SHIELD: Sustainable Hybrid Evolutionary Learning Framework for Carbon, Wastewater, and Energy-Aware Data Center Management

TL;DR

This paper introduces SHIELD, a hybrid framework that optimizes workload distribution in geo-distributed data centers to reduce environmental impact and costs, demonstrating significant improvements over existing methods.

Contribution

The paper presents a novel hybrid workload management framework combining machine learning and evolutionary algorithms for sustainable data center operation.

Findings

Achieves 34.4x speedup in optimization process.

Improves Pareto Hypervolume by 2.1x.

Reduces carbon footprint by up to 3.7x.

Abstract

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.