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This paper describes a case-based learning method for acquiring context-dependent user optimization preferences and tradeoffs and using them to incrementally improve schedule quality in predictive scheduling and reactive schedule management in response to unexpected execution events.
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Moreover, the key parameters obtained from HDMR are verified using the reactive scheduling and nominal scheduling approaches.
The paper points out the most relevant aspects of the proposed framework architecture that supports both predictive and reactive scheduling.
Based on this architecture, we present a novel scheduling algorithm (PRS1) that dynamically exploits proactive and reactive scheduling methods, for scheduling real-time, aperiodic, independent tasks.
Then, we propose an energy-efficient reactive scheduling algorithm, namely ERECT, to schedule the real-time tasks and computing resources in virtualized clouds.
Then, reactive scheduling is used to schedule dendritic cell interventions with and without interleukin interventions under the varying conditions of the patient.
Most of the studies that address this issue use some sort of adaptive or reactive scheduling that enables the FMS to better cope with randomness and variability.
Authors propose an object-oriented high-level control structure for real-time error handling, which integrates the new reconfiguration for error handling technology with the existing reactive scheduling system.
How to make a robust and adaptable dynamic reactive scheduling in a computationally efficient manner and be able to assess the quality of the schedule to improve the steel production during SCCP are the key factors for iron and steel manufacturing productivity.
This procedure is known as reactive scheduling.
Completely reactive scheduling no schedule is generated in advance and decisions are made in real time.
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