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README.html
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<!DOCTYPE html>
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<title>mgr-simulation</title>
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<body class="article">
<div id="header">
<h1>mgr-simulation</h1>
<div class="details">
<span id="author" class="author">Rafał Sławik</span><br>
<span id="email" class="email"><a href="mailto:[email protected]">[email protected]</a></span><br>
</div>
</div>
<div id="content">
<div id="preamble">
<div class="sectionbody">
<div class="paragraph">
<p>Simulation framework for online packet scheduling algorithms.</p>
</div>
</div>
</div>
<div class="sect1">
<h2 id="_overview">Overview</h2>
<div class="sectionbody">
<div class="paragraph">
<p>Framework simulates a scheduling algorithm against some adversary,
who has control over packet arrivals and error injections.
When algorithm under test makes a scheduling decision, adversary is asked to schedule an error.</p>
</div>
<div class="admonitionblock note">
<table>
<tr>
<td class="icon">
<i class="fa icon-note" title="Note"></i>
</td>
<td class="content">
It is required to implement an algorithm and an adversary in a special form.
</td>
</tr>
</table>
</div>
<div class="paragraph">
<p>Work is based on following research papers:</p>
</div>
<div class="ulist bibliography">
<ul class="bibliography">
<li>
<p><a id="sirocco"></a>[sirocco] A. F. Anta, C. Georgiou, D. R. Kowalski, J. Widmer, and E. Zavou,
<em>Measuring the impact of adversarial errors on packet scheduling strategies</em>.
SIROCCO 2013, Springer.</p>
</li>
</ul>
</div>
</div>
</div>
<div class="sect1">
<h2 id="_installation">Installation</h2>
<div class="sectionbody">
<div class="paragraph">
<p>The simulation framework works on all major platforms.
It depends on:</p>
</div>
<div class="ulist">
<ul>
<li>
<p><a href="https://www.python.org/">Python 3.4</a></p>
</li>
<li>
<p><a href="http://matplotlib.org/">matplotlib</a></p>
</li>
</ul>
</div>
<div class="sect2">
<h3 id="_linux">Linux</h3>
<div class="olist arabic">
<ol class="arabic">
<li>
<p>Install latest Python 3 using package manager.</p>
</li>
<li>
<p>Execute <code>pip install matplotlib</code></p>
</li>
</ol>
</div>
</div>
<div class="sect2">
<h3 id="_os_x">OS X</h3>
<div class="olist arabic">
<ol class="arabic">
<li>
<p>Install <a href="http://brew.sh/">Homebrew</a></p>
</li>
<li>
<p>Install Python 3 <code>brew install python3</code></p>
</li>
<li>
<p>Execute <code>pip3 install matplotlib</code></p>
</li>
</ol>
</div>
</div>
<div class="sect2">
<h3 id="_windows">Windows</h3>
<div class="olist arabic">
<ol class="arabic">
<li>
<p>Install latest Python 3 from <a href="https://www.python.org/">www.python.org</a>.</p>
</li>
<li>
<p>Open Command Line and go to <code>C:\Python34\Scripts</code> (assuming default installation path).</p>
</li>
<li>
<p>Execute <code>pip.exe install matplotlib</code>.
This step may fail if Visual C++ 2010 (part of Visual Studio 2010) is not installed.
Visual C++ is available <a href="http://www.dobreprogramy.pl/Visual-C,Program,Windows,12107.html">here</a>.</p>
</li>
</ol>
</div>
</div>
</div>
</div>
<div class="sect1">
<h2 id="_experiment">Experiment</h2>
<div class="sectionbody">
<div class="paragraph">
<p>Each experiment constists of few steps.
First, a file describing model has to be created.
Based on the model file, a sequence on packet arrival events is generated.
