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diff --git a/content/intro.tex b/content/intro.tex index 57d3003..79d604f 100644 --- a/content/intro.tex +++ b/content/intro.tex @@ -51,7 +51,13 @@ Due to insufficient technological foundations, little work is available on \gls{ Today, research is focused on bridging the gap between the long-established foundations of \gls{dt}s and new, novel applications in academia and industry, such as the \gls{dcdt}~\cite{DBLP:conf/cirp/TAO2018169, DBLP:journals/computer/AthavaleBBMMPS24}. A \gls{dcdt} mirrors the structure, context and behaviour of a datacenter~\cite{DBLP:journals/computer/AthavaleBBMMPS24}. +The foundation to any digital twin is good monitoring and sensing capabilities in the physical entity. +Datacenters, meet this requirement easily because they already connect hundreds of monitoring sensors. +With hundreds of gigabytes of useful information coming from distributed \gls{iot} sensors inside the warehouse, we can gain insight into failure patterns, energy usage, heat dissipation \etc +What remains challenging is to connect the physical and virtual spaces with a bi-directional connection +to use the monitoring insights and data analysis results for autonomous decision-making. Crucial to \gls{dcdt} operation are predictive capabilities and the continuous interaction with the real-world datacenter. + There already exist \gls{dcdt} deployments. For example, ExaDigiT~\cite{DBLP:conf/sc/BrewerMKWBHSGGW24} is a framework for digital twin development of supercomputers. It has been demonstrated at the Frontier supercomputer and it facilitates virtual prototyping and system optimization. @@ -72,7 +78,7 @@ However, predicting datacenter behaviour quickly and reliably is a non-trivial p We envision \gls{dcdt}'s as systems indispensable in future datacenters, actively interacting with the real-world facility, lowering operational costs and predicting hardware failure and software faults. -In this work, we address the lack of a unified \gls{dcdt} definition and the absence of predictive capabilities in existing \gls{dcdt} system designs. +In this work, we address the lack of a unified \gls{dcdt} system model and the absence of predictive capabilities in existing \gls{dcdt} system designs. We argue that the current state-of-the-art \gls{dcdt}'s lack sufficient predictive capabilities that are essential to real-time facility management of a modern datacenter. A \gls{dt} without predictive capabilities cannot maintain the health of the datacenter effectively. We posit that including holistic predictive analysis in \gls{dcdt} design can aid in efficient datacenter management and prevent missing \gls{sla}'s. @@ -173,7 +179,7 @@ This work addresses the four grand societal challenges related to this goal: \be \item usability \end{enumerate*}~\cite{DBLP:journals/corr/IosupKLVG22}. \gls{my_system} addresses (1) directly by making large-scale datacenter management easier. -We address (2) by ensuring our work adheres to the FAIR principles of Open Science. +We address (2) by ensuring our work adheres to the \gls{fair} principles of Open Science. Moreover, in this thesis we try to make \gls{dcdt} systems more understandable to the broader scientific community by providing a unified system model. Additionally, we contribute to responsible software design by adhering to best software engineering practices in the design of the prototype. (3) is addressed indirectly, as the consequences of the insights provided by a holistic, \gls{oda} powered \gls{dcdt} can help datacenter managers make decisions that are more sustainable in the future. |