mistral-io-datasets/paper/main.tex

\let\accentvec\vec
\documentclass[]{llncs}

\usepackage{todonotes}
\newcommand{\eb}[1]{\todo[inline]{(EB): #1}}
\newcommand{\jk}[1]{\todo[inline]{JK: #1}}

\usepackage{silence}
\WarningFilter{biblatex}{Using}
\WarningFilter{latex}{Float too large}
\WarningFilter{caption}{Unsupported}
\WarningFilter{caption}{Unknown document}

\let\spvec\vec
\let\vec\accentvec
\usepackage{amsmath}
\let\vec\spvec

\usepackage{array}
\usepackage{xcolor}
\usepackage{color}
\usepackage{colortbl}
\usepackage{subcaption}
\usepackage{hyperref}
\usepackage{listings}
\usepackage{lstautogobble}
\usepackage[listings,skins,breakable,raster,most]{tcolorbox}
\usepackage{caption}


\lstset{
	numberbychapter=false,
	belowskip=-10pt,
	aboveskip=-10pt,
}

\lstdefinestyle{lstcodebox} {
	basicstyle=\scriptsize\ttfamily,
	autogobble=true,
	tabsize=2,
	captionpos=b,
	float,
}

\usepackage{graphicx}
\graphicspath{
	{./pictures/},
  {../fig/}
}

\usepackage[backend=bibtex, style=numeric]{biblatex}
\addbibresource{bibliography.bib}


\usepackage{enumitem}
\setitemize{noitemsep,topsep=0pt,parsep=0pt,partopsep=0pt}

\definecolor{darkgreen}{rgb}{0,0.5,0}
\definecolor{darkyellow}{rgb}{0.7,0.7,0}


\usepackage{cleveref}
\crefname{codecount}{Code}{Codes}

\title{Using Machine Learning to Identify Similar Jobs Based on their IO Behavior}
\author{Julian Kunkel\inst{2} \and Eugen Betke\inst{1}}

\institute{
University of Reading--%
\email{j.m.kunkel@reading.ac.uk}%
\and
DKRZ --
\email{betke@dkrz.de}%
}
\begin{document}
\maketitle

\begin{abstract}

Support staff.
Problem, a particular job found that isn't performing well.
Now how can we find similar jobs?

Problem with definition of similarity.

In this paper, a methodology and algorithms to identify similar jobs based on profiles and time series are  illustrated.
Similar to a study.

Research questions: is this effective to find similar jobs?

The contribution of this paper...
\end{abstract}

\section{Introduction}

%This paper is structured as follows.
%We start with the related work in \Cref{sec:relwork}.
%Then, in TODO we introduce the DKRZ monitoring systems and explain how I/O metrics are captured by the collectors.
%In \Cref{sec:methodology} we describe the data reduction and the machine learning approaches and do an experiment in \Cref{sec:data,sec:evaluation}.
%Finally, we finalize our paper with a summary in \Cref{sec:summary}.

\section{Related Work}
\label{sec:relwork}

\section{Methodology}
\label{sec:methodology}

Given: the reference job ID.
Create from 4D time series data (number of nodes, per file systems, 9 metrics, time) a feature set.

Adapt the algorithms:
\begin{itemize}
	\item iterate for all jobs
		\begin{itemize}
			\item compute distance to reference job
		\end{itemize}
	\item sort the jobs based on the distance to ref job
	\item create cumulative job distribution based on distance for visualization, allow users to output jobs with a given distance
\end{itemize}

A user might be interested to explore say closest 10 or 50 jobs.

Algorithms:
Profile algorithm: job-profiles (job-duration, job-metrics, combine both)
$\rightarrow$ just compute geom-mean distance between profile

Check time series algorithms:

\begin{itemize}
	\item bin
	\item hex\_native
  \item hex\_lev
	\item hex\_quant
\end{itemize}

\section{Evaluation}
\label{sec:evaluation}

In the following, we assume a job is given and we aim to identify similar jobs.
We chose several reference jobs with different compute and IO characteristics visualized in \Cref{fig:refJobs}:
\begin{itemize}
	\item Job-S: performs postprocessing on a single node. This is a typical process in climate science where data products are reformatted and annotated with metadata to a standard representation (so called CMORization). The post-processing is IO intensive.
  \item Job-M: a typical MPI parallel 8-hour compute job on 128 nodes which writes time series data after some spin up.   %CHE.ws12
	\item Job-L: a 66-hour 20-node job.
  The initialization data is read at the beginning.
  Then only a single master node writes constantly a small volume of data; in fact, the generated data is too small to be categorized as IO relevant.
\end{itemize}

For each reference job and algorithm, we created a CSV files with the computed similarity for all other jobs.


Sollte man was zur Laufzeit der Algorithmen sagen? Denke Daten zu haben wäre sinnvoll.

Create histograms + cumulative job distribution for all algorithms.
Insert job profiles for closest 10 jobs.

Potentially, analyze how the rankings of different similarities look like.


\begin{figure}
\begin{subfigure}{0.8\textwidth}
\includegraphics[width=\textwidth]{job-timeseries4296426}
\caption{Job-S} \label{fig:job-S}
\end{subfigure}

\caption{Reference jobs: timeline of mean IO activity}
\label{fig:refJobs}
\end{figure}


\begin{figure}\ContinuedFloat

\begin{subfigure}{0.8\textwidth}
\includegraphics[width=\textwidth]{job-timeseries5024292}
\caption{Job-M} \label{fig:job-M}
\end{subfigure}

\begin{subfigure}{0.8\textwidth}
\includegraphics[width=\textwidth]{job-timeseries7488914-30.pdf}
\caption{Job-L (first 30 segments of 400; remaining segments are similar)}
\label{fig:job-L}
\end{subfigure}
\caption{Reference jobs: timeline of mean IO activity; non-shown timelines are 0}
\end{figure}


\section{Summary and Conclusion}
\label{sec:summary}

%\printbibliography
\end{document}