Speaker
Description
As increasingly powerful yet power-hungry AI models are developed and cloud computing adoption grows, the environmental impact of the Information Technology sector continues to rise. Data centers currently consume more than 400TWh of electricity annually and thus contribute to a globally concerning carbon footprint.
To meet the rising electricity demand and to reduce their emissions, many data centers increasingly rely on renewable power generation. Precise carbon intensity estimations are therefore required to enable emissions-aware scheduling techniques that align workload execution with green energy availability.
Despite their potential, these approaches are hindered by the general lack of transparency around the power consumption and emissions of cloud workloads. These metrics are rarely disclosed, keeping users uninformed about the sustainability decisions governing their computational resources.
This work proposes an architecture for minimizing the carbon footprint of workloads in hybrid cloud environments. A carbon intensity and power production forecaster was developed after benchmarking state-of-the-art time series models. These forecasts are integrated into a carbon-aware scheduling model that jointly minimizes emissions and maximizes local cluster usage. An accountant component was also implemented to assess the system accountability through provenance-based descriptions of the emission-aware management of workloads.
We evaluated the system by simulating a hybrid cloud infrastructure comprising a private cluster with on-site renewable power generation and public cloud services located across regions with different carbon intensity profiles. Extensive benchmarks validated the forecaster component over historical data from ElectricityMaps and compared various configurations of the scheduler model, where reductions in emissions of up to 27% were observed against a carbon-agnostic baseline. We present an implementation of the provenance-based accountant component in Kubernetes. A testbed over the EGI infrastructure will be developed in the context of the recently approved HE ENSURE project to validate the proposed system in a real setting under realistic operational conditions.