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Optimize and scale GCP custom VMs with IBM Turbonomic

By Juan Angel Muñoz posted Wed September 25, 2024 12:35 PM

  

Turbonomic can now generate scale actions for Google Cloud VMs running custom machine types to optimize performance and costs.  

Custom machine types are virtual machines that let users create a machine type with specific hardware resources like CPU, memory, storage, and network bandwidth. This flexibility allows users to tailor the VM to the exact workload requirements, ensuring optimal performance and cost-efficiency. 

 Turbonomic´s primary focus has been always optimizing the performance and cost of IT infrastructure while maintaining compliance. In service of that, it needs to broadly cover utilization metrics on every possible cloud entity and present trustworthy scaling recommendations. 

 This feature enables users to leverage Turbonomic when it comes to scale automatically and continuously GCP custom machine types. Currently, Turbonomic supports N1, N2, N2D, E2 and E2-shared core custom machines types. 

Solving the issue

Custom VMs are created for multiple reasons which make the issue of scaling custom VMs to another custom VM a complex problem with multiples variables and operations to take into consideration. With this feature, Turbonomic enables users to scale custom machines continuously and automatically to the best instance configuration. 

Bringing value

Turbonomic ingests Cloud Monitoring metrics for GCP N1, N2, N2D , E2 and E2-shared core running on custom machine types and present scaling recommendation based on CPU, Memory, IOPS and Throughput utilization. 

Example

A real example is exposed below where Turbonomic recommends to scale up from n2d-custom-8-8448 to e2-custom-16-8704 due to vCPU and vMem congestion. This action intends to optimize performance at the lower possible cost. 

Conversely, a savings recommendation is seen in the following example where Turbonomic recommends to scale down based on underutilized vCPU. By taking this action, user can save $58,35 per months. 


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