- You have three workloads running on one server:
- Online Transactions, your most important workload. This runs only during the day shift.
- Data Mining, which has a medium importance. This is always running, and will consume as much resource as you give it.
- Batch, which is your lowest importance work. Like data mining, it is always running, and will consume as much resource as you give it.
- In this example, the server is divided into two logical partitions
(and both partitions are in the same sysplex):
- Partition 1 runs both the online transactions and the batch work, as they happen to share the same database.
- Partition 2 runs the data mining work.
Figure 1 shows a day shift configuration. As the online transaction workload is the most important, Partition 1 is given a high enough weight to ensure that the online transaction work does not miss its goals due to CPU delay. Within the partition, the existing workload management function is making sure that the online transaction work is meeting its goals before giving any CPU resource to the batch work.
The DASD used by the online transaction work is given enough channel bandwidth to ensure that channel path delays do not cause the work to miss its goals. The channel subsystem I/O priority ensures that online transaction I/O requests are handled first. Even though the batch work is running in Partition 1 (with the increased partition weight and channel bandwidth), the data mining I/O requests will still take precedence over the batch I/O requests if the data mining work is not meeting its goals.
Figure 2 shows the night shift, when there are no more online transactions. If the partition weights had remained the same, then the batch work would be consuming most of the CPU resource, and using most of the I/O bandwidth, even though the more important data mining work may still be missing its goals. LPAR CPU management automatically adjusts to this change in workload, adjusting the partition weights accordingly. Now the data mining work will receive the CPU resource it needs to meet its goals. Similarly, dynamic channel path management will move most of the I/O bandwidth back to the data mining work.