The sequence is used in simulation of both algorithms (algorithm under test and adversary’s).</p>
</div>
<div class="sect2">
<h3 id="_model">Model</h3>
<div class="paragraph">
<p>A model file contains all model parameters:</p>
</div>
<div class="olist arabic">
<ol class="arabic">
<li>
<p>packet lengths; separated with spaces,</p>
</li>
<li>
<p>parameter λ; has to be ≥ 0.0,</p>
</li>
<li>
<p>length distribution; separated with spaces (i-th element on the list corresponds to i-th packet length),</p>
</li>
<li>
<p>speedup; has to be ≥ 1.0.</p>
</li>
</ol>
</div>
<div class="paragraph">
<p>Example model file may look like this:</p>
</div>
<div class="listingblock">
<div class="title">model.in</div>
<div class="content">
<pre>3 5 7
5.0
0.33 0.17 0.5
1.5</pre>
</div>
</div>
<div class="paragraph">
<p>In this example new packets appear with rate 5.
The propability of length 5 is 17%, length 3 — 33% and half of packets have length 7.
Simulation using this model gives an algorithm a 1.5 speedup.</p>
</div>
</div>
<div class="sect2">
<h3 id="_generate">Generate</h3>
<div class="paragraph">
<p>To generate a sequence of inject events use:</p>
</div>
<div class="literalblock">
<div class="content">
<pre>generate.py <generator> <n> <model-file></pre>
</div>
</div>
<div class="paragraph">
<p><code><generator></code> is the name of one of available generators,
<code><n></code> is the number of events to generate
and <code><model-file></code> points to a file with model description.</p>
</div>
<div class="sect3">
<h4 id="_available_generators">Available generators</h4>
<div class="ulist">
<ul>
<li>
<p><code>sirocco_thm9</code> — generates an inject event sequance using the strategy described in Theorem 9 (distribution parameters are ignored).</p>
</li>
<li>
<p><code>stochastic</code> — generates inject events according to Poisson process with parameter λ.
Packet length is randomly chosen with the probability from model file.</p>
</li>
</ul>
</div>
</div>
<div class="sect3">
<h4 id="_output_format">Output format</h4>
<div class="paragraph">
<p>The result is a sequence of pairs (time and the length of a packet).
Without loss of generality time starts with the arrival of first packet.
An example result looks like this:</p>
</div>
<div class="listingblock">
<div class="title">events.in</div>
<div class="content">
<pre>0.0 10.0
2.459899742060707 1.0
5.383135023609432 5.0
6.710508745166248 10.0
9.397366473753106 5.0
11.41576280873459 1.0
12.265779790163247 10.0
12.433092337654008 10.0
17.43791330370601 10.0
19.84048150554071 1.0
...</pre>
</div>
</div>
</div>
</div>
<div class="sect2">
<h3 id="_simulate">Simulate</h3>
<div class="paragraph">
<p>To run a simulation of an algorithm use:</p>
</div>
<div class="literalblock">
<div class="content">
<pre>simulate.py <algorithm> <adversary> <events-file> <model-file></pre>
</div>
</div>
<div class="paragraph">
<p><code><algorithm></code> is the name of one of available algorithms,
<code><adversary></code> is the name of one of available adversaries,
<code><events-file></code> is the file with packet arrival events generated in the previous step
and <code><model-file></code> points to a file with model description.</p>
</div>
<div class="sect3">
<h4 id="_algorithm">Algorithm</h4>
<div class="ulist">
<ul>
<li>
<p><code>SL</code> — Shortest Length,</p>
</li>
<li>
<p><code>LL</code> — Longest Length,</p>
</li>
<li>
<p><code>SLPreamble</code> — the algorithm<span class="footnote">[<a id="_footnoteref_1" class="footnote" href="#_footnote_1" title="View footnote.">1</a>]</span>
