Merge branch 'master' of tea.maggioni.xyz:maggicl/bachelorThesis

This commit is contained in:
Claudio Maggioni 2021-05-12 12:34:23 +00:00
commit 818b0c57a1
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task_slowdown/?_state_changes.json.gz task_slowdown/?_state_changes.json.gz
**/*.gz **/*.gz
**/.DS_Store
figure_9/*.parquet/ figure_9/*.parquet/
figure_9/?_task_count/ figure_9/?_task_count/
figure_9/?_machine_locality/ figure_9/?_machine_locality/

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"cells": [ "cells": [
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"# Machine configurations\n", "# Machine configurations\n",
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---
documentclass: usiinfbachelorproject
title: Understanding and Comparing Unsuccessful Executions in Large Datacenters
author: Claudio Maggioni
pandoc-options:
- --filter=pandoc-include
- --latex-engine-opt=--shell-escape
- --latex-engine-opt=--enable-write18
header-includes:
- |
```{=latex}
\usepackage{subcaption}
\usepackage{booktabs}
\usepackage{graphicx}
\captionsetup{labelfont={bf}}
%\subtitle{The (optional) subtitle}
\versiondate{\today}
\begin{committee}
\advisor[Universit\`a della Svizzera Italiana,
Switzerland]{Prof.}{Walter}{Binder}
\assistant[Universit\`a della Svizzera Italiana,
Switzerland]{Dr.}{Andrea}{Ros\'a}
\end{committee}
\abstract{The project aims at comparing two different traces coming from large
datacenters, focusing in particular on unsuccessful executions of jobs and
tasks submitted by users. The objective of this project is to compare the
resource waste caused by unsuccessful executions, their impact on application
performance, and their root causes. We will show the strong negative impact on
CPU and RAM usage and on task slowdown. We will analyze patterns of
unsuccessful jobs and tasks, particularly focusing on their interdependency.
Moreover, we will uncover their root causes by inspecting key workload and
system attributes such asmachine locality and concurrency level.}
```
---
# Introduction (including Motivation)
# State of the Art
- Introduce Ros\'a 2015 DSN paper on analysis
- Describe Google Borg clusters
- Describe Traces contents
- Differences between 2011 and 2019 traces
# Project requirements and analysis
(describe our objective with this analysis in detail)
# Analysis methodology
## Technical overview of traces' file format and schema
## Overview on challenging aspects of analysis (data size, schema, avaliable computation resources)
## Introduction on apache spark
## General workflow description of apache spark workflow
The Google 2019 Borg cluster traces analysis were conducted by using Apache
Spark and its Python 3 API (pyspark). Spark was used to execute a series of
queries to perform various sums and aggregations over the entire dataset
provided by Google.
In general, each query follows a general Map-Reduce template, where traces are
first read, parsed, filtered by performing selections, projections and computing
new derived fields. Then, the trace records are often grouped by one of their
fields, clustering related data toghether before a reduce or fold operation is
applied to each grouping.
Most input data is in JSONL format and adheres to a schema Google profided in
the form of a protobuffer specification[^1].
[^1]: [Google 2019 Borg traces Protobuffer specification on Github](
https://github.com/google/cluster-data/blob/master/clusterdata_trace_format_v3.proto)
On of the main quirks in the traces is that fields that have a "zero" value
(i.e. a value like 0 or the empty string) are often omitted in the JSON object
records. When reading the traces in Apache Spark is therefore necessary to check
for this possibility and populate those zero fields when omitted.
Most queries use only two or three fields in each trace records, while the
original records often are made of a couple of dozen fields. In order to save
memory during the query, a projection is often applied to the data by the means
of a .map() operation over the entire trace set, performed using Spark's RDD
API.
Another operation that is often necessary to perform prior to the Map-Reduce core of
each query is a record filtering process, which is often motivated by the
presence of incomplete data (i.e. records which contain fields whose values is
unknown). This filtering is performed using the .filter() operation of Spark's
RDD API.
The core of each query is often a groupBy followed by a map() operation on the
aggregated data. The groupby groups the set of all records into several subsets
of records each having something in common. Then, each of this small clusters is
reduced with a .map() operation to a single record. The motivation behind this
computation is often to analyze a time series of several different traces of
programs. This is implemented by groupBy()-ing records by program id, and then
map()-ing each program trace set by sorting by time the traces and computing the
desired property in the form of a record.
Sometimes intermediate results are saved in Spark's parquet format in order to
compute and save intermediate results beforehand.
