<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>1161</id>
    <completedYear/>
    <publishedYear>2014</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>233</pageFirst>
    <pageLast>238</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-02-06</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Optimization of Real-Time Multicore Systems Reached by a Genetic Algorithm Approach for Runnable Sequencing</title>
    <abstract language="eng">The deployment of complex real-time systems with everyday increasing demands and possibilities, is a challenging task for engineers when performance and efficiency have to be maximized while cost have to be minimized at the same time. For already designed systems it became necessary to perform different modifications in order to find optimal software architecture configuration by respecting all timing constraints which are essential when speaking of real-time systems. In this work, we present a model-based approach of optimizing the execution sequence of runnables within tasks in order to reduce the system's reaction times by improving the overall signal flow duration. Hereby, a genetic optimization algorithm is used to create and evaluate multiple solutions for the runnable sequencing problem. We conclude by demonstration the efficiency of the presented approach with experimental results.</abstract>
    <parentTitle language="eng">2014 International Conference on Applied Electronics, Pilsen, Czech Republic</parentTitle>
    <identifier type="doi">10.1109/AE.2014.7011709</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <author>Erna Oklapi</author>
    <author>Michael Deubzer</author>
    <author>Stefan Schmidhuber</author>
    <author>Erjola Lalo</author>
    <author>Jürgen Mottok</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>runnable sequencing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>embedded real-time systems</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>genetic algorithms</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>multi-core</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>optimization</value>
    </subject>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
  </doc>
</export-example>
