report: added figures
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,maggicl,Apple2gs.local,10.05.2021 18:02,file:///Users/maggicl/Library/Application%20Support/LibreOffice/4;
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"cells": [
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{
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"cell_type": "markdown",
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"id": "built-symbol",
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"metadata": {},
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"source": [
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"# Machine configurations\n",
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "stuffed-lightning",
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"metadata": {},
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"outputs": [],
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"source": [
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "upper-lloyd",
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"metadata": {},
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"outputs": [],
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"source": [
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "presidential-farmer",
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"metadata": {},
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"outputs": [
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "informative-vietnam",
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"metadata": {},
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"outputs": [],
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"source": [
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "pretty-taiwan",
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"metadata": {},
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"outputs": [
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{
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "supreme-hepatitis",
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"metadata": {},
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"outputs": [],
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"source": []
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.9"
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"version": "3.8.3"
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}
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},
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"nbformat": 4,
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documentclass: usiinfbachelorproject
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title: Understanding and Comparing Unsuccessful Executions in Large Datacenters
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author: Claudio Maggioni
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pandoc-options:
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- --filter=pandoc-include
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- --latex-engine-opt=--shell-escape
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- --latex-engine-opt=--enable-write18
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header-includes:
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- |
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```{=latex}
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\usepackage{subcaption}
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\usepackage{booktabs}
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\usepackage{graphicx}
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\captionsetup{labelfont={bf}}
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%\subtitle{The (optional) subtitle}
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@ -29,57 +39,116 @@ header-includes:
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```
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---
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Introduction
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============
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# Introduction (including Motivation)
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General issues
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--------------
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# State of the Art
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Latex is not so complex. If you aren't familiar with it just spend some
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time in googling for latex commands (e.g. font formats, tables, figures,
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items,...).
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- Introduce Ros\'a 2015 DSN paper on analysis
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- Describe Google Borg clusters
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- Describe Traces contents
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- Differences between 2011 and 2019 traces
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Getting started
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---------------
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# Project requirements and analysis
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In order to use the bachelor thesis template, be sure that the following
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files are present in your working directory:
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(describe our objective with this analysis in detail)
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- usiinfbachelorproject.cls (The latex template)
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# Analysis methodology
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- logo-info.pdf (The logo figure)
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## Technical overview of traces' file format and schema
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- references.bib (The references file)\
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## Overview on challenging aspects of analysis (data size, schema, avaliable computation resources)
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Compilation issues
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------------------
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## Introduction on apache spark
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If you are not familiar with Tex, I advise you to download TexShop for
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Mac OS.\
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To include the references and display them in the final pdf, you have
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first to typeset this file with *LaTex* (ComboBox upper left, if you use
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TexShop), then with *BibTex* and finally again with *LaTex*.\
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In order to resolve figures/table/... references you have to run 2 times
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the (latex) typeset.
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## General workflow description of apache spark workflow
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Document structure
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------------------
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The Google 2019 Borg cluster traces analysis were conducted by using Apache
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Spark and its Python 3 API (pyspark). Spark was used to execute a series of
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queries to perform various sums and aggregations over the entire dataset
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provided by Google.
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Some basic sections:
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In general, each query follows a general Map-Reduce template, where traces are
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first read, parsed, filtered by performing selections, projections and computing
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new derived fields. Then, the trace records are often grouped by one of their
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fields, clustering related data toghether before a reduce or fold operation is
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applied to each grouping.
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- Introduction (including Motivation)
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Most input data is in JSONL format and adheres to a schema Google profided in
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the form of a protobuffer specification[^1].
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- State of the Art
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[^1]: [Google 2019 Borg traces Protobuffer specification on Github](
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https://github.com/google/cluster-data/blob/master/clusterdata_trace_format_v3.proto)
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- Project requirements and analysis
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On of the main quirks in the traces is that fields that have a "zero" value
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(i.e. a value like 0 or the empty string) are often omitted in the JSON object
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records. When reading the traces in Apache Spark is therefore necessary to check
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for this possibility and populate those zero fields when omitted.
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- Project design (top-down)
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Most queries use only two or three fields in each trace records, while the
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original records often are made of a couple of dozen fields. In order to save
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memory during the query, a projection is often applied to the data by the means
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of a .map() operation over the entire trace set, performed using Spark's RDD
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API.
