Qi, Sirui, authorMilojicic, Dejan, authorBash, Cullen, authorPasricha, Sudeep, authorACM, publisher2024-11-112024-11-112024-05-09Sirui Qi, Dejan Milojicic, Cullen Bash, and Sudeep Pasricha. 2023. SHIELD: Sustainable Hybrid Evolutionary Learning Framework for Carbon, Wastewater, and Energy-Aware Data Center Management. In THE 14th international Green and Sustainable Computing Conference (IGSC '23), October 28–29, 2023, Toronto, ON, Canada. ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/3634769.3634810https://hdl.handle.net/10217/239525Today'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 34.4× speedup and 2.1× improvement in Pareto Hypervolume while reducing the carbon footprint by up to 3.7×, water footprint by up to 1.8×, energy costs by up to 1.3×, and a cumulative improvement across all objectives (carbon, water, cost) of up to 4.8× compared to the state-of-the-art.born digitalarticleseng© Sirui Qi, et al. | ACM 2023. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in IGSC '23. https://dx.doi.org/10.1145/3634769.3634810.geo-distributed data centerscarbon emissionswastewatermachine learningevolutionary algorithmsTexthttps://doi.org/10.1145/3634769.3634810