From eaa7bfdab525c2ea367d6e9c08382f9a00049109 Mon Sep 17 00:00:00 2001
From: mjkwiatkowski <mati.rewa@gmail.com>
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.
+
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