defined in section 3.2 in <a href="#sirocco">[sirocco]</a>;
it starts each phase with a preamble of short packets
(if there are at least <code>floor(l_2 / l_1)</code> short packets in the queue)
and then uses <code>LL</code>,</p>
</li>
<li>
<p><code>CSLPreamble</code> — the conditional variant of the <code>SLPreamble</code> algorithm;
depending on model parameters it uses
<code>SL</code> or <code>SLPreamble</code>,</p>
</li>
<li>
<p><code>Greedy</code> — the Algorithm 1 from the paper,
shown to achieve throughput at most 0.5
in a model with an arbitrary number of packet lengths and adversarial arrivals;
if the algorithm is about to transmit a packet <code>l_i</code>
it checks whether there are enough shorter packets in the queue to cover length <code>l_i</code>
and schedules them instead,</p>
</li>
<li>
<p><code>Prudent</code> --the Algorithm 5 from the paper,
shown to achieve throughput 1,
when run with speedup 2
in a model with an arbitrary number of packet lengths and adversarial arrivals;
it sends the special preamble and then switches to <code>LL</code>,</p>
</li>
<li>
<p><code>ESLPreamble</code> — a generalized version<span class="footnote">[<a id="_footnoteref_2" class="footnote" href="#_footnote_2" title="View footnote.">2</a>]</span>
of the <code>SLPreamble</code> algorithm;
it uses two preambles (one of length <code>l_2</code> and one of length <code>l_3</code>)
before switching to <code>LL</code>,</p>
</li>
<li>
<p><code>OnlySL</code> — an algorithm that schedules packets of the shrotest length only.</p>
</li>
</ul>
</div>
<div class="listingblock">
<div class="title">The pseudo code of the <code>ESLPreamble</code> algorithm</div>
<div class="content">
<pre>// queue[l_i] := the number of l_i packets in the queue
loop
// first preamble
if (queue[l_1] * l_1 >= l_2)
transmit packets l_1 up to the length of l_2
// second preamble
if (queue[l_1] * l_1 + queue[l_2] * l_2 >= l_3)
transmit packets l_1 and l_2 up to the length of l_3
loop
transmit longest unsent packet</pre>
</div>
</div>
</div>
<div class="sect3">
<h4 id="_adversary">Adversary</h4>
<div class="ulist">
<ul>
<li>
<p><code>NoErrors</code> — an adversary that does not inject any jamming errors,</p>
</li>
<li>
<p><code>SiroccoThm9</code> — the adversary from the proof of Theorem 9 in <a href="#sirocco">[sirocco]</a>,
used to show an upper bound for the throughput of algorithm SL under adversarial arrivals,</p>
</li>
<li>
<p><code>SiroccoThm11</code> — the adversary from the proof of Theorem 11 in <a href="#sirocco">[sirocco]</a>,
used to show that algorithm LL cannot achieve relative throughput larger than 0,
even under stochastic arrivals,</p>
</li>
<li>
<p><code>Sirocco</code> — the adversary defined in section 4.1 in <a href="#sirocco">[sirocco]</a>,
used to show an upper bound for the throughput of any algorithm
in a model with two packet lengths,</p>
</li>
<li>
<p><code>SiroccoL</code> — the modified version of the <code>Sirocco</code> adversary
that extends phases as much as possible,</p>
</li>
<li>
<p><code>ESirocco</code> — Adversary from part 3.2.1 in thesis</p>
</li>
</ul>
</div>
</div>
<div class="sect3">
<h4 id="_output_format_2">Output format</h4>
<div class="paragraph">
<p>The simulation produces a log that contains records
what the algorithm and the adversary were doing over time.
It includes information about packet arrivals, error injections,
successful and unsuccessful packet transmissions.
An example log of a simulation looks like this:</p>
</div>
<div class="listingblock">
<div class="title">simulation.log</div>
<div class="content">
<pre>...
