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diff --git a/content/implementation.tex b/content/implementation.tex index ae100bd..2395963 100644 --- a/content/implementation.tex +++ b/content/implementation.tex @@ -6,7 +6,8 @@ After reading one should understand the technical decisions, choice of tools and Any complex system is more than the sum of its parts~\cite{Wikipedia:article/Systems_Thinking}. To understand why \gls{my_system} it is crucial to provide a holistic view on the prototype. Therefore, the rest of the chapter is structured in a top-down approach: \Cref{ss:implementation_overview} -presents the rationale for using the specific software packages, \Cref{ss:data_flow} shows the flow of data within the system, and \Cref{ss:programming} details the different modifications and new software extensions. +presents the rationale for using the specific software packages, \Cref{ss:data_flow} shows the flow of data within the system, and \Cref{ss:extensions} details the different modifications and new software extensions to \code{OpenDC}. +Lastly, \Cref{ss:programming} carefully explains the design decisions behind the major Python modules. \section{Overview}\label{ss:implementation_overview} @@ -21,14 +22,14 @@ presents the rationale for using the specific software packages, \Cref{ss:data_f At the onset of the project, we decided \gls{my_system} will use only state-of-the-art software, deployed in the industry or evaluated in peer-reviewed scientific publications. The mapping of software packages used onto the reference architecture can be seen in \Cref{fig:implementation}. In order to facilitate visualizations and interactive dashboards, we decided to use \code{Grafana} (\myCircled{2a})~\cite{Wikipedia:article/Grafana}. -To enable the flow of data into the digital twin, we use \code{Kafka} (\myCircled{2b})~\cite{Wikipedia:article/Confluent}. +To enable the flow of data into the \gls{dt}, we use \code{Kafka} (\myCircled{2b})~\cite{Wikipedia:article/Confluent}. To store the in-band data we use a \code{Redis} (\myCircled{3b})~\cite{Wikipedia:article/Redis} cache, and for out-of-band data we use a \code{PostgreSQL}(\myCircled{3a})~\cite{Wikipedia:article/Postgresql}. To enable predictive analytics, we chose a discrete-event simulator, \code{OpenDC}(\myCircled{4a})~\cite{GitHub:software/OpenDC}. The \code{Analytics Engine} (\myCircled{4b}), \code{Monitoring Service} (\myCircled{4c}), and \code{HTTP Server} (\myCircled{3c}) are described in detail in \Cref{ss:programming}. \code{Grafana} (\myCircled{2a})is a state-of-the-art industry tool to visualize dashboards. We posit it is crucial to include a user-friendly \gls{ui}. -A number of previous publications on digital twinning find dashboards important~\cite{DBLP:conf/sc/TaheriBPRHDEWPM24, DBLP:conf/wosp/SumanCNTMI24, DBLP:conf/wosp/NicolaeTKLI26}. +A number of previous publications on \gls{dt}s find dashboards important~\cite{DBLP:conf/sc/TaheriBPRHDEWPM24, DBLP:conf/wosp/SumanCNTMI24, DBLP:conf/wosp/NicolaeTKLI26}. We chose \code{Grafana} (\myCircled{2a}) instead of other software packages because of its seamless integration with \code{PostgreSQL} (\myCircled{3a}). \code{Grafana} (\myCircled{2a}) provides good separation of concerns and compartmentalization as it does not store the displayed metrics itself. Instead, it queries the \code{PostgreSQL} (\myCircled{3a}) database in real-time~\cite{Wikipedia:article/Grafana}, unlike \eg \code{InfluxDB}. @@ -67,15 +68,15 @@ For a specific overview of advantages of \code{OpenDC} (\myCircled{4a}) and a t \section{Data Flow}\label{ss:data_flow} In this section we describe the data flow within \Cref{fig:implementation} using a separate diagram. -Efficient data flow is of utmost importance to digital twinning. +Efficient data flow is of utmost importance to \gls{dt}s. In \Cref{fig:flow_diagram} we present the moving of data within \gls{my_system}. In the