## General Query script design
## Ad-Hoc presentation of some analysis scripts (w diagrams)
# Analysis (w observations)
## machine_configs
\input{figures/machine_configs}
**Observations**:
- machine configurations are definitely more varied than the ones in the 2011
traces
- some clusters have more machine variability
## machine_time_waste
\input{figures/machine_time_waste}
**Observations**:
## task_slowdown
## spatial_resource_waste
## figure_7
## figure_8
## figure_9
## table_iii, table_iv, figure_v
## Potential causes of unsuccesful executions
# Implementation issues -- Analysis limitations
## Discussion on unknown fields
## Limitation on computation resources required for the analysis
## Other limitations ...
# Conclusions and future work or possible developments
Some examples
-------------
**Figure  [1](#fig:USILogo){reference-type="ref"
reference="fig:USILogo"}** shows how to insert figures in the document.
![Caption of the figure](logo-info.pdf){#fig:USILogo width="50%"}
**Table  [1](#tab:numbers){reference-type="ref"
reference="tab:numbers"}** shows how to insert tables in the document.
::: {#tab:numbers}
**Col 1** **Col 2** **Col 3** **Col 4**
----------- ----------- ----------- -----------
1 2 3 Goofy
4 5 6 Mickey
: Caption of the table
:::
<!-- vim: set ts=2 sw=2 et tw=80: -->

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\newcommand{\machineconfigs}[3][0.9\textwidth]{
\begin{subfigure}{0.32\textwidth}
\begin{minipage}[c][#1]{\textwidth}%
\resizebox{\textwidth}{!}{
\centering
\begin{tabular}{llll}
\toprule
\textbf{CPU (NCU)} & \textbf{RAM (NMU)} & \textbf{Machine count} &
\textbf{\% Machines} \\
\midrule
#3
\bottomrule
\end{tabular}}
\end{minipage}
\caption{#2}
\end{subfigure}}
\begin{figure}
\centering
\machineconfigs[1.2\textwidth]{All clusters}{
Unknown & Unknown & 8729 & 1.639218\% \\
1.000000 & 0.500000 & 124234 & 23.329891\% \\
0.591797 & 0.333496 & 103013 & 19.344801\% \\
0.259277 & 0.166748 & 78078 & 14.662260\% \\
0.708984 & 0.333496 & 55801 & 10.478864\% \\
0.386719 & 0.333496 & 36237 & 6.804943\% \\
0.958984 & 0.500000 & 31151 & 5.849843\% \\
0.708984 & 0.666992 & 29594 & 5.557454\% \\
0.386719 & 0.166748 & 27011 & 5.072393\% \\
1.000000 & 1.000000 & 12286 & 2.307187\% \\
0.591797 & 0.166748 & 9902 & 1.859496\% \\
1.000000 & 0.250000 & 7550 & 1.417814\% \\
0.958984 & 1.000000 & 3552 & 0.667030\% \\
0.259277 & 0.333496 & 3024 & 0.567877\% \\
0.591797 & 0.666992 & 1000 & 0.187790\% \\
0.259277 & 0.083374 & 634 & 0.119059\% \\
0.958984 & 0.250000 & 600 & 0.112674\% \\
0.500000 & 0.062500 & 54 & 0.010141\% \\
0.500000 & 0.250000 & 34 & 0.006385\% \\
0.479492 & 0.250000 & 12 & 0.002253\% \\
0.708984 & 0.250000 & 6 & 0.001127\% \\
0.591797 & 0.250000 & 4 & 0.000751\% \\
0.708984 & 0.500000 & 2 & 0.000376\% \\
0.479492 & 0.500000 & 2 & 0.000376\% \\
}
\machineconfigs[1.2\textwidth]{A cluster}{
Unknown & Unknown & 1377 & 1.623170\% \\
0.591797 & 0.333496 & 29487 & 34.758469\% \\
1.000000 & 0.500000 & 13440 & 15.842705\% \\
0.708984 & 0.333496 & 12495 & 14.728764\% \\
0.386719 & 0.333496 & 9057 & 10.676144\% \\
0.386719 & 0.166748 & 5265 & 6.206238\% \\
0.708984 & 0.666992 & 4608 & 5.431784\% \\
1.000000 & 1.000000 & 4446 & 5.240823\% \\
0.591797 & 0.166748 & 2484 & 2.928071\% \\
0.958984 & 0.500000 & 1143 & 1.347337\% \\
0.958984 & 1.000000 & 654 & 0.770917\% \\
1.000000 & 0.250000 & 366 & 0.431431\% \\
0.479492 & 0.250000 & 6 & 0.007073\% \\