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- Implementation issues (bottom-up)
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Another operation that is often necessary to perform prior to the Map-Reduce core of
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each query is a record filtering process, which is often motivated by the
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presence of incomplete data (i.e. records which contain fields whose values is
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unknown). This filtering is performed using the .filter() operation of Spark's
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RDD API.
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- Tests (methodology, results, comments)
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The core of each query is often a groupBy followed by a map() operation on the
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aggregated data. The groupby groups the set of all records into several subsets
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of records each having something in common. Then, each of this small clusters is
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reduced with a .map() operation to a single record. The motivation behind this
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computation is often to analyze a time series of several different traces of
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programs. This is implemented by groupBy()-ing records by program id, and then
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map()-ing each program trace set by sorting by time the traces and computing the
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desired property in the form of a record.
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Sometimes intermediate results are saved in Spark's parquet format in order to
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compute and save intermediate results beforehand.
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## General Query script design
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## Ad-Hoc presentation of some analysis scripts (w diagrams)
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# Analysis (w observations)
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## machine_configs
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\input{figures/machine_configs}
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**Observations**:
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- machine configurations are definitely more varied than the ones in the 2011
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traces
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- some clusters have more machine variability
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## machine_time_waste
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\input{figures/machine_time_waste}
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**Observations**:
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## task_slowdown
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## spatial_resource_waste
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## figure_7
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## figure_8
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## figure_9
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## table_iii, table_iv, figure_v
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## Potential causes of unsuccesful executions
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# Implementation issues -- Analysis limitations
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## Discussion on unknown fields
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## Limitation on computation resources required for the analysis
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## Other limitations ...
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# Conclusions and future work or possible developments
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- Conclusions and future work or possible developments
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Some examples
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-------------
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Binary file not shown.
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\newcommand{\machineconfigs}[3][0.9\textwidth]{
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\begin{subfigure}{0.32\textwidth}
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\begin{minipage}[c][#1]{\textwidth}%
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\resizebox{\textwidth}{!}{
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\centering
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\begin{tabular}{llll}
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\toprule
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\textbf{CPU (NCU)} & \textbf{RAM (NMU)} & \textbf{Machine count} &
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\textbf{\% Machines} \\
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\midrule
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#3
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\bottomrule
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\end{tabular}}
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\end{minipage}
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\caption{#2}
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\end{subfigure}}
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\begin{figure}
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\centering
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\machineconfigs[1.2\textwidth]{All clusters}{
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Unknown & Unknown & 8729 & 1.639218\% \\
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1.000000 & 0.500000 & 124234 & 23.329891\% \\
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0.591797 & 0.333496 & 103013 & 19.344801\% \\
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0.259277 & 0.166748 & 78078 & 14.662260\% \\
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0.708984 & 0.333496 & 55801 & 10.478864\% \\
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0.386719 & 0.333496 & 36237 & 6.804943\% \\
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0.958984 & 0.500000 & 31151 & 5.849843\% \\
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0.708984 & 0.666992 & 29594 & 5.557454\% \\
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0.386719 & 0.166748 & 27011 & 5.072393\% \\
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1.000000 & 1.000000 & 12286 & 2.307187\% \\
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0.591797 & 0.166748 & 9902 & 1.859496\% \\
