SHIELD: Sustainable Hybrid Evolutionary Learning Framework for Carbon,
  Wastewater, and Energy-Aware Data Center Management
Sirui Qi Dejan Milojicic Cullen Bash Sudeep Pasricha
Today's cloud data centers are often distributed geographically to provide
robust data services. But these geo-distributed data centers (GDDCs) have a
significant associated environmental impact due to their increasing carbon
emissions and water usage, which needs to be curtailed. Moreover, the energy
costs of operating these data centers continue to rise. This paper proposes a
novel framework to co-optimize carbon emissions, water footprint, and energy
costs of GDDCs, using a hybrid workload management framework called SHIELD that
integrates machine learning guided local search with a decomposition-based
evolutionary algorithm. Our framework considers geographical factors and
time-based differences in power generation/use, costs, and environmental
impacts to intelligently manage workload distribution across GDDCs and data
center operation. Experimental results show that SHIELD can realize 34.4x
speedup and 2.1x improvement in Pareto Hypervolume while reducing the carbon
footprint by up to 3.7x, water footprint by up to 1.8x, energy costs by up to
1.3x, and a cumulative improvement across all objectives (carbon, water, cost)
of up to 4.8x compared to the state-of-the-art.