Reducing the environmental impact of cloud computing requires efficient workload distribution across geographically dispersed Data Center Clusters (DCCs) and simultaneously optimizing liquid and air (HVAC) cooling with time shift of workloads within individual data centers (DC). This paper introduces Green-DCC, which proposes a Reinforcement Learning (RL) based hierarchical controller to optimize both workload and liquid cooling dynamically in a DCC. By incorporating factors like weather, carbon intensity, and resource availability, Green-DCC addresses realistic constraints and interdependencies. We demonstrate how the system optimizes multiple data centers synchronously, enabling the scope of digital twins, and compare the performance of various RL approaches based on carbon emissions and sustainability metrics while also offering a framework and benchmark simulation for broader ML research in sustainability.

In recent years, Large Language Models (LLM) such as ChatGPT, Copilot, and Gemini have been widely adopted in different areas. As the use of LLMs continues to grow, many efforts have focused on reducing the massive training overheads of these models. But it is the environmental impact of handling user requests to LLMs that is increasingly becoming a concern. Recent studies estimate that the costs of operating LLMs in their inference phase can exceed training costs by 25× per year. As LLMs are queried incessantly, the cumulative carbon footprint for the operational phase has been shown to far exceed the footprint during the training phase. Further, estimates indicate that 500 ml of fresh water is expended for every 20–50 requests to LLMs during inference. To address these important sustainability issues with LLMs, we propose a novel framework called SLIT to co-optimize LLM quality of service (time-to-first token), carbon emissions, water usage, and energy costs. The framework utilizes a machine learning (ML) based metaheuristic to enhance the sustainability of LLM hosting across geo-distributed cloud datacenters. Such a framework will become increasingly vital as LLMs proliferate.

Machine learning has driven an exponential increase in computational demand, leading to massive data centers that consume significant amounts of energy and contribute to climate change. This makes sustainable data center control a priority. In this paper, we introduce SustainDC, a set of Python environments for benchmarking multi-agent reinforcement learning (MARL) algorithms for data centers (DC). SustainDC supports custom DC configurations and tasks such as workload scheduling, cooling optimization, and auxiliary battery management, with multiple agents managing these operations while accounting for the effects of each other. We evaluate various MARL algorithms on SustainDC, showing their performance across diverse DC designs, locations, weather conditions, grid carbon intensity, and workload requirements. Our results highlight significant opportunities for improvement of data center operations using MARL algorithms. Given the increasing use of DC due to AI, SustainDC provides a crucial platform for the development and benchmarking of advanced algorithms essential for achieving sustainable computing and addressing other heterogeneous real-world challenges.

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 27.45× speedup and 1.53 × improvement in Pareto Hypervolume while reducing the carbon footprint by up to 1.33 ×, water footprint by up to 3.09 ×, and energy costs by up to 1.40 ×. In the simultaneous three-objective co-optimization scenario, MOSAIC achieves a cumulative improvement across all objectives (carbon, water, cost) of up to 4.61 × compared to the state-of-the-arts.

Commercial buildings are significant consumers of electricity. We propose a number of methods for managing power in commercial buildings. The first step toward better energy management in commercial buildings is monitoring consumption. However, instrumenting every electrical panel in a large commercial building is an expensive proposition. In this article, we demonstrate that it is also unnecessary. Specifically, we propose a greedy meter (sensor) placement algorithm based on maximization of information gain subject to a cost constraint. The algorithm provides a near-optimal solution guarantee, and our empirical results demonstrate a 15% improvement in prediction power over conventional methods. Next, to identify power-saving opportunities, we use an unsupervised anomaly detection technique based on a low-dimensional embedding. Furthermore, to enable a building manager to effectively plan for demand response programs, we evaluate several solutions for fine-grained, short-term load forecasting. Our investigation reveals that support vector regression and an ensemble model work best overall. Finally, to better manage resources such as lighting and HVAC, we propose a semisupervised approach combining hidden Markov models (HMMs) and a standard classifier to model occupancy based on readily available port-level network statistics. We show that the proposed two-step approach simplifies the occupancy model while achieving good accuracy. The experimental results demonstrate an average occupancy estimation error of 9.3% with a potential reduction of 9.5% in lighting load using our occupancy models.

The environmental impact of servers and datacenters is an important future challenge. System architects have traditionally focused on operational energy as a proxy for designing green servers, but this ignores important environmental implications from server production (materials, manufacturing, etc.). In contrast, this paper argues for a lifecycle focus on the environmental impact of future server designs, to include both operation and production. We present a new methodology to quantify the total environmental impact of system design decisions. Our approach uses the thermodynamic metric of exergy consumption, adapted and validated for use by system architects. Using this methodology, we evaluate the lifecycle impact of several example system designs with environment-friendly optimizations. Our results show that environmental impact from production can be important (around 20% on current servers and growing) and system design choices can reduce this component (by 30--40%). Our results also highlight several, sometimes unexpected, cross-interactions between the environmental impact of production and operation that further motivate a total lifecycle emphasis for future green server designs.

