  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.
