Ecological Impact of Cloud Computing, by Alexander Taylor



Please find below the updates on Alexander’s research into cloud computing and its implications with the environment.

Update #1:


Most of us don’t think about the digital cloud; by design it’s a convenience that slips into the background. But does this detachment from the physical infrastructure that powers the cloud obfuscate responsibility?

The 8 million data centres scattered around the globe currently account for over 2% of total global demand for electricity, with demand rising exponentially; Cleantechnica estimate that Alibaba Cloud alone produces 1,600,000 metric tons of CO2e debt in a single year. Yet with 30 minutes of Netflix streaming costing the planet 1.6kg of CO2e, is there justification to a call for responsible cloud use from its users?

According to Stanford Magazine, 0.2 tons of Co2 is produced for every 100gb of data that we store in the cloud per year— approximately a million times more energy than what it would take to save it on a local hard drive.

The paper ‘Does Cloud Computing Have A Silver Lining?’ makes the rare point that perhaps we do have a level of responsibility in terms of decreasing our data outputs, offering steps such as the lowering of resolution of files uploaded to the minimum acceptable requirement, actively deleting files that are no longer useful, and ending the duplication of documentation in order to lower our overall cloud footprint.

Update #2:


As well as contributing to the ecological waste generated by cloud computing, our digital leftovers take on a second life as material for the training of datasets, both publicly published (ImageNet) and strictly private (Google's JFT-300M dataset). In 2018, Facebook revealed they have used 3.5 billion publicly posted Instagram photos to train an internal AI system. One side effect of using vernacular human imagery to classify an entire universe is that it prioritizes one scale of existence; a neural network trained on the largest object datasets will recognize a plastic bottle or domestic cat, but not an avocado stone, piece of pollon, or a protozoa.

Rather than mirroring industrial concerns, could we teach our machines to see matter in a way that helps us to see - and exist - differently? What would a data set of everyday objects look like if it was created by a tree, an insect, or a solar system?








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