In VMTurbo’s new 4.5 Release, we have introduced a new dashboard that is designed to unify a diverse set of infrastructure elements and technologies under a single topology for IT Operations:
Following in the footsteps of earlier versions to simplify the operation of complex virtualized environments, the goal of the new view is twofold:
First, map the relationships between inter-related components at all levels of the IT stack for easily navigated management:
- Hypervisor – VMWare, XenServer, RHEV, Hyper-V
- Cloud Management – vCloud Director, Cloudstack, VMM
- Converged Fabric – Cisco UCS
- Storage Fabric – NetApp
- Application – Windows, Java, Linux
- Public Cloud – AWS, Azure
And second, using VMTurbo’s Economic Scheduling Engine: define, drive, and control each relationship on a macro-scale to bring the entire environment into an optimal state. By leveraging this view, users will see that VMTurbo:
- Agentlessly discovers and maps objects across each level of the supply chain view
- Pre-ranks and prioritizes those areas at risk
- Determines the exact executable decisions to maintain health across the supply chain at each level of view (Virtual Machine, Physical Machine, Array, Etc…)
The view itself is extremely useful, but the real magic operating underneath the hood is VMTurbo’s common data model.
Because the software is built through an abstraction model that is purpose-built to drive and control the macro-level health across these relationships, every action suggested at each level of view understands the impact and outcome across other entities. Let’s use an example to illustrate:
Consider a NetApp architecture with multiple aggregates serving many volumes which, in turn, are serving a variety of mission-critical workloads.
If a user clicks on Aggregate 1 within VMTurbo’s supply chain view and sees that IOPs is constrained at 95%, then a very complex scenario ensues across this single channel of our supply chain. In this case, it would logically make no sense to move workloads across volumes within the aggregate as our constraint is further down the stack and they are sharing performance.
- VMTurbo observes this issue from the workloads perspective, all the way down to the aggregate itself, and then determines the necessary actions for the whole supply chain. The user can quickly tab through the volumes hosted on the aggregate, and then select the virtual machines consuming from this layer to see all related objects and risks, as well as the actions to take.
Without any manual analysis of the data, VMTurbo suggests the right volume to move to a lower-risk aggregate, analyzing the destination aggregate and volume/workload demands, and then allowing the user to execute the action through the interface.
As far as other risks within the supply chain, VMTurbo also analyzes other elements in this way to determine actions that need to be made to the storage controllers, VMs, Volumes and more.
All of these actions would be pre-ranked within an operational to-do list, allowing the user to select their comfort level for manual en-action or automation:
The topology view embodies VMTurbo’s mission to transform IT management from a mode of viewing infrastructure elements through a linear and manual approach of statistical analysis, to one where software can drive and control environments in a continuous state of health. By leveraging this view, users can expect to use VMTurbo’s analysis in an even more fluid way, consistent with how operations views its infrastructure topology to eliminate risks across their architectures.