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.

翻译：摘要：当今的云数据中心通常分布在不同地理位置，以提供稳健的数据服务。然而，这些地理分布式数据中心（GDDCs）因日益增加的碳排放和水资源消耗而对环境产生显著影响，亟需加以控制。此外，运营这些数据中心的能源成本持续上升。本文提出了一种新型框架，通过名为SHIELD的混合工作负载管理框架，协同优化GDDCs的碳排放、水足迹和能源成本。该框架将机器学习引导的局部搜索与基于分解的进化算法相结合，综合考虑地理因素及发电/用电的时间差异、成本与环境影响，智能化管理工作负载在GDDCs间的分配及数据中心运营。实验结果表明，与现有最优方法相比，SHIELD在Pareto超体积上实现了34.4倍的加速和2.1倍的提升，同时碳排放降低高达3.7倍，水足迹降低高达1.8倍，能源成本降低高达1.3倍，所有目标（碳、水、成本）的综合改进高达4.8倍。