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

  1. Jesus Fernandez-Conde 1
  2. Pedro Cuenca-Jimenez 1
  3. Rafael Toledo-Moreo 2
  1. 1 Universidad Rey Juan Carlos, Madrid, Spain
  2. 2 Universidad Politécnica de Cartagena, Spain
Book:
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
  1. José Manuel Ferrández Vicente (dir. congr.)
  2. José Ramón Álvarez-Sánchez (dir. congr.)
  3. Félix de la Paz López (dir. congr.)
  4. Javier Toledo Moreo (dir. congr.)
  5. Hojjat Adeli (coord.)

Publisher: Springer Suiza

ISBN: 978-3-030-19651-6

Year of publication: 2019

Pages: 127-136

Type: Book chapter

DIALNET GOOGLE SCHOLAR

Abstract

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.

Data source: Dialnet