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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. Its really time that cloud providers offer measurements.
Any mobile phone can tell me which app uses batterie!! This includes Browsing activities, sending mails, handover to fulfillment systems.
I am happy to see your thoughts around key metrics for sustainability. Schneider Electric has been active for many years in The Green Grid Association and in international standards organizations like JTC1 SC39 to create measurement methodologies for data center and IT metrics.
Your dashboard is quite good, given the imperfect data that you get from your cloud suppliers. I welcome the opportunity to collaborate on advancing and improving your metrics.
Etsy — Thanks for make a push to estimate energy intensity from public cloud deployments. With the lack of transparency, this is an important exercise that many companies will need to start doing.
We too are wrestling with it and will review your methodologies for ideas. Looking forward to getting back in touch to collaborate!
Thanks for sharing this great work. Which is indeed the reverse of hard drives where most energy is used by the spinning motor about 5W once spun up AFAIK , the rest head step motor, embedded controler being far less than 1W.
Memory seems much more complicated. It seems the max power is about 0. Any idea welcome. Code as Craft Speaker Series Events About Archive.
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