Thesis Open Access

Application aware digital objects access and distribution using Named Data Networking (NDN)

Mousa, Rahaf


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    <subfield code="a">&lt;p&gt;In big data infrastructures, Persistent Identifiers (PIDs) are widely used to identify digital&lt;/p&gt;

&lt;p&gt;content and research data. A typical example of PIDs is the Digital Object Identifier (DOI). In&lt;/p&gt;

&lt;p&gt;a data centric application (such as a scientific workflow) it is often required to fetch different&lt;/p&gt;

&lt;p&gt;data objects from multiple locations. When reproducing a workflow published by community,&lt;/p&gt;

&lt;p&gt;data objects involved in the workflow often have PIDs. In this project we investigated how to&lt;/p&gt;

&lt;p&gt;optimize the fetching and sharing of DOI identified objects with Information centric networking&lt;/p&gt;

&lt;p&gt;paradigm such as Named Data Networking (NDN). In order to achieve that goal, first we&lt;/p&gt;

&lt;p&gt;presented an approach for integrating PIDs with Named Data Networking (NDN) networks.&lt;/p&gt;

&lt;p&gt;NDN identifies digital objects with their names and route them also based on their names.&lt;/p&gt;

&lt;p&gt;In addition, we proposed an approach for optimizing the NDN network’s performance using&lt;/p&gt;

&lt;p&gt;application level knowledge, such as the size, number, and order of the requested objects. We&lt;/p&gt;

&lt;p&gt;investigated the effect of ordering a group of objects in ascending or descending order according&lt;/p&gt;

&lt;p&gt;to their sizes before requesting them one by one. The results showed that the order of the&lt;/p&gt;

&lt;p&gt;requests can dramatically influence performance of fetching objects from NDN networks.&lt;/p&gt;</subfield>
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