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mistral-io-datasets
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Spacve ^-^

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Julian M. Kunkel 2021-06-02 15:22:56 +02:00
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lncs-from-jhps/assets/job_similarities_5024292-out/user-ids.png
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lncs-from-jhps/main.tex
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@ -229,10 +229,22 @@ Note that the current algorithms are sequential and executed on just one core.
They could easily be parallelized which would then allow for an online analysis.
\begin{figure}
\begin{subfigure}{0.47\textwidth}
\centering
\includegraphics[width=0.5\textwidth]{progress_5024292-out-boxplot}
\includegraphics[width=\textwidth]{progress_5024292-out-boxplot}
\caption{Runtime of the algorithms to compute the similarity to our reference job}%
\label{fig:performance}
\end{subfigure}
\qquad
\begin{subfigure}{0.47\textwidth}
\centering
\includegraphics[width=0.85\textwidth]{job_similarities_5024292-out/user-ids}
\caption{User information for all 100 top-ranked jobs. Each color represents a specific user for the given data.}
\label{fig:userids}
\end{subfigure}
\caption{Algorithm runtime and user distribution}
\end{figure}
@ -287,13 +299,6 @@ The job runtime of the Top\,100 jobs is shown using boxplots in \Cref{fig:runtim
While all algorithms can compute the similarity between jobs of different length, the B algorithms and Q-native penalize jobs of different length preferring jobs of very similar length.
Q-phases is able to identify much shorter or longer jobs.
\begin{figure}[bt]
\centering
\includegraphics[width=0.6\textwidth]{job_similarities_5024292-out/user-ids}
\caption{User information for all 100 top-ranked jobs. Each color represents a specific user for the given data.}
\label{fig:userids}
\end{figure}
\begin{figure}
\centering
@ -302,6 +307,7 @@ Q-phases is able to identify much shorter or longer jobs.
\caption{Node counts ($job=128 nodes$)}%
\label{fig:nodes-job}
\end{subfigure}
\quad
\begin{subfigure}{0.47\textwidth}
\includegraphics[width=\textwidth]{job_similarities_5024292-out/jobs-elapsed}
\caption{Runtime ($job=28,828s$)}%
@ -317,15 +323,12 @@ Q-phases is able to identify much shorter or longer jobs.
\label{sec:timelines}
To verify the suitability of the similarity metrics, for each algorithm, we carefully investigated the timelines of each of the jobs in the Top\,100.
We subjectively found that the approach works very well and identifies suitable similar jobs.
To demonstrate this, we include a selection of job timelines and selected interesting job profiles.
These can be visually and subjectively compared to our reference jobs shown in \Cref{fig:refJobs}.
To demonstrate this, we include a selection of job timelines and selected interesting job profiles.
These can be visually and subjectively compared to our reference job shown in \Cref{fig:refJobs}.
For space reasons, the included images will be scaled down making it difficult to read the text.
However, we believe that they are still well suited for a visual inspection and comparison.
\subsection{Job-M}
Inspecting the Top\,100 for this reference job is highlighting the differences between the algorithms.
Inspecting the Top\,100 is highlighting the differences between the algorithms.
All algorithms identify a diverse range of job names for this reference job in the Top\,100.
Firstly, the same name of the reference job appears 30 times in the whole dataset.
Additional 932 jobs have a slightly modified name.