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<!doctype html>
<html>
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no">
<title>MLInNet</title>
<link rel="icon" type="image/icon" href="img/favicon.png">
<link rel="stylesheet" href="css/share.css">
<link rel="stylesheet" href="css/reveal.css">
<link rel="stylesheet" href="css/theme/material.css">
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<link rel="stylesheet" href="lib/css/zenburn.css">
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var link = document.createElement( 'link' );
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<body>
<div class="reveal">
<div class="slides">
<section data-background-image="img/background.jpeg">
<h1 style="color: white">Machine Learning in Networking</h1>
<h3 style="color: white">Parham Alvani</h3>
<h5><a href="https://1995parham.github.io" >@1995parham</a></h5>
</section>
<section>
<div class="toc"></div>
</section>
<section>
<div class="toc" data-selected="0"></div>
</section>
<section>
<h3>Introduction</h3>
<ul>
<li>Networks needs more intelligence to deploy</li>
<li>Traditional networks are <span class="material-italic">distributed</span></li>
<li>Each node, such as router or switch, can only view and act over a <span class="material-italic">small partion</span> of the system.</li>
<li>Learning from the nodes that have only a small partial view of the complete system to perform contol beyond the local domain is <span class="material-bold">very complex</span></li>
</ul>
</section>
<section>
<h3>Introduction</h3>
<ul>
<li>SDN decouples the control plane and the data plane</li>
<li>The network resources in SDN are managed by a logically centralized controller</li>
</ul>
</section>
<section>
<h3>Software Defined Networking</h3>
<img src="img/sdn.jpg" alt="sdn">
</section>
<section>
<h3>Machine Learning in SDN</h3>
<ul>
<li>Traffic Classification</li>
<li>Routing Optimization</li>
<li>QoS/QoE Prediction</li>
<li>Resource Management</li>
<li>Security</li>
</ul>
</section>
<section>
<div class="toc" data-selected="1"></div>
</section>
<section>
<ul>
<li>Traffic classificiation is an important network function, which provides a way to perform fine-grained network management by identifying different flow types.</li>
<li>In general <span class="material-bold">supervised and semi-supervised</span> learning algorithms can be used</li>
<li>Deep Packet Inspection (DPI) is a common method to label traffic flows, but it incurs high computational cost when a large number of traffic flows are labeled</li>
</ul>
</section>
<section>
<div class="toc" data-selected="2"></div>
</section>
<section>
<ul>
<li>Routing is a fundamental network function</li>
<li>Supervised Learning for obtaining the optimal huristic-like routing solution</li>
<li>RL algorithms without labeled taining datasets can have flexible optimization targets</li>
<li>The state space is composed of <span class="material-bold">network and traffic states</span></li>
</ul>
</section>
<section>
<div class="toc" data-selected="3"></div>
</section>
<section>
<ul>
<li>QoS prediction aims to discover the quantitative correlations between KPIs and QoS parameters</li>
<li>QoS parameters are generally continuous data (Regression)</li>
<li>As an example here we can use NN-Model for network delay estimation instead of M/M/1 model</li>
<li>QoE prediction aims to discover the quantitative correlations between QoS parameters and QoE values</li>
<li>QoE values are generally discrete data (Classification)</li>
</ul>
</section>
<section>
<div class="toc" data-selected="4"></div>
</section>
<section>
<ul>
<li>Efficient network resource management is the primary requirement of network operators to improve network performance</li>
<li>The <span class="material-italic">data plane</span> resource allocation problem is generally considered as a decision-making task.</li>
<li><span class="material-bold">RL</span> and <span class="material-bold">ML-based game theory</span> are two effective approaches</li>
<li>RL in single-tenancy</li>
<li>ML-based game theory in multi-tenancy</li>
<li>The mapping between the resource consumption and control message rate is very important for <span class="material-italic">control plane</span> resource allocation</li>
</ul>
</section>
<section>
<div class="toc" data-selected="5"></div>
</section>
<section>
<ul>
<li>An <span class="material-bold">Instrusion Detection System (IDS)</span> is a device or software application and its objective is to monitor the events in a network system and identify possible attacks</li>
<li>signature-based IDS and anomaly-based IDS</li>
<li>Machine learning methods are widely used in anomaly-based IDS by trainning a model to identify normal activities and intrusions</li>
<li>Intrusion detection problem can be considered as a classification task</li>
</ul>
</section>
<section>
<div class="toc" data-selected="6"></div>
</section>
<section>
<ul>
<li>High-quality Training Datasets</li>
<li>Distributed Multi-controller Platform</li>
<li>Improving Network Security</li>
<li>Cross-layer Network Optimization</li>
<li>Incrementally Deployed SDN</li>
</ul>
</section>
<section data-background-image="img/thank-you.jpeg">
</section>
</div>
</div>
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</body>
</html>