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1.000000 & 0.250000 & 7550 & 1.417814\% \\
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0.958984 & 1.000000 & 3552 & 0.667030\% \\
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0.259277 & 0.333496 & 3024 & 0.567877\% \\
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0.591797 & 0.666992 & 1000 & 0.187790\% \\
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0.259277 & 0.083374 & 634 & 0.119059\% \\
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0.958984 & 0.250000 & 600 & 0.112674\% \\
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0.500000 & 0.062500 & 54 & 0.010141\% \\
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0.500000 & 0.250000 & 34 & 0.006385\% \\
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0.479492 & 0.250000 & 12 & 0.002253\% \\
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0.708984 & 0.250000 & 6 & 0.001127\% \\
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0.591797 & 0.250000 & 4 & 0.000751\% \\
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0.708984 & 0.500000 & 2 & 0.000376\% \\
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0.479492 & 0.500000 & 2 & 0.000376\% \\
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}
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\machineconfigs[1.2\textwidth]{A cluster}{
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Unknown & Unknown & 1377 & 1.623170\% \\
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0.591797 & 0.333496 & 29487 & 34.758469\% \\
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1.000000 & 0.500000 & 13440 & 15.842705\% \\
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0.708984 & 0.333496 & 12495 & 14.728764\% \\
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0.386719 & 0.333496 & 9057 & 10.676144\% \\
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0.386719 & 0.166748 & 5265 & 6.206238\% \\
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0.708984 & 0.666992 & 4608 & 5.431784\% \\
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1.000000 & 1.000000 & 4446 & 5.240823\% \\
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0.591797 & 0.166748 & 2484 & 2.928071\% \\
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0.958984 & 0.500000 & 1143 & 1.347337\% \\
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0.958984 & 1.000000 & 654 & 0.770917\% \\
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1.000000 & 0.250000 & 366 & 0.431431\% \\
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0.479492 & 0.250000 & 6 & 0.007073\% \\
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0.708984 & 0.250000 & 6 & 0.007073\% \\
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}
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\machineconfigs[1.2\textwidth]{Cluster B}{
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Unknown & Unknown & 134 & 0.264812\% \\
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0.591797 & 0.333496 & 16184 & 31.982926\% \\
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1.000000 & 0.500000 & 9790 & 19.347061\% \\
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0.708984 & 0.333496 & 8448 & 16.694992\% \\
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0.958984 & 0.500000 & 5502 & 10.873088\% \\
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0.708984 & 0.666992 & 3832 & 7.572823\% \\
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1.000000 & 1.000000 & 2214 & 4.375321\% \\
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0.591797 & 0.166748 & 2152 & 4.252796\% \\
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0.386719 & 0.333496 & 816 & 1.612584\% \\
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0.958984 & 1.000000 & 618 & 1.221296\% \\
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0.591797 & 0.666992 & 500 & 0.988103\% \\
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0.386719 & 0.166748 & 412 & 0.814197\% \\
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}
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\machineconfigs{Cluster C}{
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Unknown & Unknown & 1466 & 2.274208\% \\
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0.259277 & 0.166748 & 15754 & 24.439204\% \\
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0.386719 & 0.333496 & 11104 & 17.225652\% \\
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0.591797 & 0.333496 & 10404 & 16.139741\% \\
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0.958984 & 0.500000 & 6634 & 10.291334\% \\
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1.000000 & 0.500000 & 5654 & 8.771059\% \\
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0.386719 & 0.166748 & 3580 & 5.553660\% \\
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0.708984 & 0.666992 & 2900 & 4.498774\% \\
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1.000000 & 1.000000 & 2736 & 4.244361\% \\
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1.000000 & 0.250000 & 2132 & 3.307375\% \\
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0.958984 & 1.000000 & 766 & 1.188297\% \\
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0.708984 & 0.333496 & 620 & 0.961807\% \\
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0.958984 & 0.250000 & 600 & 0.930781\% \\
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0.591797 & 0.166748 & 112 & 0.173746\% \\
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}
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\machineconfigs{Cluster D}{
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Unknown & Unknown & 498 & 0.794309\% \\
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0.591797 & 0.333496 & 28394 & 45.288376\% \\
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0.386719 & 0.333496 & 8402 & 13.401174\% \\
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0.259277 & 0.166748 & 8020 & 12.791885\% \\
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0.386719 & 0.166748 & 5806 & 9.260559\% \\
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0.708984 & 0.666992 & 4380 & 6.986092\% \\
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0.708984 & 0.333496 & 3924 & 6.258772\% \\
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0.591797 & 0.166748 & 2548 & 4.064055\% \\
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0.259277 & 0.333496 & 426 & 0.679469\% \\
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1.000000 & 0.500000 & 292 & 0.465739\% \\
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0.591797 & 0.250000 & 4 & 0.006380\% \\
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0.708984 & 0.500000 & 2 & 0.003190\% \\
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}
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\machineconfigs{Cluster E}{
