What's new

You can find out about new features and enhancements in IBM® Z OMEGAMON® AI for Storage® for Release 6.1

December 2025 updates

PTF UJ98715 for APAR OA68606

New User Storage Groups and Dataset Groups views in RDM

New attribute group S3 Device Summary in enhanced 3270UI
With the new Device Summary attributes, you can review summarized information on number of devices for different Device Class.

In enhanced 3270UI, you can access Device Summary attributes by going to LPAR name -> DASD Storage and User Groups -> Device Summary.

For more information, see S3 Device Summary.

New attribute group S3 Device Class Details in enhanced 3270UI
With the new Device Class Details attributes, you can review detailed information on devices for specific Device Class.

In enhanced 3270UI, you can access Device Class Details attributes by going to LPAR name -> DASD Storage and User Groups -> Device Summary -> Device Class Details.

For more information, see S3 Device Class Details.

September 2025 updates

PTF UJ98110 for APAR OA68282

New History mode option in RDM

With the History mode option, now you can review previously collected data for the following attribute groups:

  • DASD Dataset
    • HLQ (Dataset High-level Qualifier)
    • SMS Class (Management Class, Data Class, or Storage Class)
    • Storage Group
    • Catalogs
  • DASD Volumes
    • Storage Groups
    • User Groups

For more information on how to set and use the History mode option, see Realtime Dataset Metrics basic features.

New User Groups for Storage Groups workspace

With the new User Groups for Storage Groups workspace, now you can create custom groups for storage groups and review the summarized data in one place.

You can add storage groups, edit, and delete storage groups from user storage groups by using the corresponding dialog boxes.

The User Groups for Storage Groups workspace is not available in the Tivoli Enterprise Portal.

For more information, see:

New attributes in Catalog Dataset Attributes
The Catalog Dataset Attributes table now also displays the following information:
  • The number of Aliases in the catalog.
  • The tracks, cylinders, and MB allocated for the catalog's dataset.
  • The number of control area splits.
  • The number of control interval splits.

For more information, see Catalog Dataset Attributes.

July 2025 updates

PTF UJ97484 for APAR OA67934

Zowe 3 Conformance

With the APAR OA67934, PTF UJ97484, the Realtime Dataset Metrics (RDM), Zowe-based UI, has been enhanced according to the Zowe 3 Conformance guidelines to increase RDM’s smooth and reliable performance.

For more information, see:
New attribute groups
  • The Catalog Dataset Attributes attribute group provides dataset attributes for catalogs. This data is required for efficient catalog management and setting situations to get alerts.
  • The S3 Volume Event attribute group provides information on when Volume Online and Volume Offline events happen. So, you can set up the situation and get notifications when specific volumes get offline.
IBM Z OMEGAMON Data Requester 1.1.0

IBM Z OMEGAMON Data Requester is a new component of Tivoli Management Services on z/OS 6.3.4 that provides the framework for the Grafana®-based IBM Z OMEGAMON user interface. It includes:

  • Prebuilt dashboards offering enterprise-wide views of IBM® Z OMEGAMON® AI for Storage® data.
  • A plugin to retrieve data from the IBM Z OMEGAMON REST API.
  • A query editor optimized for OMEGAMON, allowing you to build custom Grafana dashboards.

The query editor also supports the migration of existing Enhanced 3270UI (e3270 UI) and Tivoli Enterprise Portal (TEP) dashboards to Grafana, helping you transition their custom views efficiently.

For more information about OMEGAMON Data Requester, see the IBM Z OMEGAMON Data Requester documentation.

January 2025 updates

PTF UJ96553 for APARs OA67240 and OA67241

Added new attributes
Added new parameters
In KS3 parameters, added two new parameters:
  • KS3_TOOLKIT_DELETE_AFTER_DAYS to delete the Storage Toolkit results that were created long ago. This helps to keep the environment clean and prevent the Storage Toolkit related dataset from growing.
  • KS3_ZIIP_OFFLOAD to move some workload from CP to zIIP, which reduces cost for CPU usage.
Updated Procedure for Upgrading to version 6.1
In the procedure for Upgrading to version 6.1, added a step to consider if you move the Realtime Dataset Metrics (RDM) feature from version 5.5.

Release 6.1

The new features and enhancements in IBM® Z OMEGAMON® AI for Storage® for Release 6.1 are the following:

AI Insights support

IBM® Z OMEGAMON® AI for Storage® integrates with IBM® Z OMEGAMON® AI Insights, which allows the users to leverage Machine Learning (ML) capabilities to build custom models on storage data streamed through IBM® Z OMEGAMON® Data Provider. These models unlock the ability to construct dashboards which can be used to proactively avoid issues and improve performance in the context of a user’s environment; example use cases include dataset growth, storage group growth, volume growth, and more. IBM® Z OMEGAMON® AI Insights includes Elastic Kibana dashboards that utilize linear regression algorithms to help users identify derivative rate of change and storage growth deltas.

For more information about IBM® Z OMEGAMON® AI Insights, see Performance indicators of Storage systems and applications.

Unified Storage Toolkit action for all triggered rows

The triggered rows are unified into one Storage Toolkit action to submit only one Job Control Languages (JCL). Earlier, you submitted Storage Toolkit actions for each triggered row. For example, if the situation was true for three rows, Storage Toolkit submits 3 JCLs. Storage Toolkit actions in stations must support )DOT and )ENDDOT constructions to run actions for multi-triggered rows correctly.

For more information about the unified Storage Toolkit actions, see Custom actions in Storage Toolkit.

To send e-mails as Storage Toolkit actions, you can schedule periodic reports of data. To schedule the reports, you must set the required situation and provide JCL.

Storage Toolkit in E3270UI

You can create or modify existing situations by using the Storage Toolkit feature available in E3270UI. Earlier, it was available only in Tivoli Enterprise Portal and Realtime Dataset Metrics.

For more information about using the Storage Toolkit feature in E3270UI, see Storage Toolkit.

Run scheduled collection

With this parameter, you can run Dataset Attribute, DFSMSrmm, or CA1 collections during Agent Start, if the next collection in minutes is less than the specified time. For example, you start Tivoli Enterprise Monitoring Server (TEMS) with Storage Agent and require the Dataset Attribute collection data at your specified time. However, the next collection is in 12 hours. You can start the collection during product start.

For more information about running scheduled collection, see:

Enhanced RDM with Storage Groups, Volumes, and CATALOG data representation

A new workspace is available in Realtime Dataset Metrics (RDM) to display Storage Groups and DASD Volume statistics. You can view the DASD Storage Groups information both from the volume and dataset perspectives.

Another workspace is available to display a summarized CATALOG representation of the data. For example, you can view the total VSAM dataset usage including DATA and INDEX components or review summarized statistics for your GDG datasets.

For more information about the enhancements in RDM, see Realtime Dataset Metrics.

Clusters Summary view

With this view, you can get summarized information of the clusters and access the datasets assigned to a cluster.

For more information about the Clusters Summary view, see Realtime Dataset Metrics basic features.

Improved Performance for data collections

The CPU consumption is reduced for various collection types, such as Dataset Attributes, All Datasets, RDM-related services, Ficon Collection, RLS collection, CA 1 collection, Cache collection, ADABAS collection, and Copy Services functionality.

For example, the CPU usage for the Dataset Attribute collection is reduced by ~10%, All Datasets collection is reduced by ~8%.