SHIELD：面向碳排放、废水与能耗感知的数据中心管理的可持续混合进化学习框架
SHIELD: Sustainable Hybrid Evolutionary Learning Framework for Carbon, Wastewater, and Energy-Aware Data Center Management
AI总结：
针对地理分布式数据中心碳排放、水足迹高及能源成本上升的问题，提出融合机器学习引导局部搜索与分解进化算法的SHIELD框架，协同优化多项目标，在效率和优化效果上均显著优于现有方法。
摘要：
当今的云数据中心通常采用地理分布式部署以提供可靠的数据服务，但这类地理分布式数据中心（GDDCs）因碳排放和用水量不断增加，带来了显著的环境影响，亟需加以遏制。此外，运营这些数据中心的能源成本也在持续上升。本文提出了一种新型框架，用于协同优化GDDCs的碳排放、水足迹和能源成本，该框架名为SHIELD（可持续混合进化学习框架），是一种混合工作负载管理框架，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。我们的框架考虑了发电/用电、成本以及环境影响方面的地理因素和时间差异，以智能管理GDDCs之间的工作负载分配和数据中心运营。实验结果表明，与现有最优方法相比，SHIELD可实现34.4倍的加速，帕累托超体积提升2.1倍，同时碳足迹最多降低3.7倍，水足迹最多降低1.8倍，能源成本最多降低1.3倍，所有目标（碳、水、成本）的累计提升最高可达4.8倍。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