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Unknown & Unknown & 536 & 0.671915\% \\
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0.259277 & 0.166748 & 38452 & 48.202377\% \\
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0.708984 & 0.333496 & 11786 & 14.774608\% \\
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0.958984 & 0.500000 & 8646 & 10.838389\% \\
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0.708984 & 0.666992 & 7606 & 9.534674\% \\
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1.000000 & 0.500000 & 5586 & 7.002457\% \\
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0.386719 & 0.166748 & 4470 & 5.603470\% \\
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0.259277 & 0.333496 & 1268 & 1.589530\% \\
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0.259277 & 0.083374 & 634 & 0.794765\% \\
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0.591797 & 0.333496 & 324 & 0.406158\% \\
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1.000000 & 0.250000 & 268 & 0.335957\% \\
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1.000000 & 1.000000 & 138 & 0.172993\% \\
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0.500000 & 0.062500 & 54 & 0.067693\% \\
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0.500000 & 0.250000 & 4 & 0.005014\% \\
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}
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\machineconfigs{Cluster F}{
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Unknown & Unknown & 1432 & 2.299958\% \\
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1.000000 & 0.500000 & 41340 & 66.396839\% \\
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0.708984 & 0.333496 & 6878 & 11.046866\% \\
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0.591797 & 0.333496 & 5564 & 8.936430\% \\
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0.958984 & 0.500000 & 2172 & 3.488484\% \\
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0.386719 & 0.166748 & 1544 & 2.479843\% \\
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0.708984 & 0.666992 & 1244 & 1.998008\% \\
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1.000000 & 0.250000 & 792 & 1.272044\% \\
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0.958984 & 1.000000 & 536 & 0.860878\% \\
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0.386719 & 0.333496 & 398 & 0.639234\% \\
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1.000000 & 1.000000 & 344 & 0.552504\% \\
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0.500000 & 0.250000 & 18 & 0.028910\% \\
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}
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\machineconfigs{Cluster G}{
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Unknown & Unknown & 1566 & 2.261568\% \\
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0.259277 & 0.166748 & 15852 & 22.892958\% \\
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1.000000 & 0.500000 & 11808 & 17.052741\% \\
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0.708984 & 0.333496 & 7968 & 11.507134\% \\
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0.591797 & 0.333496 & 7830 & 11.307839\% \\
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0.386719 & 0.166748 & 4690 & 6.773150\% \\
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0.708984 & 0.666992 & 4258 & 6.149269\% \\
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0.958984 & 0.500000 & 4196 & 6.059731\% \\
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0.386719 & 0.333496 & 3864 & 5.580267\% \\
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0.591797 & 0.166748 & 2606 & 3.763503\% \\
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1.000000 & 0.250000 & 2100 & 3.032754\% \\
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0.259277 & 0.333496 & 1330 & 1.920744\% \\
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0.958984 & 1.000000 & 778 & 1.123563\% \\
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1.000000 & 1.000000 & 378 & 0.545896\% \\
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0.500000 & 0.250000 & 12 & 0.017330\% \\
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0.479492 & 0.250000 & 6 & 0.008665\% \\
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0.479492 & 0.500000 & 2 & 0.002888\% \\
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}
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\machineconfigs{Cluster H}{
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Unknown & Unknown & 1720 & 2.933251\% \\
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1.000000 & 0.500000 & 36324 & 61.946178\% \\
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0.591797 & 0.333496 & 4826 & 8.230158\% \\
|
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0.708984 & 0.333496 & 3682 & 6.279205\% \\
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0.958984 & 0.500000 & 2858 & 4.873973\% \\
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0.386719 & 0.333496 & 2596 & 4.427163\% \\
|
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1.000000 & 1.000000 & 2030 & 3.461919\% \\
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1.000000 & 0.250000 & 1892 & 3.226577\% \\
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0.386719 & 0.166748 & 1244 & 2.121491\% \\
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0.708984 & 0.666992 & 766 & 1.306320\% \\
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0.591797 & 0.666992 & 500 & 0.852689\% \\
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||||
0.958984 & 1.000000 & 200 & 0.341076\% \\
|
||||
}
|
||||
\caption{Overwiew of machine configurations in terms of CPU and RAM resources for each cluster}
|
||||
\end{figure}
|
||||
|
|
@ -0,0 +1,58 @@
|
|||
\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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|
|
@ -3,7 +3,6 @@
|
|||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"id": "black-funeral",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -19,7 +18,6 @@
|
|||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 70,
|
||||
"id": "hawaiian-cabin",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -29,7 +27,6 @@
|
|||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 84,
|
||||
"id": "blessed-updating",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -68,7 +65,6 @@
|
|||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 85,
|
||||
"id": "consistent-toilet",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -406,9 +402,9 @@
|
|||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "venv",
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "venv"
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
|
@ -420,7 +416,7 @@
|
|||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.4"
|
||||
"version": "3.8.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
|
|
|||
Loading…
Reference in New Issue