\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.
In this thesis we pave 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.
As such, we believe we answer the main research question by addressing each sub-research question.

\section{Answers to Research Questions}

\begin{enumerate}[label=\emph{RQ\textsubscript{\arabic*}}]
	\item \emph{How to asses the current state-of-the-art of digital twinning for datacenters?}\\
	      In order 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.
	      There exist few existing \gls{dcdt} deployments.
	      The current efforts in modelling datacenters focus on very specialized parts of the datacenter management, \ie cooling and heat modelling, network mapping.
	      These standalone systems fail to offer the holistic capabilities envisioned for \gls{dt}s.
	      The results of the literature survey are in \Cref{tab:dt_features_comparison}.
	      \Cref{tab:dt_features_comparison} contains systems which we found though 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 single host 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 a predictive analytics system and significantly lower the total number of task failures during a workload.
\end{enumerate}

\section{Future Work}

We envision \gls{dcdt}s as systems that encompass features necessary to model the entire datacenter behaviour.
It came to our attention that with the explosive 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.
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.

