Content

Highlights

New platform: IBM Storage Virtualize

SVC was the first platform supported by BVQ°. With the introduction of Policy-Based High Availability (PBHA) and Policy-Based Replication (PBR) methods, along with the accompanying FlashSystem Grid architecture, IBM has ushered in a new era for Storage Virtualize. But these concepts bring entirely new requirements that push the existing SVC platform in BVQ° to its limits.
Adapting the existing SVC object model in BVQ° - which has evolved over many years - would have been highly complex and error-prone. Therefore, we saw this moment as an opportunity to eliminate legacy burdens and give the platform a fresh start as “IBM Storage Virtualize ('StorV')”.

Key innovations include:

• Multi-System scanning: Multiple clusters can be captured simultaneously (aligned with FlashSystem Grid environments)

• Monitoring for PBHA & PBR including performance metrics

• Integration of new features since Storage Virtualize Code 8.6.x (e.g. volume groups, policies, new performance metrics)

• Optimized object model: Outdated objects and attributes have been removed and terminology has been adapted to the current IBM Storage Virtualize world

• Focus on future technologies: Legacy replication methods such as Global Mirror and Metro Mirror are no longer supported

What does this mean for BVQ° users?

• No further development of the original SVC platform: It will remain for legacy environments but will not be enhanced.

• Recommendation: New Storage Virtualize systems (SVC, FlashSystem) in your environment should now be added using the new StorV scanner in BVQ°.

Please see Storage layer for further information

Migration to MongoDB 8

The latest production ready release of MongoDB delivers several new features and improvements that will benefit the overall runtime performance of BVQ:

• Read throughput overall has been improved

• Bulk writes are faster and less resource consuming

• WiredTiger WAL less likely to grow under load

• Resource usage has been optimized

• Ready for AI with support for Vector Search

Performance data stored in time series collections

Performance data is now stored in MongoDB's time series collections. This will take pressure from the WiredTiger storage engine and scales better under load, resulting in more consistent and faster performance-persist times, depending on the scanned platform.
The process of migrating existing data is done entirely in the background and is designed to impact normal operations as little as possible.
The migration status can be monitored live at Support > Migration

In a nut-shell:

• New collections and indexes need less overall space, better compression

• Performance expiration is handled by the DB

• Performance persist creates way less stress on the storage engine

• Existing data is available during migration

• Expert GUI performance will be slightly impacted during migration

• Restarting BVQ server during migration is possible, but not advised

Reporting: Custom grouping

BVQ° Reporting now supports grouping for both table and line chart snippets.
With this new feature, you can combine objects dynamically based on any attribute or performance indicator - unlocking a whole new level of analytical depth and clarity.
Custom grouping enables you to:

• Discover new insights by aggregating related objects and adding meaningful context to your data

• Generate KPIs automatically and compare units more effectively, such as I/O load per storage tier or workload per business unit

• Enhance multi-tenant and multi-cluster reporting by cleanly separating and analyzing metrics across tenants, regions, or clusters

• Improve visibility and reduce noise by structuring information and focusing on what truly matters

Custom grouping makes BVQ° Reporting more powerful, flexible, and efficient - especially in complex environments.

More information how to use the feature can be found in the manual: Global elements > Selection path > Custom grouping

Requirements and restrictions