0.708984 & 0.250000 & 6 & 0.007073\% \\
}
\machineconfigs[1.2\textwidth]{Cluster B}{
Unknown & Unknown & 134 & 0.264812\% \\
0.591797 & 0.333496 & 16184 & 31.982926\% \\
1.000000 & 0.500000 & 9790 & 19.347061\% \\
0.708984 & 0.333496 & 8448 & 16.694992\% \\
0.958984 & 0.500000 & 5502 & 10.873088\% \\
0.708984 & 0.666992 & 3832 & 7.572823\% \\
1.000000 & 1.000000 & 2214 & 4.375321\% \\
0.591797 & 0.166748 & 2152 & 4.252796\% \\
0.386719 & 0.333496 & 816 & 1.612584\% \\
0.958984 & 1.000000 & 618 & 1.221296\% \\
0.591797 & 0.666992 & 500 & 0.988103\% \\
0.386719 & 0.166748 & 412 & 0.814197\% \\
}
\machineconfigs{Cluster C}{
Unknown & Unknown & 1466 & 2.274208\% \\
0.259277 & 0.166748 & 15754 & 24.439204\% \\
0.386719 & 0.333496 & 11104 & 17.225652\% \\
0.591797 & 0.333496 & 10404 & 16.139741\% \\
0.958984 & 0.500000 & 6634 & 10.291334\% \\
1.000000 & 0.500000 & 5654 & 8.771059\% \\
0.386719 & 0.166748 & 3580 & 5.553660\% \\
0.708984 & 0.666992 & 2900 & 4.498774\% \\
1.000000 & 1.000000 & 2736 & 4.244361\% \\
1.000000 & 0.250000 & 2132 & 3.307375\% \\
0.958984 & 1.000000 & 766 & 1.188297\% \\
0.708984 & 0.333496 & 620 & 0.961807\% \\
0.958984 & 0.250000 & 600 & 0.930781\% \\
0.591797 & 0.166748 & 112 & 0.173746\% \\
}
\machineconfigs{Cluster D}{
Unknown & Unknown & 498 & 0.794309\% \\
0.591797 & 0.333496 & 28394 & 45.288376\% \\
0.386719 & 0.333496 & 8402 & 13.401174\% \\
0.259277 & 0.166748 & 8020 & 12.791885\% \\
0.386719 & 0.166748 & 5806 & 9.260559\% \\
0.708984 & 0.666992 & 4380 & 6.986092\% \\
0.708984 & 0.333496 & 3924 & 6.258772\% \\
0.591797 & 0.166748 & 2548 & 4.064055\% \\
0.259277 & 0.333496 & 426 & 0.679469\% \\
1.000000 & 0.500000 & 292 & 0.465739\% \\
0.591797 & 0.250000 & 4 & 0.006380\% \\
0.708984 & 0.500000 & 2 & 0.003190\% \\
}
\machineconfigs{Cluster E}{
Unknown & Unknown & 536 & 0.671915\% \\
0.259277 & 0.166748 & 38452 & 48.202377\% \\
0.708984 & 0.333496 & 11786 & 14.774608\% \\
0.958984 & 0.500000 & 8646 & 10.838389\% \\
0.708984 & 0.666992 & 7606 & 9.534674\% \\
1.000000 & 0.500000 & 5586 & 7.002457\% \\
0.386719 & 0.166748 & 4470 & 5.603470\% \\
0.259277 & 0.333496 & 1268 & 1.589530\% \\
0.259277 & 0.083374 & 634 & 0.794765\% \\
0.591797 & 0.333496 & 324 & 0.406158\% \\
1.000000 & 0.250000 & 268 & 0.335957\% \\
1.000000 & 1.000000 & 138 & 0.172993\% \\
0.500000 & 0.062500 & 54 & 0.067693\% \\
0.500000 & 0.250000 & 4 & 0.005014\% \\
}
\machineconfigs{Cluster F}{
Unknown & Unknown & 1432 & 2.299958\% \\
1.000000 & 0.500000 & 41340 & 66.396839\% \\
0.708984 & 0.333496 & 6878 & 11.046866\% \\
0.591797 & 0.333496 & 5564 & 8.936430\% \\
0.958984 & 0.500000 & 2172 & 3.488484\% \\
0.386719 & 0.166748 & 1544 & 2.479843\% \\
0.708984 & 0.666992 & 1244 & 1.998008\% \\
1.000000 & 0.250000 & 792 & 1.272044\% \\
0.958984 & 1.000000 & 536 & 0.860878\% \\
0.386719 & 0.333496 & 398 & 0.639234\% \\
1.000000 & 1.000000 & 344 & 0.552504\% \\
0.500000 & 0.250000 & 18 & 0.028910\% \\
}
\machineconfigs{Cluster G}{
Unknown & Unknown & 1566 & 2.261568\% \\
0.259277 & 0.166748 & 15852 & 22.892958\% \\
1.000000 & 0.500000 & 11808 & 17.052741\% \\
0.708984 & 0.333496 & 7968 & 11.507134\% \\
0.591797 & 0.333496 & 7830 & 11.307839\% \\
0.386719 & 0.166748 & 4690 & 6.773150\% \\
0.708984 & 0.666992 & 4258 & 6.149269\% \\
0.958984 & 0.500000 & 4196 & 6.059731\% \\
0.386719 & 0.333496 & 3864 & 5.580267\% \\
0.591797 & 0.166748 & 2606 & 3.763503\% \\
1.000000 & 0.250000 & 2100 & 3.032754\% \\
