Conference paper Open Access

An Exposed Closed-Loop Model forCustomer-Driven Service Assurance Automation

Xie, Min; Michelinakis, Foivos; Dreibholz, Thomas; S. Pujol-Roig, Juan; Malacarne, Sara; Majumdar, Sayantini; Yi Poe, Wint; M. Elmokashfi, Ahmed


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    <dct:title>An Exposed Closed-Loop Model forCustomer-Driven Service Assurance Automation</dct:title>
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    <dcat:keyword>autonomous management</dcat:keyword>
    <dcat:keyword>closed loop</dcat:keyword>
    <dcat:keyword>machine learning</dcat:keyword>
    <dcat:keyword>service exposure</dcat:keyword>
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    <dct:description>&lt;p&gt;This repository accompanies the conference paper with the same title: &amp;quot;An Exposed Closed-Loop Model forCustomer-Driven Service Assurance Automation&amp;quot;, published in EuCNC 2021. In the paper we propose&amp;nbsp;an exposed CL model based on service exposure which is&amp;nbsp;meant&amp;nbsp;to automate service assurance tasks like auto-scaling in a network function virtualization (NFV) system orchestrated by ETSI Open Source MANO (OSM).&amp;nbsp;Here we provide&amp;nbsp;the testbed-generated experimental results we used to validate our proof-of-concept.&lt;/p&gt; &lt;p&gt;Please refer to the conference paper for details about the topology and the scenario of each experiment.&lt;/p&gt; &lt;p&gt;The csv files can be grouped in 3 categories, which correspond to the three vantage points (levels) of monitoring. Each experiment repetition is represented by a hash. The hash is either in the file name or as a column inside the csv files.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;1. prometheus.csv&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;Some of the columns are:&lt;/p&gt; &lt;ul&gt; &lt;li&gt;timestamp: timestamp in Oslo timezone&lt;/li&gt; &lt;li&gt;experimentHash: the unique identifier of the experiment&lt;/li&gt; &lt;li&gt;vdu_name: Where the data comes from&lt;/li&gt; &lt;li&gt;metric_name: what is being measured&lt;/li&gt; &lt;li&gt;value: the actual value.&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;It was hard to make this csv&amp;nbsp;flat&amp;nbsp;(ie. use as a unique index the timestamp), because the column names would be very long. The way to use it is to filter per,&amp;nbsp;&lt;em&gt;&lt;strong&gt;&amp;lt;experimentHash, vdu_name, metric_name&amp;gt;&lt;/strong&gt;&lt;/em&gt;&amp;nbsp;and then use&amp;nbsp;&lt;em&gt;&lt;strong&gt;&amp;lt;timestamp, value&amp;gt;&lt;/strong&gt;&lt;/em&gt;&amp;nbsp;to plot the timeseries of the value for this metric (experimentHash) of this component (vdu_name) for this experiment (experimentHash).&lt;/p&gt; &lt;p&gt;&lt;strong&gt;2.&amp;nbsp;sysstat&amp;nbsp;csv&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;There are 3 csv with the systat values . They contain&amp;nbsp;exactly the same information&amp;nbsp;just in different representations. So, pick the one that is most convenient for you.&lt;/p&gt; &lt;p&gt;&lt;strong&gt;3. packet capture csv (csv that starts with a hash)&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;These are csvs from the packet capture (pcap) files. You can tell by the name which component created them. The files that end with a number are captured at the client (UE). Refer to the topology figure of the conference publication.&lt;/p&gt;</dct:description>
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