Today we’re going to look at an interesting trend we are seeing toward the use of custom machine types in Google Cloud Platform. One of the interesting byproducts of managing the ParkMyCloud platform is that we get to see changes and trends in cloud usage in real time. Since we’re directly at the customer level, we can often see these changes before they are spotted by official cloud industry commentators. They start off as small signals in the noise, but practice has allowed us to see when something is shifting and a trend is emerging – as is the case with these custom machine types.
Over the last year, the shift to greater use of Custom Machine Types (launched in 2016 on Google Compute Engine) and to a lesser extent Optimized EC2 instances (launched in 2018 on AWS) are just such a signal that we have observed growing in strength. Interestingly, Microsoft has yet to offer their equivalent version on Azure.
What do GCE custom machine types let you do?
Custom machine types let you build a bespoke instance to match the specific needs of your workload. Many workloads can be matched to off-the-shelf instance types, but there are many workloads for which it is now possible to build a better matching machine which delivers a more cost effective price. The growth in adoption of this particular instance type supports the case and likely benefits of their availability.
So what are the benefits of these new customized machines? First, they provide a granular level of control to match the needs of your specific application workloads. In practice, this leads to compromise as you select the closest instance type to your optimal configuration. Such compromises typically lead to over-provisioning, a situation we see across the board among our customer base. We analyzed usage of the instances in our platform this summer, and found that across all the instances in our platform, the average vCPU utilization was less than 10%!
Secondly, they allow you to finely tune your machine to maximize the cost effectiveness of your infrastructure. Google claims savings of up to 50% when utilizing their customized options compared to traditional predefined instances, which we believe to be a reasonable assessment as we see the standard instance types are often massively overprovisioned.
On GCE, the variables that you can configure include:
- Quantity and type of vCPU’s;
- Quantity and type of GPU;
- Memory size (albeit there are some limits on the maximum per vCPU).
On AWS, customized options are currently more limited and include only the number and type of vCPU’s, and the options are focused on per-core licensed software problems, rather than cost optimization. It will be interesting if they follow Google and open up cost-based customization options in the coming months, and allow the effective unbundling of fixed off-the-shelf instance types.
Should you use custom machine types?
So just because customization is an option, is this something you should actually pursue? In fact, you will pay a small premium compared to the size of standard instances/VMs, albeit you can optimize for specific workloads, which oftentimes will mean an overall lower cost. To make such an assessment will require that you examine your applications resource use and performance requirements. Such determinations require that you carefully analyze your infrastructure utilization data. This quickly gets complex, although there are a number of platforms which can support thorough analytics and data visualization. Ideally, such analytics would be combined with the ability to recommend specific cost-effective customized instance configurations as well as automate their provisioning.
Watch this space for more news on custom machine types!