0.259277 & 0.333496 & 1330 & 1.920744\% \\
0.958984 & 1.000000 & 778 & 1.123563\% \\
1.000000 & 1.000000 & 378 & 0.545896\% \\
0.500000 & 0.250000 & 12 & 0.017330\% \\
0.479492 & 0.250000 & 6 & 0.008665\% \\
0.479492 & 0.500000 & 2 & 0.002888\% \\
}
\machineconfigs{Cluster H}{
Unknown & Unknown & 1720 & 2.933251\% \\
1.000000 & 0.500000 & 36324 & 61.946178\% \\
0.591797 & 0.333496 & 4826 & 8.230158\% \\
0.708984 & 0.333496 & 3682 & 6.279205\% \\
0.958984 & 0.500000 & 2858 & 4.873973\% \\
0.386719 & 0.333496 & 2596 & 4.427163\% \\
1.000000 & 1.000000 & 2030 & 3.461919\% \\
1.000000 & 0.250000 & 1892 & 3.226577\% \\
0.386719 & 0.166748 & 1244 & 2.121491\% \\
0.708984 & 0.666992 & 766 & 1.306320\% \\
0.591797 & 0.666992 & 500 & 0.852689\% \\
0.958984 & 1.000000 & 200 & 0.341076\% \\
}
\caption{Overwiew of machine configurations in terms of CPU and RAM resources for each cluster}
\end{figure}

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\newcommand{\machinetimewaste}[3][0]{
\begin{subfigure}{\ifnum#1=1 0.5\textwidth \else 0.24\textwidth \fi}
\vspace{0.5cm}
%\ifnum#1=1 \hspace{0.25\textwidth} \fi
\begin{minipage}[c]{\textwidth}%
\includegraphics[width=1\textwidth]{figures/machine_time_waste/#3}
\end{minipage}
%\hfill
\caption{#2}
\end{subfigure}}
\newcommand{\machinetimewastelegend}{
\begin{subfigure}{0.5\textwidth}
\vspace{0.5cm}
\centering
\begin{tabular}{cc}
\toprule
\textbf{Color} & \textbf{Execution phase} \\
\midrule
{\color{blue}Blue} & Queued \\
{\color{orange}Orange} & Ended \\
{\color{teal}Green} & Ready \\
{\color{red}Red} & Running \\
{\color{violet}Violet} & Evicted \\
{\color{brown}Brown} & Unknown \\
\bottomrule
\end{tabular}
\caption{Execution state legend for the graphs}
\end{subfigure}
\hfill}
\begin{figure}
\machinetimewastelegend
\machinetimewaste[1]{All clusters}{output_9_16.png}
\machinetimewaste{Cluster A}{output_9_0.png}
\machinetimewaste{Cluster B}{output_9_2.png}
\machinetimewaste{Cluster C}{output_9_4.png}
\machinetimewaste{Cluster D}{output_9_6.png}
\machinetimewaste{Cluster E}{output_9_8.png}
\machinetimewaste{Cluster F}{output_9_10.png}
\machinetimewaste{Cluster G}{output_9_12.png}
\machinetimewaste{Cluster H}{output_9_14.png}
\caption{Total task time (in milliseconds) spent in each execution phase w.r.t. task termination.}
\end{figure}
\begin{figure}
\machinetimewastelegend
\machinetimewaste[1]{All clusters}{output_9_17.png}
\machinetimewaste{Cluster A}{output_9_1.png}
\machinetimewaste{Cluster B}{output_9_3.png}
\machinetimewaste{Cluster C}{output_9_5.png}
\machinetimewaste{Cluster D}{output_9_7.png}
\machinetimewaste{Cluster E}{output_9_9.png}
\machinetimewaste{Cluster F}{output_9_11.png}
\machinetimewaste{Cluster G}{output_9_13.png}
\machinetimewaste{Cluster H}{output_9_15.png}
\caption{Relative task time (in milliseconds) spent in each execution phase w.r.t. task termination.}
\end{figure}

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8
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@book{Stru1899a,
Author = {William Strunk and E. B. White},
Title = {The Elements of Style},
Edition = {4th},
ISBN = {0-205-30902-X},
Keywords = {},
Publisher = {Longman Publishers},
Year = {1899}}

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@ -0,0 +1,265 @@
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%
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%
%
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