Jewels SГјdd A little background Video
Nor can we guarantee the accuracy of the rough estimates the tool provides. In the face of a changing climate, we at Etsy are committed to reducing our ecological footprint.
In early , we finished our two-year migration from our own physical servers in a colocated data center to Google Cloud. Our old data centers had an average PUE Power Usage Effectiveness of 1.
PUE is a ratio of the total amount of energy a data center uses to how much energy goes to powering computers. It captures how efficient factors like the building itself and air conditioning are in the data center.
While a lower PUE helps our energy footprint significantly, we need to be able to measure and optimize the amount of power that our servers draw.
Knowing how much energy each of our workloads uses helps us make design and code decisions that optimize for sustainability.
The Google Cloud team has been a terrific partner to us throughout our migration, but they are unable to provide us with data about our cloud energy consumption.
This is a challenge across the industry: neither Amazon Web Services nor Microsoft Azure provide this information to customers.
We thought about how we might be able to estimate our energy consumption in Google Cloud using the data we do have: Google provides us with usage data that shows us how many virtual CPU Central Processing Unit seconds we used, how much memory we requested for our servers, how many terabytes of data we have stored for how long, and how much networking traffic we were responsible for.
Our supposition was that if we could come up with general estimates for how many watt-hours Wh compute, storage and networking draw in a cloud environment, particularly based on public information, then we could apply those coefficients to our usage data to get at least a rough estimate of our cloud computing energy impact.
We are calling this set of estimated conversion factors Cloud Jewels. Other cloud computing consumers can look at this and see how it might work with their own energy usage across providers and usage data.
The goal is to help cloud users across the industry to help refine our estimates, and ultimately help us encourage cloud providers to empower their customers with more accurate cloud energy consumption data.
The sources that most influenced our methodology were the U. Data Center Energy Usage Report , The Data Center as a Computer , and the SPEC power report.
We also spoke with industry experts Arman Shehabi , Jon Koomey , and Jon Taylor , who suggested additional resources and reviewed our methodology.
Using the resources we found online, we were able to determine what we think are reasonable, conservative estimates for the amount of energy that compute and storage tasks consume.
We are aiming for a conservative over-estimate of energy consumed to make sure we are holding ourselves fully accountable for our computing footprint.
We have yet to determine a reasonable way to estimate the impact of RAM or network usage, but we welcome contributions to this work! We are open-sourcing a script for others to apply these coefficients to their usage data, and the full methodology is detailed in our repository on Github.
The following coefficients are our estimates for how many watt-hours Wh it takes to run a virtual server and how many watt-hours Wh it takes to store a terabyte of data on HDD hard disk drive or SSD solid-state drive disks in a cloud computing environment:.
As you may note: we are using point estimates without confidence intervals. This is partly intentional and highlights the experimental nature of this work.
Our sources also provide single, rough estimates without confidence intervals, so we decided against numerically estimating our confidence so as to not provide false precision.
Our work has been reviewed by several industry experts and our energy and carbon metrics for cloud computing have been assured by PricewaterhouseCoopers LLP.
That said, we acknowledge that this estimation methodology is only a first step in giving us visibility into the ecological impacts of our cloud computing usage, which may evolve as our understanding improves.
Whenever there has been a choice, we have erred on the side of conservative estimates, taking responsibility for more energy consumption than we are likely using to avoid overestimating our savings.
While we have limited data, we are using these estimates as a jumping-off point and carrying forth in order to push ourselves and the industry forward.
We especially welcome contributions and opinions. Let the conversation begin! At a high level, to estimate server wattage, we used a general formula for calculating server energy use over time:.
To estimate storage wattage, we used industry-wide estimates from the U. Data Center Usage Report. That report contains estimated average capacity of disks as well as average disk wattage.
We used both those estimates to get an estimated wattage per terabyte. The resources we found related to networking energy estimates were for general internet data transfer, as opposed to intra data center traffic between connected servers.
Networking also made up a significantly smaller portion of our overall usage cost, so we are assuming it requires less energy than compute and storage.
Finally, as far as the research we found indicated, the energy attributable to networking is generally far smaller than that attributable to compute and storage.
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