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Erschienen in:
2019 | OriginalPaper | Buchkapitel
Improving Scheduling Performance of a Real-Time System by Incorporation of an Artificial Intelligence Planner
verfasst von : Jesus Fernandez-Conde, Pedro Cuenca-Jimenez, Rafael Toledo-Moreo
Erschienen in: From Bioinspired Systems and Biomedical Applications to Machine Learning
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Abstract
Scheduling is one of the classic problems in real-time systems. In real-time adaptive applications, the implementation of some sort 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-level scheduler 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.