Improving scheduling performance of a real-time system by incorporation of an artificial intelligence planner

• Jesus Fernandez-Conde ^[1] ; Pedro Cuenca-Jimenez ^[1] ; Rafael Toledo-Moreo ^[2]
  1. [1] Universidad Rey Juan Carlos

     Universidad Rey Juan Carlos

     Madrid, España

     
  2. [2] Universidad Politécnica de Cartagena

     Universidad Politécnica de Cartagena

     Cartagena, España

     
• Localización: From Bioinspired Systems and Biomedical Applications to Machine Learning: 8th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2019, Almería, Spain, June 3–7, 2019, Proceedings, Part II / coord. por Hojjat Adeli; José Manuel Ferrández Vicente (dir. congr.), José Ramón Álvarez Sánchez (dir. congr.), Félix de la Paz López (dir. congr.), Francisco Javier Toledo Moreo (dir. congr.), 2019, ISBN 978-3-030-19651-6, págs. 127-136
• Idioma: inglés
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• Resumen

  • Scheduling is one of the classic problems in real-time systems.In real-time adaptive applications, the implementation of somesort of run-time intelligence is required, in order to build real-time intelligent systems capable of operating adequately in dynamic and complex environments. The incorporation of artificial intelligence planning techniques in a real-time architecture allows the on-line reaction to external and internal unexpected events. In this work a layered architecture integrating real-time scheduling and artificial intelligence planning techniques has been designed, in order to implement a real-time scheduler with capability to perform effectively in these scenarios. This multi-levelscheduler has been implemented and evaluated in a simulated information access system destined to broadcast information to mobile users.Results show that incorporation of artificial intelligence to the real-time scheduler improves the performance, adaptiveness and responsiveness of the system.