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Diffstat (limited to 'main.tex')
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@@ -23,16 +23,14 @@ \begin{tcolorbox}[title=DCDT's lack predictive analytics] We need Datacenter Digital Twins (DCDT) to be better able to detect and solve issues in critical ICT infrastructure~\cite{DBLP:journals/computer/AthavaleBBMMPS24}. However, DCDT's are still actively developed and lack crucial features such as predictive analytics~\cite{DBLP:usdoe/report/AP26894} to \emph{e.g.,} prevent unexpected failures. - With predictive analysis (\emph{e.g.,} simulation) DCDT's could save millions of lost \$USD~\cite{DBLP:conf/acsos/TalluriOVTI21}. \end{tcolorbox} \begin{center} \includegraphics[width=0.9\linewidth]{images/predictive_analytics.pdf} \end{center} \tiny - \textbf{Figure 1.2:} Where does our work fit within the field of datacenter digital twinning? - There are 5 core elements to any Digital Twin: \myCircled{A} The Digital $\rightarrow$ Physical Twin link, \myCircled{B} the Physical Twin (\emph{e.g.,} the datacenter), \myCircled{C} the Physical $\rightarrow$ Digital Twin link, \myCircled{D} the Digital Twin, \myCircled{E} the features necessary to any Digital Twin. - \textcolor{Green}{\faHighlighter~Highlighted areas are the contributions from this thesis, which include the autonomous actions resulting from predictive insights \myCircledGreen{A} and the predictive analysis itself within \myCircledGreen{E}.} + \textbf{Figure 1.2:} Datacenter Digital Twin Diagram. There are 5 core elements to any Digital Twin: \myCircled{A} The Digital $\rightarrow$ Physical Twin link, \myCircled{B} the Physical Twin (\emph{e.g.,} the datacenter), \myCircled{C} the Physical $\rightarrow$ Digital Twin link, \myCircled{D} the Digital Twin, \myCircled{E} the features necessary to any Digital Twin. + \textcolor{Green}{\faHighlighter~Highlighted areas are the contributions from this thesis, which include the autonomous actions resulting from predictive insights \myCircledGreen{A} and the predictive analysis (including simple storage capabilities) within \myCircledGreen{E}.} \end{frame} \begin{frame}\frametitle{Research Questions} @@ -56,7 +54,7 @@ \begin{frame}\frametitle{\textbf{RQ1}: Literature Review I} \begin{tcolorbox}[title=Main Finding I] - The literature on DCDTs is scarce. + The literature on DCDTs is sparse. Some systems barely classify as DTs (\emph{e.g.,} Kalibre~\cite{DBLP:conf/sensys/WangZD0TCWZ20}, ChatTwin~\cite{DBLP:conf/sensys/LiW0Z0T23}). Existing deployments specialize in \textcolor{Red}{Cooling and Heat Modelling}, together with \textcolor{Red}{3D visualizations}. Most lack predictive modelling of DC operations. @@ -195,13 +193,21 @@ \emph{Sunfish} is capable of dynamic adjustments to the physical twin at runtime, and can lower the mean number of failed tasks. \end{tcolorbox} \hspace{0.2cm} + % Let's say we have some knowledge about the kind of workload we are going to run, e.g., Skype video calls. + % We can then estimate on previous Skype node failures and one of statistical distributions when are failures likely to happen. + % During the experiment, we unfortunately do not know what kind of distribution will the failures follow, so we constantly check to see which one fits best, and dynamically adjust the scheduling policy based on that. + %---% + % Step 1: We know we are going to soon run a workload coming in from Skype. Let's try to predict the failure pattern we might encounter. + % Run the OpenDC simulator 5 times to estimate the possible failure patterns. Save the results inside the Digital Twin. + % Step 2: Run the Digital Twin. Each time a new metric comes in, update the similarity score of each possible distribution. + % If the distribution with the similarity score that is the highest is about to match timestamps with the running workload AND according to the distribution we are going to experience failures in hosts A,B,C,D, % We decide to stop scheduling tasks on hosts A,B,C and D (we send a message to the running datacenter). \begin{minipage}[b]{0.45\linewidth} \begin{center} \includegraphics[width=1.1\textwidth]{images/23_Jun_2026_102028.pdf} \end{center} \vspace{-0.3cm} \tiny - \textbf{Figure 1.8a:} Experiment 2a. + \textbf{Figure 1.8a:} Experiment 2a. We can see in this plot which failure distribution is the most likely to be the true distribution while the simulation is running. \end{minipage} \begin{minipage}[b]{0.45\linewidth} \begin{center} @@ -209,26 +215,26 @@ \end{center} \vspace{-0.3cm} \tiny - \textbf{Figure 1.8b:} Experiment 2b. + \textbf{Figure 1.8b:} Experiment 2b. The gains in number of failures from turning faulty hosts in advance. \end{minipage} \end{frame} \begin{frame}\frametitle{Key Takeaways} - \begin{tcolorbox}[title=What is the societal context?] + \begin{tcolorbox}[title=Societal Context] Datacenter manageability is a top-priority for the digital society. Over 3 million jobs in the Netherlands directly depend on cloud services, which are hosted in datacenters~\cite{DBLP:journals/corr/IosupKLVG22}. \end{tcolorbox} - \begin{tcolorbox}[title=What problem did we solve?] + \begin{tcolorbox}[title=Problem Statement] DCDT's, still under development, lack crucial features such as predictive analytics to manage datacenters well. The entire DCDT design space remains largely unexplored. \end{tcolorbox} - \begin{tcolorbox}[title=How did we solve this problem?] - Our contributions are: a thorough literature survey with a system model, a DCDT reference architecture, and prototype-based experiments via a novel evaluation method. + \begin{tcolorbox}[title=Contributions] + (1) A thorough literature survey with a system model, (2) a DCDT reference architecture, and (3) prototype-based experiments via a novel evaluation method. \end{tcolorbox} - \begin{tcolorbox}[title=What did we find?] + \begin{tcolorbox}[title=Main Findings] \emph{Sunfish} can reliably detect unexpected failures based on discrete-event predictions, and can serve as a foundation for additional research and future work. \end{tcolorbox} % Mandatory to mention here the future work that you see happening. |