\chapter{Conclusion}\label{s:conclusion}

Datacenter manageability is a top-priority for the digital society.
Over 3 million jobs in the Netherlands directly depend on cloud services, which are hosted in datacenters~\cite{DBLP:journals/corr/IosupKLVG22}.
Datacenter digital twinning, a promising management technique can offer unique insight into complex facility behaviour~\cite{DBLP:journals/computer/AthavaleBBMMPS24}.
In this thesis we paved the way to more advanced \gls{dcdt}s.
We contribute to the scientific community a set of findings that we hope will prove helpful in enabling predictive analytics in both existing \gls{dcdt}s and future projects.
Starting from a thorough investigation into the new, emerging field of datacenter digital twinning, we designed a system capable of incorporating sophisticated data analysis techniques.
We ended our project with a novel evaluation method used in a set of exhaustive experiments.
We answer the main research question by addressing each sub-research question.

\section{Answers to Each Research Question}\label{ss:answers_to_rqs}

\begin{enumerate}[label=\emph{RQ\textsubscript{\arabic*}}]
	\item \emph{How to asses the current state-of-the-art of digital twinning for datacenters?}\\
	      To answer this research question, we conducted a semi-structured literature review.
	      Our findings indicate that the field of datacenter digital twinning is still under development, and there exist few \gls{dcdt} deployments.
	      The current efforts in modelling datacenters focus on very specialized parts of datacenter management, \ie cooling and heat modelling, network mapping.
	      Many crucial features, inherent to the \gls{dt} definition are still missing from current \gls{dcdt}s.
	      Present, standalone \gls{dcdt} systems fail to offer the holistic capabilities envisioned by the inventors of \gls{dt}s.
	      The results of the literature survey are in \Cref{tab:dt_features_comparison}, which contains systems which we found through a semi-structured literature review process.
	      We first used structured queries, followed by a mix of snowballing and manual search.
	      As a result, the second contribution to answering research question 2 is a holistic system model that encompasses the features of all the systems from \Cref{tab:dt_features_comparison} (see \Cref{fig:system_model}).

	\item \emph{How to design a reference architecture for a predictive datacenter digital twin using discrete-event simulation?}\\
	      To answer this research question, we first brainstormed the potential use-cases for a predictive \gls{dcdt}.
	      The use-cases are based on the findings of our literature survey.
	      We list the use-cases we found in \Cref{s:design}.
	      Based on a set of use-cases we created a set of functional and non-functional requirements to guide our system design.
	      Then, using the \emph{AtLarge Design Process} we created the reference architecture that enables predictive analysis for datacenter operators through digital twinning.

	\item \emph{How to validate and evaluate a datacenter digital twin architecture in relation to system requirements?}\\
	      To answer the last research question we crated a prototype to evaluate our system.
	      Lacking the physical datacenter to experiment with, we came up with a novel digital twin evaluation method, that uses discrete-event simulation to model the physical datacenter.
	      Our main findings indicate that \gls{my_system} can reliably differentiate between large host failures and insignificant downtime using predictions based on the results from \code{OpenDC}, a state of the art datacenter modelling software.
	      Moreover, we show that \gls{my_system} can be used to incorporate predictive analytics systems and significantly lower the total number of task failures during a workload.
\end{enumerate}

\section{Future Work}\label{ss:future_work}

\subsection{A Strong, New Principle of \gls{dcdt} Design}\label{sss:future_work_in_analytics}
We envision \gls{dcdt}s as systems that encompass features necessary to model the entire datacenter.
It came to our attention that with the growth of \gls{ai} and the diversification of datacenters under way, \gls{dt}s will be indispensable in datacenter management.
To power the predictions, we envision an \gls{ml}-based inference engine as a necessary component of digital twinning.
The need for \gls{ml} arises naturally in scenarios where large volumes of data, requiring little to no preprocessing meet the demand for estimating future facility behaviour.

\subsection{}\label{sss:future_work_in_failures}
For future work in failure prediction, we envision an \gls{abc} approach to estimate the real failure distribution within the datacenter.
Additionally, power usage optimization is a critical concern in datacenter management.
We hope future attempts to enhance datacenter digital twinning can enable datacenter operators with actionable insights towards lowering the power consumption.

