In yet another step toward running its operations entirely on carbon-free energy sources by 2030, Google is now deploying machine learning technology that will help automatically shift workloads between data centers, depending on the availability of renewable energy resources, which can vary by type, location or the time of day. The move is part of Google's plan to transition to what it calls Carbon-Intelligent Compute Management, a system that will use artificial intelligence to automatically maximize clean electricity use across their data centers -- and therefore minimize the carbon footprint and operational costs. The system functions by delaying non-urgent workloads that aren't time-sensitive, such as encoding and analyzing videos that are uploaded to YouTube, or processing images that are uploaded to Google Photos and Drive. The company says that these "temporally flexible" tasks will still be completed within 24 hours, while critical production tasks and user-facing services that need to run around the clock -- such as Search, Maps, YouTube and cloud customers' workloads running in allocated Virtual Machines (VMs) -- will not be changed by the new system. "Workloads are comprised of compute jobs," explained the team of Google engineers in their recent paper on the new platform.