<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>1191</id>
    <completedYear/>
    <publishedYear>2014</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>253</pageFirst>
    <pageLast>268</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>bookpart</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Dodrecht</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-02-06</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">How to improve the reactiveness and efficiency of embedded multicore systems by use of probabilistic simulation and optimization techniques</title>
    <abstract language="eng">Safe and reliable multi-core technology becomes more and more important in the field of embedded systems. Today’s and future embedded systems require increasing performance while being more energy efficient. Moreover, the functional safety for these embedded systems has to be improved or developed completely new. In this chapter, we first address the challenges of embedded multi-core real-time systems. To raise the resilience of such systems we use the deadlock-free synchronization model of Block et al. (A flexible real-time locking protocol for multiprocessors. 2012 IEEE international conference on embedded and real-time computing systems and applications, vol 0, pp 47–56, 2007). The metric mean Normalized Blocking Time (mNBT) is hereby used to measure the timing effects of the blocking behavior of strongly interacting tasks. In a second step, we present a model-based approach to map the tasks of an embedded real-time system to the cores of a multi-core processor. Moreover, we derive an execution time model from runtime measurements of software functions. This information is then used to perform precise probabilistic simulations of different task-to-core mappings and evaluate them with regard to task response times, inter-task blocking overhead and load distribution. Subsequently, we integrate the probabilistic simulation within an optimization technique to systematically improve the task-to-core mapping. We conclude with a case-study, where we demonstrate the effectiveness of the presented approach by optimizing the task-to-core mapping of a practical automotive powertrain system.</abstract>
    <parentTitle language="eng">Improving Disaster Resilience and Mitigation - New Means and Tools. NATO Science for Peace and Security Series C: Environmental Security</parentTitle>
    <identifier type="doi">10.1007/978-94-017-9136-6_16</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <author>Jürgen Mottok</author>
    <author>Martin Alfranseder</author>
    <author>Stefan Schmidhuber</author>
    <author>Matthias Mucha</author>
    <author>Andreas Sailer</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Embed System</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Functional Safety</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Arrival Curve</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Task Allocation Problem</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deadline Violation</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>
