Adaptive job scheduling using neural network priority functions

Details Adaptive job scheduling using neural network priority functions Adaptive job scheduling using neural network priority functions

1993-10-08

1995-08-15

MacDonald, Allen R.

Railway wheels and axles

Axle

Mine car

295 24, 295650, 295700, G06F 1518

Patent

active

054427304

ABSTRACT:
A job scheduler makes decisions concerning the order and frequency of access to a resource according to a substantially optimum delay cost function. The delay cost function is a single value function of one or more inputs, where at least one of the inputs is a delay time which increases as a job waits for service. The job scheduler is preferably used by a multi-user computer operating system to schedule jobs of different classes. The delay cost functions are preferably implemented by neural networks. The user specifies desired performance objectives for each job class. The computer system runs for a specified period of time, collecting data on system performance. The differences between the actual and desired performance objectives are computed, and used to adaptively train the neural network. The process repeats until the delay cost functions stabilize near optimum value. However, if the system configuration, workload, or desired performance objectives change, the neural network will again start to adapt.

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