Sirui Qi, Hayden Moore, Dejan Milojičić, Cullen Bash, Sudeep Pasricha
For over a decade, the problem of distributed cloud workload management has been studied with the goal of co-optimizing operational costs with other metrics, such as energy efficiency, using multi-objective optimization algorithms. However, there is a lack of a multi-objective algorithm that can not only provide a diverse and high-quality Pareto-optimal solution set, but also scale to different cloud management scenarios. The heterogeneity of cloud workloads and geo-distributed datacenters introduces complex operational scenarios over time and geographic locations. The introduction of emerging sustainability objectives, such as carbon emissions and wastewater generation, further aggravates the cloud management challenge. Moreover, inter-datacenter network costs must also be considered during cloud workload management to prevent unrealistic workload migration scenarios. In this article, we propose a novel cloud resource management framework calledSHIELD-EBto co-optimize operational costs, operational carbon emissions, and wastewater generation in a geo-distributed cloud datacenter platform. To generate a diverse solution set with different tradeoffs,SHIELD-EBintegrates a customized evolutionary strategy (CES) with eXtreme Gradient Boosting (XGB). Experimental results show thatSHIELD-EBcan achieve improvements of up to 33% in Pareto hypervolume, 7.1% ($0.6 M) in operational costs, 11.6% (7.6 tons) in operational carbon, and 12.0% (102.4 tons) in water usage compared to the state-of-the-art over a duration of 120 hours.