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diff --git a/content/conclusion.tex b/content/conclusion.tex index 122b2be..83a499c 100644 --- a/content/conclusion.tex +++ b/content/conclusion.tex @@ -9,11 +9,11 @@ Starting from a thorough investigation into the new, emerging field of datacente 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 Research Questions}\label{ss:answers_to_rqs} +\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?}\\ - In order to answer this research question, we conducted a semi-structured literature review. + 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. @@ -38,10 +38,13 @@ We answer the main research question by addressing each sub-research question. \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 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. +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. |