diagram whenever we refer to \emph{control}, we mean small, one-in-a-while data packets that contain either instructions, insights or small amount of data. Whenever we refer to \emph{data}, we mean the hundreds of thousands of metrics exported with minimal latency from the operating datacenter (\code{OpenDC}). We describe the flow of data through a timeline. -First, the datacenter informs the digital twin of an upcoming workload (\grayCircled{2}). +First, the datacenter informs the \gls{dt} of an upcoming workload (\grayCircled{2}). This packet contains the datacenter topology and the upcoming workload tasks (workload trace, collected from \eg BitBrains). -The digital twin stores this data locally, and passes it forward to the \code{Analytics Engine} (\grayCircled{3}). +The \gls{dt} stores this data locally, and passes it forward to the \code{Analytics Engine} (\grayCircled{3}). The analytics engine queries the \code{OpenDC} simulator to run a simulation of what might happen in the datacenter under such workload (\grayCircled{7}). \code{OpenDC} returns the potential results to the \code{Analytics Engine} directly (\myCircled{6}). @@ -108,4 +109,35 @@ This setup achieves excellent abstraction level, because only the most important \begin{figure}[t] \input{sources/listing_sinks.tex} \end{figure} -\section{Programming Effort}\label{ss:programming} +\section{Extensions to \code{OpenDC}}\label{ss:extensions} + +\code{OpenDC} is a state-of-the-art datacenter simulator. +In order to turn it into a \gls{dt}, we have made several design decisions and extensions. + +\begin{enumerate}[label=\textbf{\arabic*.}] + \item \textbf{\code{SmartScheduler}}\\ + The new \code{SmartScheduler} is a scheduling mechanism capable of incorporating the insights from the \gls{dt} into its scheduling decisions. + It relies on the functionality of the \code{HTTPClient} to poll the \gls{dt} at each scheduling step for potential insights. + For example, if \gls{dt} sends to the datacenter a list of hosts likely to fail in the future, the \code{SmartScheduler} acts as \emph{system knobs} to enforce the \gls{dt} insights (\ie it can be mapped to \myCircled{2c} from \Cref{fig:implementation}). + \item \textbf{\code{KafkaMonitor}}\\ + The datacenter acts as the \emph{producer} of metrics, ingested by the \code{Kafka} topic (see \Cref{fig:flow_diagram}). + We equip \code{OpenDC} with a new \code{ComputeMonitor} capable of exporting data directly into a \code{Kafka} topic. + The \code{KafkaMonitor} class implements the generic \code{ComputeMonitor} interface, therefore the new extension follows the other metric exporting options in \code{OpenDC} (\ie follows the implementation for exporting metrics into \code{.parquet files}). + \item \textbf{\code{HTTPClient}}\\ + The \code{HTTPClient} offers the necessary functionality to communicate between the \gls{dt} and the datacenter. + We decided to use the HTTP protocol for short, one-off communications between the \gls{dt} and the datacenter, as is common industry practice. +\end{enumerate} + +\section{Python Modules}\label{ss:programming} + +\code{Analytics Engine}, \code{HTTPServer}, \code{MonitoringService} are Python modules we prototyped for the reference architecture evaluation. +It is important to note that we do not evaluate the performance of \gls{my_system} due to the substantial overheads of the Python interpreter. +\code{Kafka}, \code{Redis} and \code{PostgreSQL} enable milisecond-latency and huge throughput within the system, however what stops us from a performance evaluation is the high cost of Python interpretation. +For future work, we envision a system that implements the reference architecture in a compiled language, \eg \code{C} or \code{C++}. + +\begin{enumerate}[label=\textbf{\arabic*.}] + \item \textbf{\code{AnalyticsEngine}}\\ + \item \textbf{\code{HTTPServer}}\\ + \item \textbf{\code{MonitoringService}}\\ +\end{enumerate} + |