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In this work, we propose to enable predictive analytics for datacenters using digital twinning.
We survey the datacenter digital twinning field, and organize our findings into a system model.
-Additionally, we design \mysystem~-- a novel reference architecture for predictive datacenter digital twins, and evaluate it through prototype-based experiments.
+Based on real-world use-cases we design \mysystem~-- a novel reference architecture for predictive datacenter digital twins, and evaluate it through prototype-based experiments.
Our results indicate \mysystem is capable of reliably differentiating between mild and severe compute failures, and can successfully incorporate a predictive analytics engine to the benefit of datacenter managers.
+The main findings are backed-up by experiments which replicate a peer-reviewed publication evaluation approach to show that \mysystem can notify datacenter operators of compute failures.
+Moreover, to show that the system can incorporate a predictive analytics-engine, we have conducted a novel, conceptual experiment to exhibit the potential future gains of a holistic Datacenter Digital Twin.
diff --git a/content/preamble/acknowledgement.tex b/content/preamble/acknowledgement.tex
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\end{center}
I wish to express my thanks to the many people who have helped me in preparation of this thesis.
-Dante Niewehuis, my daily supervisor, with patience and understanding mentored me during our collaboration, which led to the timely and successful completion of this thesis.
+Dante Niewenhuis, my daily supervisor, with patience and understanding mentored me during our collaboration, which led to the timely and successful completion of this thesis.
Alexandru Iosup, my 1\textsuperscript{st} supervisor, provided invaluable feedback and suggestions that helped immensely during the design and writing.