Shared Storage Analysis Card
Organizing and optimizing storage just got easier
With today’s complex data center topologies containing numerous datastores and vCenters, the ability to correlate datastores, hardware vendors, models, capacity and performance for all VM’s and hosts can be a daunting task. That task goes beyond human abilities and requires a data science platform when correlations and changes must be analyzed over time to understand and to predict trends.
Count, Summarize, and Correlate Shared Storage Details
The HPE CloudPhysics Shared Storage Analysis card quickly summarizes all datastores currently deployed in your environment based on storage types, vendors, models, and performance. Visibility into these values can assist you to:
- Find datastores by hardware Vendor and Model
- Find performance bottlenecks by datastore and associated hardware
- Identify inefficiencies in hardware that can be upgraded or replaced
- Identify capacity and usage trends by datastore
- Use non-disruptive, patented workload tracing to simulate workloads on alternative storage platforms, and to conduct POCs remotely, eliminating on-premise POC work and systems
The Shared Storage Analysis card can also quickly quantify storage utilization and performance for each datastore to focus on hosts and VM’s generating the greatest impact on the storage subsystem. These same metrics can be used to scope and scale hardware upgrades and replacements within your environment. Take the data further and use the capacity output to plan placement or storage migration plans for new workloads based on over-commits, snapshots, and thin/thick provisioned capacity
Plan Your Storage Upgrades
When trying to identify datastores experiencing the greatest performance issues, this card will help you quickly identify the datastores with the greatest contention, lowest performance, or most activity. Use these results to identify read/write ratios, hardware vendors, and hardware models to quickly determine which resources should be upgraded and the potential impact on your data center.