In recent years, cloud service providers have been building and hosting datacenters across multiple geographical locations to provide robust services. However, the geographical distribution of datacenters introduces growing pressure to both local and global environments, particularly when it comes to water usage and carbon emissions. Unfortunately, efforts to reduce the environmental impact of such datacenters often lead to an increase in the cost of datacenter operations. To co-optimize the energy cost, carbon emissions, and water footprint of datacenter operation from a global perspective, we propose a novel framework for multi-objective sustainable datacenter management (MOSAIC) that integrates adaptive local search with a collaborative decomposition-based evolutionary algorithm to intelligently manage geographical workload distribution and datacenter operations. Our framework sustainably allocates workloads to datacenters while taking into account multiple geography- and time-based factors including renewable energy sources, variable energy costs, power usage efficiency, carbon factors, and water intensity in energy. Our experimental results show that, compared to the best-known prior work frameworks, MOSAIC can achieve 27.45x speedup and 1.53x improvement in Pareto Hypervolume while reducing the carbon footprint by up to 1.33x, water footprint by up to 3.09x, and energy costs by up to 1.40x. In the simultaneous three-objective co-optimization scenario, MOSAIC achieves a cumulative improvement across all objectives (carbon, water, cost) of up to 4.61x compared to the state-of-the-arts.

翻译：近年来，云服务提供商在全球多个地理位置建设和托管数据中心，以提供稳健的服务。然而，数据中心的分布式部署对本地及全球环境带来了日益增长的压力，尤其是在水资源消耗与碳排放方面。遗憾的是，减少此类数据中心环境影响的努力往往会导致运营成本上升。为在全球视角下协同优化数据中心的能源成本、碳排放与水足迹，我们提出了一种新型多目标可持续数据中心管理框架MOSAIC，该框架将自适应局部搜索与基于协作分解的进化算法相结合，以智能管理地理工作负载分布与数据中心运营。我们的框架在可持续分配工作负载至数据中心时，综合考虑了地理与时间多维度因素，包括可再生能源、可变能源成本、电能使用效率、碳因子及能源水强度。实验结果表明，与已知最优的先验工作框架相比，MOSAIC实现了27.45倍的加速比及1.53倍的帕累托超体积改进，同时将碳足迹降低至多1.33倍、水足迹降低至多3.09倍、能源成本降低至多1.40倍。在三目标同步协同优化场景中，MOSAIC相比现有最先进方案，在所有目标（碳、水、成本）上实现了至多4.61倍的累积提升。