Distributed Scheduling¶

A new scheduling process type has been introduced to dynamically handle large volumes of data that can be scheduled over a long period of time.

This new process type - known as 'Distributed Scheduling' - involves setting up multiple child datasets, each offering a user defined filtered view on the full scheduling window. One of these will be a 'dynamic' child dataset, which will handle updates, appointment requests etc. in a very similar way to a standard dynamic schedule. The others will be static schedules which can cover a much longer period of time and will only be updated occasionally. These would typically be used for capacity planning purposes.

The full input data is always loaded into a single 'parent' dataset, and all input data changes are also applied to this dataset (though plans are only saved for the child datasets). This means that there is no need for any external system to judge which activities should be scheduled dynamically and which should not. Instead, this is all handled internally by the Schedule Distribution Service, which is responsible for maintaining and updating the child datasets. If required, the Schedule Distribution Service can further subdivide the schedule into smaller segment datasets for the DSE(s) to process, to ensure that the entire scheduling problem is processed efficiently.

For full details of this functionality see the Architecture and Sizing Guide.