IoT sensor, essentially.
+% This is an important consideration -- DT is not simply a sensor, it must have predictive capabilities and be able to simulate the future.
+% Zhang argues that ``digital twin services'' are enabled by simulation monitoring \etc.
+% Nonetheless, I dub that they are primarily data analysis services.
+
+\gls{oda} can be performed in-band (real-time) and out-of-band (from historical data).
+Likewise, Zhao \etal shows that crucial to the digital twin system are ``always-on'' analytics (akin to in-band \gls{oda}) and ``on-demand`` analytics (akin to out-of-band \gls{oda}).
 
 %Include something about data-preprocessing in the pipeline.
 %See the article by Fei Tao
@@ -126,6 +132,15 @@ DyTwin~\cite{DBLP:conf/sc/TaheriBPRHDEWPM24} is an adaptive digital twin with vi
 	\label{fig:system_model}
 \end{figure}
 
+The design of DyTwin~\cite{DBLP:conf/sc/TaheriBPRHDEWPM24}  incorporates in its architecture a ``virtual-to-virtual`` digital thread between different digital twins.
+Zhao \etal include this element in their architecture too~\cite{DBLP:conf/AppliedEnergy/Zhao20}.
+Moreover, a crucial parallel between the work of Zhao \etal and ExaDigiT is the concept of multiple models within a single digital twin.
+Brewer \etal argue ExaDigiT is compromised of 5 ``smaller'' twins too.
+
+%In Zhang \etal the digital twin can communicate with different other digital twins, as in the work of Taheri \etal.
+%To do this, the working program has an API, with a specific API endpoint to communicate with other Digital Twins.
+%In your work, consider adding such an endpoint, albeit explain in future work that you envision \emph{implementing} this endpoint in the future.
+
 
 
 
diff --git a/content/intro.tex b/content/intro.tex
index 57d3003..79d604f 100644
--- a/content/intro.tex
+++ b/content/intro.tex
@@ -51,7 +51,13 @@ Due to insufficient technological foundations, little work is available on \gls{
 Today, research is focused on bridging the gap between the long-established foundations of \gls{dt}s and new, novel applications in academia and industry, such as the \gls{dcdt}~\cite{DBLP:conf/cirp/TAO2018169, DBLP:journals/computer/AthavaleBBMMPS24}.
 
 A \gls{dcdt} mirrors the structure, context and behaviour of a datacenter~\cite{DBLP:journals/computer/AthavaleBBMMPS24}.
+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
+What remains challenging is to connect the physical and virtual spaces with a bi-directional connection
+to use the monitoring insights and data analysis results for autonomous decision-making.
 Crucial to \gls{dcdt} operation are predictive capabilities and the continuous interaction with the real-world datacenter.
+
 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.
@@ -72,7 +78,7 @@ However, predicting datacenter behaviour quickly and reliably is a non-trivial p
 
 
 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.
+In this work, we address the lack of a unified \gls{dcdt} system model 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.
@@ -173,7 +179,7 @@ This work addresses the four grand societal challenges related to this goal: \be
 	\item usability
 \end{enumerate*}~\cite{DBLP:journals/corr/IosupKLVG22}.
 \gls{my_system} addresses (1) directly by making large-scale datacenter management easier.
-We address (2) by ensuring our work adheres to the FAIR principles of Open Science.
+We address (2) by ensuring our work adheres to the \gls{fair} principles of Open Science.
 Moreover, in this thesis we try to make \gls{dcdt} systems more understandable to the broader scientific community by providing a unified system model.
 Additionally, we contribute to responsible software design by adhering to best software engineering practices in the design of the prototype.
 (3) is addressed indirectly, as the consequences of the insights provided by a holistic, \gls{oda} powered \gls{dcdt} can help datacenter managers make decisions that are more sustainable in the future.