Rising computational demands have made cloud datacenters' water footprint a critical concern. We demonstrate how different water footprint accounting methodologies - incorporating operational, manufacturing, and decommissioning water consumption, impact measurements and highlight the need for methodology standardization for water-aware operations. Our analysis reveals opportunities for water-aware scheduling in datacenters by considering regional water variations and lifecycle impacts.

The carbon and water footprint of large-scale computing systems poses serious environmental sustainability risks. In this study, we discover that, unfortunately, carbon and water sustainability are at odds with each other - and, optimizing one alone hurts the other. Toward that goal, we introduce, WaterWise, a novel job scheduler for parallel workloads that intelligently co-optimizes carbon and water footprint to improve the sustainability of geographically distributed data centers.

High-performance computing (HPC) systems are becoming increasingly water-intensive due to their reliance on water-based cooling and the energy used in power generation. However, the water footprint of HPC remains relatively underexplored—especially in contrast to the growing focus on carbon emissions. In this paper, we present ThirstyFLOPS - a comprehensive water footprint analysis framework for HPC systems. Our approach incorporates region-specific metrics, including Water Usage Effectiveness, Power Usage Effectiveness, and Energy Water Factor, to quantify water consumption using real-world data. Using four representative HPC systems – Marconi, Fugaku, Polaris, and Frontier – as examples, we provide implications for HPC system planning and management. We explore the impact of regional water scarcity and nuclear-based energy strategies on HPC sustainability. Our findings aim to advance the development of water-aware, environmentally responsible computing infrastructures.

Due to the unique aspects of serverless computing like keep-alive and co-location of functions, it is challenging to account for its carbon footprint. This is the first work to introduce the need for systematic methodologies for carbon accounting in the serverless environment, propose new methodologies and in-depth analysis, and highlight how the carbon footprint estimation can vary based on the chosen methodology. It discusses how serverless-specific scheduling choices can impact the tradeoffs between performance and carbon footprint, with an aim toward standardizing methodological choices and identifying opportunities for future improvements.

The rapid advancement of generative AI has heightened environmental concerns, particularly regarding carbon emissions.Our framework, SPROUT, addresses these challenges by reducing the carbon footprint of inference in large language models (LLMs).SPROUT introduces "generation directives" to guide the autoregressive generation process, achieving a balance between ecological sustainability and high-quality outputs.By employing a strategic optimizer for directive assignment and a novel offline quality evaluator, SPROUT reduces the carbon footprint of generative LLM inference by over 40% in real-world evaluations, using the Llama model and global electricity grid data.This work is crucial as the rising interest in inference time compute scaling laws amplifies environmental concerns, emphasizing the need for eco-friendly AI solutions.

This work introduces ECOLIFE, the first carbon-aware serverless function scheduler to co-optimize carbon footprint and performance. ECOLIFE builds on the key insight of intelligently exploiting multi-generation hardware to achieve high performance and lower carbon footprint. ECOLIFE designs multiple novel extensions to Particle Swarm Optimization (PSO) in the context of serverless execution environment to achieve high performance while effectively reducing the carbon footprint.