138.68732775266903 inject 1.0
138.68732775266903 error
138.68732775266903 error
138.68732775266903 schedule ALG 1.0
138.68732775266903 schedule ADV 5.0
139.68732775266903 sent ALG
139.68732775266903 schedule ALG 5.0
143.68732775266903 sent ADV
143.68732775266903 error
...</pre>
</div>
</div>
</div>
</div>
<div class="sect2">
<h3 id="_analyze">Analyze</h3>
<div class="paragraph">
<p>There are several utilities for log analysis.
They offer a graphical representation of data from a log file.
Only the throughput metric and the size of the queue are supported.</p>
</div>
<div class="sect3">
<h4 id="_throughput">Throughput</h4>
<div class="paragraph">
<p>We are interested in the value of the <em>long term relative throughput</em>.
It is the ratio between
the total length of packets transmitted up to time <code>t</code> by an algorithm under test
and adversary’s algorithm
as <code>t</code> goes to infinity.
Utilities from our framework calculate that ratio in provided samples.</p>
</div>
<div class="sect4">
<h5 id="_metric_value">Metric value</h5>
<div class="paragraph">
<p>To obtain only the value of the throughput use:</p>
</div>
<div class="literalblock">
<div class="content">
<pre>throughput.py <log-file></pre>
</div>
</div>
<div class="paragraph">
<p><code><log-file></code> is a log created during a simulation.</p>
</div>
</div>
<div class="sect4">
<h5 id="_plot">Plot</h5>
<div class="paragraph">
<p>It is possible to draw a plot how the throughput ratio changes in time.
To obtain the plot use:</p>
</div>
<div class="literalblock">
<div class="content">
<pre>plot-throughput.py <log-file></pre>
</div>
</div>
<div class="paragraph">
<p><code><log-file></code> is a log created during a simulation.</p>
</div>
<div class="paragraph">
<p>One might want to add a reference value to the plot.
It is possible to do so by using:</p>
</div>
<div class="literalblock">
<div class="content">
<pre>plot-throughput.py <log-file> <reference-value></pre>
</div>
</div>
<div class="paragraph">
<p><code><reference-value></code> is a float value.
It is used to draw a horizontal line y = reference value.</p>
</div>
</div>
</div>
<div class="sect3">
<h4 id="_the_size_of_the_queue">The size of the queue</h4>
<div class="paragraph">
<p>Another way to characterize an algorithm is by looking at the size of the queue.
Competitiveness can be expressed in terms of the size of the queues of a specified packet length.
It allows us to detect which packet accumulate over time.
To obtain a plot use:</p>
</div>
<div class="literalblock">
<div class="content">
<pre>plot-queuesize.py <log-file></pre>
</div>
</div>
<div class="paragraph">
<p><code><log-file></code> is a log created during a simulation.</p>
</div>
</div>
<div class="sect3">
<h4 id="_visualization">Visualization</h4>
<div class="paragraph">
<p>The visualization utility allows to see
what an algorithm under test was doing at given point in time.
Whether it was transmitting a packet and
if that transmission ended with a success or was jammed.
To obtain such plot use:</p>
</div>
<div class="literalblock">
<div class="content">
<pre>visualize.py <log-file></pre>
</div>
</div>
<div class="paragraph">
<p>or</p>
</div>
<div class="literalblock">
<div class="content">
<pre>visualize.py <log-file> <from> <to></pre>
</div>
</div>
<div class="paragraph">
<p><code><log-file></code> is a log created during a simulation
and parameters <code>from</code> and <code>to</code> can be provide to restrict visualization
to specified interval [from, to].</p>
</div>
<div class="paragraph">
<p>Figure shows an example visualization.
Each packet length has its own color.
Segments drew with a solid line denote transmission that ended with a success.
A dashed line denotes a try to send one.
Vertical gray dashed lines show when jamming errors occurred.</p>
</div>
<div class="paragraph">
<p>Hence drawing thousands of packets is inefficient,
it is a good idea to restrict the plot to some interval.