Information technology (IT) forms a crucial foundation for designing, building and managing future sustainable cities. This paper proposes a model to innovate the engineering and computing curriculum to include sustainability and IT topics in order to develop the skills and competencies that future professionals will need to design, build and manage future cities. Rather than developing a new program, we propose a curriculum model — called SustainIT -adapted from the successful 2006 US NAE Gordon Prize engineering curriculum innovation, The Learning Factory — as a possible roadmap to reform and complement existing Bachelors of Science (BS) degrees in engineering. By providing a series of guided electives, any engineering program may offer engineering, and/or computer science students the opportunity to learn about and become specialized in IT for Sustainability. Multidisciplinary topics include traditional ecological engineering; life-cycle design; design and application of resource microgrids; pervasive sensing and data aggregation; knowledge discovery, data mining and visualization; and, policy based control and operation for resource provisioning.

Commercial buildings are significant consumers of electricity. The first step towards better energy management in commercial buildings is monitoring consumption. However, instrumenting every electrical panel in a large commercial building is expensive and wasteful. In this paper, we propose a greedy meter (sensor) placement algorithm based on maximization of information gained, subject to a cost constraint. The algorithm provides a near-optimal solution guarantee. Furthermore, to identify power saving opportunities, we use an unsupervised anomaly detection technique based on a low-dimensional embedding. Further, to better manage resources such as lighting and HVAC, we propose a semi-supervised approach combining hidden Markov models (HMM) and a standard classifier to model occupancy based on readily available port-level network statistics.

Recently, the demand for data center computing has surged, increasing the total energy footprint of data centers worldwide. Data centers typically comprise three subsystems: IT equipment provides services to customers; power infrastructure supports the IT and cooling equipment; and the cooling infrastructure removes heat generated by these subsystems. This work presents a novel approach to model the energy flows in a data center and optimize its operation. Traditionally, supply-side constraints such as energy or cooling availability were treated independently from IT workload management. This work reduces electricity cost and environmental impact using a holistic approach that integrates renewable supply, dynamic pricing, and cooling supply including chiller and outside air cooling, with IT workload planning to improve the overall sustainability of data center operations. Specifically, we first predict renewable energy as well as IT demand. Then we use these predictions to generate an IT workload management plan that schedules IT workload and allocates IT resources within a data center according to time varying power supply and cooling efficiency. We have implemented and evaluated our approach using traces from real data centers and production systems. The results demonstrate that our approach can reduce both the recurring power costs and the use of non-renewable energy by as much as 60% compared to existing techniques, while still meeting the Service Level Agreements.

A world consisting of billions of service-oriented client devices and thousands of data centers can deliver a diverse range of services, from social networking to management of our natural resources. However, these services must scale in order to meet the fundamental needs of society. To enable such scaling, the total cost of ownership of the data centers that host the services and comprise the vast majority of service delivery costs will need to be reduced. As energy drives the total cost of ownership of data centers, there is a need for a new paradigm in design and management of data centers that minimizes energy used across their lifetimes, from “cradle to cradle”. This tutorial article presents a blueprint for a “net-zero data center”: one that offsets any electricity used from the grid via adequate on-site power generation that gets fed back to the grid at a later time. We discuss how such a data center addresses the total cost of ownership, illustrating that contrary to the oft-held view of sustainability as “paying more to be green”, sustainable data centers—built on a framework that focuses on integrating supply and demand management from end-to-end—can concurrently lead to lowest cost and lowest environmental impact.

This paper describes an approach for designing a power management plan that matches the supply of power with the demand for power in data centers. Power may come from the grid, from local renewable sources, and possibly from energy storage subsystems. The supply of renewable power is often time-varying in a manner that depends on the source that provides the power, the location of power generators, and the weather conditions. The demand for power is mainly determined by the time-varying workloads hosted in the data center and the power management policies implemented by the data center. A case study demonstrates how our approach can be used to design a plan for realistic and complex data center workloads. The study considers a data center's deployment in two geographic locations with different supplies of power. Our approach offers greater precision than other planning methods that do not take into account time-varying power supply and demand and data center power management policies.

Data centers contain IT, power and cooling infrastructures, each of which is typically managed independently. In this paper, we propose a holistic approach that couples the management of IT, power and cooling infrastructures to improve the efficiency of data center operations. Our approach considers application performance management, dynamic workload migration/consolidation, and power and cooling control to “right-provision” computing, power and cooling resources for a given workload. We have implemented a prototype of this for virtualized environments and conducted experiments in a production data center. Our experimental results demonstrate that the integrated solution is practical and can reduce energy consumption of servers by 35% and cooling by 15%, without degrading application performance.

The environmental impact of data centers is significant and is growing rapidly. Servers alone in the US consumed 1.2% of the nation's energy in 2005, according to the EPA. In the following year, the EPA found that the cost of energy rose by 10%. However, there are many opportunities for greater efficiency through integrated design and management of data center components. To that end, we propose a sustainable data center that replaces conventional resource delivery models with a framework centered around the supply and demand side management of all data center resources including IT, power and cooling. We have identified five elements for achieving this vision: data center scale lifecycle design, flexible and configurable building blocks, pervasive cross-layer sensing, knowledge discovery and visualization, and autonomous control. We describe these principles and provide selected results that quantify the potential for savings.

A recent study found that IT contributes about 2% of global greenhouse gas emissions, in line with that of the aviation industry. Furthermore, it projected that that this share would double by the year 2020. Increasing environmental concern and regulatory action will soon force a paradigm shift in how IT solutions are designed and managed across their lifecycles. Data centers are a prominent and fast growing component of this impact.