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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.
Since we do not yet have a coefficient for networking or RAM that we feel confident in, we are leaving that data out for now.
The experts we have consulted with are confident that our coefficients are conservative enough to account for our overall energy consumption without separate consideration for networking and RAM.
Applying our Cloud Jewels coefficients to our aggregated usage data and comparing the estimates to our former local data center actual kWh totals over the past two years indicates that our energy footprint in Google Cloud is smaller than it was on premises.
However, overall, relatively speaking over time assuming our estimates are even moderately close to accurate and verified to be conservative , we are on track to be using less overall energy to do more computing than we were two years ago as our business has grown , meaning we are making progress towards our energy intensity reduction goal.
We would next like to find ways to estimate the energy cost of network traffic and memory. There are also minor refinements we could make to our current estimates, though we want to ensure that further detail does not lead to false precision, that we do not overcomplicate the methodology, and that the work we publish is as generally applicable and useful to other companies as possible.
Part of our reasoning for open-sourcing this work is selfish: we want input! We welcome contributions to our estimates and additional resources that we should be using to refine them.
We hope that publishing these coefficients will help other companies who use cloud computing providers estimate their energy footprint.
And finally we hope that efforts and estimations encourage more public information about cloud energy usage, and particularly help cloud providers find ways to determine and deliver data like this, either as broad coefficients for estimation or actual energy usage metrics collected from their internal monitoring.
Very nice approach to make your efford transparent. We are running online shops and went into very similiar calculations.
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