The Water Footprint of Data Centers

The internet and associated Information and Communications Technologies (ICT) are diffusing at an astounding pace. As data centers (DCs) proliferate to accommodate this rising demand, their environmental impacts grow too. While the energy efficiency of DCs has been researched extensively, their water footprint (WF) has so far received little to no attention. This article conducts a preliminary WF accounting for cooling and energy consumption in DCs. The WF of DCs is estimated to be between 1047 and 151,061 m3/TJ. Outbound DC data traffic generates a WF of 1–205 liters per gigabyte (roughly equal to the WF of 1 kg of tomatos at the higher end). It is found that, typically, energy consumption constitues by far the greatest share of DC WF, but the level of uncertainty associated with the WF of different energy sources used by DCs makes a comprehensive assessment of DCs’ water use efficiency very challenging. Much better understanding of DC WF is urgently needed if a meaningful evaluation of this rapidly spreading service technology is to be gleaned and response measures are to be put into effect

Optimizing Water Efficiency in Distributed Data Centers

Abstract—The number and scale of data centers explode with the dramatically surging demand for cloud computing services, resulting in huge electricity consumption as well as an enormous impact on sustainability. While numerous efforts have been dedicated to decreasing the carbon footprint of data centers, there is a surprising and also embarrassing lack of attention to the enormity of data center water consumption despite its emergence as a critical concern for future sustain-ability. In this paper, we take the first step towards the data center water efficiency. We first identify two characteristics of data center water efficiency: water efficiency varies by location and also over time. Then, by exploiting these characteristics, we propose a novel geographical load balancing (GLB) algorithm, called GLB for Water Sustainability (GLB-WS), which dynam-ically schedules workloads to water-efficient data centers for improving the overall water usage effectiveness (an emerging metric for quantifying data center water efficiency) while satisfying the electricity cost constraint. We also perform a trace-based simulation study to validate the analysis. The result shows that compared to the state-of-the-art cost-minimizing GLB, GLB-WS significantly improves the water efficiency (by 60%) and reduces the water consumption (by 51%). Keywords-Data center, Geographic load balancing, Sustain-ability, Water efficienc