It speeds up preparing the plot and makes it more readable.</p>
</div>
</div>
</div>
</div>
</div>
<div class="sect1">
<h2 id="_implementation_notes">Implementation notes</h2>
<div class="sectionbody">
<div class="paragraph">
<p>Our framework can be extended by
adding new implementations of algorithms and adversaries.
The only requirement is to add methods
allowing our simulation framework to execute them.</p>
</div>
<div class="sect2">
<h3 id="_algorithm_2">Algorithm</h3>
<div class="paragraph">
<p>The main part of an algorithm is the <code>schedule</code> method.
Is is called every time the algorithm is asked to make a scheduling decision.
The method should return the length of a packet that
the algorithm schedules now or <code>None</code> if it does not schedule anything.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlight"><code class="language-python" data-lang="python">class Algorithm:
def schedule(self): <i class="conum" data-value="1"></i><b>(1)</b>
yield ...</code></pre>
</div>
</div>
<div class="colist arabic">
<table>
<tr>
<td><i class="conum" data-value="1"></i><b>1</b></td>
<td><code>schedule</code> method — yields the length of the scheduled packet or <code>None</code></td>
</tr>
</table>
</div>
<div class="admonitionblock note">
<table>
<tr>
<td class="icon">
<i class="fa icon-note" title="Note"></i>
</td>
<td class="content">
Handling packets queue is done internally.
The framework guarantees that the method gets called only when
the algorithm has to make a scheduling decision.
</td>
</tr>
</table>
</div>
</div>
<div class="sect2">
<h3 id="_adversary_2">Adversary</h3>
<div class="paragraph">
<p>Besides the <code>schedule</code> method like an algorithm has,
an adversary has two methods <code>algorithmSchedules</code> and <code>adversarySchedules</code>
that are used to schedule jamming errors.</p>
</div>
<div class="listingblock">
<div class="content">
<pre class="highlight"><code class="language-python" data-lang="python">class NewAdversary(Adversary):
def schedule(self): <i class="conum" data-value="1"></i><b>(1)</b>
yield ...
def algorithmSchedules(self, packet): <i class="conum" data-value="2"></i><b>(2)</b>
return ...
def adversarySchedules(self, packet): <i class="conum" data-value="3"></i><b>(3)</b>
return ...</code></pre>
</div>
</div>
<div class="colist arabic">
<table>
<tr>
<td><i class="conum" data-value="1"></i><b>1</b></td>
<td><code>schedule</code> method — yields the length of the scheduled packet or <code>None</code></td>
</tr>
<tr>
<td><i class="conum" data-value="2"></i><b>2</b></td>
<td><code>algorithmSchedules</code> method — returns time in which next error occurs (in reaction on packet scheduled by algorithm)</td>
</tr>
<tr>
<td><i class="conum" data-value="3"></i><b>3</b></td>
<td><code>adversarySchedules</code> method — returns time in which next error occurs (in reaction on packet scheduled by adversary)</td>
</tr>
</table>
</div>
<div class="paragraph">
<p>The <code>schedule</code> method behaves exactly like in the algorithm.
The <code>algorithmSchedules</code> method is called when
the algorithm schedules a packet.
The length of the scheduled packet is passed to it.
It is used to schedule a jamming error in reaction on the packet schedules by the algorithm.
It should return time in which the next jamming error occurs or
<code>None</code> if the adversary decides not to cause an error.
The <code>adversarySchedules</code> method is called when
the adversary’s algorithm makes a scheduling decision.
It receives the length of the scheduled packet and
returns time in which the next jamming error occurs.
It may be used to inject errors in the middle of a phase
to eliminate unproductive time in the phase for the adversary’s algorithm.</p>
</div>
<div class="admonitionblock note">
<table>
<tr>
<td class="icon">
<i class="fa icon-note" title="Note"></i>
</td>
<td class="content">
Handling packet queue is done internally.
The framework guarantees that the methods get called only when
the adversary has to make a scheduling decision.
</td>
</tr>
</table>