Turbonomic & HPE OneView Integration
Turbonomic and HPE enable customers to accelerate their transition to hybrid cloud infrastructure while providing the application performance end-users demand. Turbonomic provides intelligent placement, sizing, and provisioning decisions across the elastic infrastructure HPE OneView exposes, empowering customers with three key benefits.
Let’s take a closer look at the integration. Turbonomic attaches to HPE OneView and imports all physical and logical entities and templates. For example, blades, enclosures, storage volumes, and interconnects.
Turbonomic also attaches to vCenter, system center, or open stack and imports all relevant entities and maps them to HPE OneView topology. For example, VMs to specific blades, and data storage to storage volumes.
Turbonomic then initiates resource utilization polling and periodic topology updates. It continuously satisfies workload demand with real-time decisions. For example, migrate a VM, provision a blade, or resize a volume.
Now let’s see a sample environment. In this elastic infrastructure, we have five servers occupying 5 of the 16 bays, 2 server profiles and 3 storage pools. We will focus in on the server in Bay 1 with the ESX3 server profile, which is currently turned off, and the server in Bay 2 with the ESX1 profile that is currently turned on. Let’s take a look at one of the three-par storage pools. FC_r5 is currently used by 10 volumes with 242 allocated gigabytes.
Moving to the Turbonomic supply chain view, we see that Turbonomic attaches to HPE OneView and vCenter and abstracts all relevant entities across the environment with the goal of ensuring applications get the resources they need to perform. We can see the same server in Bay 2 with the relevant interconnects and all VMs currently residing on and consuming resources from this physical machine. Taking a look at the storage, you can see 1 of the 10 data stores that are currently mapped to the FC_r5 storage pool.
Moving up to the virtualization layer, Turbonomic understands the real-time resource consumption of the VMs from the underlying physical machine and shared storage, as well as the application guest load consuming resources from the VM.
Now let’s focus in on one of the guest loads for the cloud payroll application. In this case, we see degraded and unpredictable QoS driven by memory and storage congestion. To deliver a predictable quality of service, Turbonomic makes the decision to provision a new physical machine, increase the shared storage, and migrate the VMs to prevent memory and storage congestion. The user can action these decisions to provision a new blade by simply clicking Apply. Turbonomic feeds this decision into HPE OneView. As you can see, the blade in Bay 1 has been turned on.
Now, to prevent storage congestion, Turbonomic makes the decision to increase the storage amount on two of the data storage mapped to the FC_r5 storage pool. The allocated storage amount on FC_r5 has been increased to 376 gigabytes and the two volumes mapped to the data stores are now sized appropriately, at just over 129 gigabytes each. With the HPE elastic infrastructure properly sized and provisioned, Turbonomic makes the decision to move VMs to the new blade to prevent memory and storage congestion. These decisions are actioned from Turbonomic and executed through vCenter.
Taking a look again at the cloud payroll application, the response time and transactions per minute have been improved to the predictable levels end users demand, and the Tomcat server is no longer experiencing critical memory congestion or storage latency.
In conclusion, Turbonomic and HPE OneView enable customers to guarantee the performance of any application, accelerate the transition to a hybrid-cloud infrastructure, and deliver cloud-like economics on premises.
To learn more, visit https://turbonomic.com/hpe