From eaa7bfdab525c2ea367d6e9c08382f9a00049109 Mon Sep 17 00:00:00 2001 From: mjkwiatkowski Date: Mon, 6 Jul 2026 12:29:51 +0200 Subject: feat: finished the conclusion --- content/conclusion.tex | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) (limited to 'content/conclusion.tex') diff --git a/content/conclusion.tex b/content/conclusion.tex index d964ae9..4db890d 100644 --- a/content/conclusion.tex +++ b/content/conclusion.tex @@ -41,5 +41,10 @@ As such, we believe we answer the main research question by addressing each sub- \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. -We predict that in the near future, a number of +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. + -- cgit v1.2.3