Optimizing your personal energy use consists of more than offsetting where you run your DR site. It involves thinking about your DR strategy carefully beyond taking the simplest “let’s just replicate everything” approach. Some of the important questions you need to ask include:
Are there some parts of your application that can withstand a longer recovery time objective (RTO) than others?
Can you make use of Google Cloud storage as part of your DR configuration?
Can you get closer to a cold DR pattern, and thus optimize your personal energy consumption?
The elephant in the room, though, is “What if I absolutely need to have resources when I need them? How do I know the resources will be there when I need them? How will this work if I optimize the design of my DR failover site on Google Cloud such that I have minimal resources running until I need them?”
In this situation, you should look into the ability to reserve Compute Engine zonal resources. This ensures resources are available for your DR workloads when you need them. Using reservations for virtual machines also means you can take advantage of discounting options (which we discuss later in this post).
In summary, using Google Cloud as the target for your failover site can help immediately lower your net carbon emissions, and it’s also important to optimize your DR configuration by asking the right questions and implementing the right pattern. Lastly, if your particular use case permits, consider migrating your on-prem workloads to Google Cloud altogether. This will enable your organization to really move the needle in terms of reducing its carbon footprint as much as possible.
2. Production on Google Cloud, with Google Cloud as the DR site
Running your applications and DR failover site on Google Cloud means there are zero net operational emissions to operate both your production application and the DR configuration.
From here, you want to focus on optimizing the design of your DR failover site on Google Cloud. The most optimal pattern depends on your use case.
For example, a full high availability (HA) configuration, or hot pattern, means you are using all your resources. There are no standby resources idling, and you are using what you need, when you need it, all the time. Alternatively, your RTO may not require a full HA configuration, but you can adopt a warm or cold pattern when you need to scale or spin up resources as needed in the event of a disaster or major event.
Adopting a warm or cold pattern means all or some of the resources needed for DR are not in use until you need them. This may
A simple solution is, like in the previous scenario, to reserve Compute Engine zonal resources for your workloads when you need them. And since you’re running your production on Google Cloud, you can work with your Google Cloud sales representative to forecast your usage and take advantage of committed use discounts. These are where you purchase compute resources (vCPUs, memory, GPUs, and local SSDs) at a discounted price in return for committing to paying for those resources for one or three years. Committed use discounts are ideal for workloads with predictable resource needs.
Taking advantage of committed use discounts enables Google Cloud to use your forecasting to help ensure our data centers are optimized for what you need, when you need it—rather than Google Cloud over-provisioning and essentially running servers that are not optimally used. Sustainability is a balancing act between the power that is being consumed, what sort of power is in use, and the usage of the resources that are being powered by the data centers.