Sustainable Data Centers Research Articles

District heating utilizing waste heat of a data center: High-temperature heat pumps

Data centers are energy-intensive facilities with substantial low-grade waste heat. High-temperature heat pumps can be critical in boosting the data center’s waste heat for district heating, improving the system-level energy efficiency of data centers, and reducing CO2 emissions in district heating. This study built thermodynamic models to assess high-temperature heat pumps with six configurations using low global warming potential refrigerants to supply heat up to 120 °C. The heat pump configurations include single-stage or two-stage cycles with advanced components, such as internal heat exchanger, economizer, flash tank, or parallel compressor. The refrigerants include R1234ze(Z), R1233ed(E), R1224yd(Z), R600, and R600a, and R245fa is used as a reference. A case study was carried out to recover the waste heat from the Frontier high-performance computing data center and provide hot water for district heating at the US Department of Energy’s Oak Ridge National Laboratory campus. The optimized performance of high-temperature heat pumps is characterized with various effectiveness of internal heat exchangers, and the operating parameters of economizer or flash tank, as well as their combination. The results show that the configurations of two-stage cycles with internal heat exchanger + flash tank and internal heat exchanger + economizer/parallel-compressor provide the highest coefficient of performance under scenarios of the maximum allowable value and a fixed value (0.3) of the internal heat exchangers’ effectiveness, respectively. R1234ze(Z) and R600a are the most promising refrigerants, considering trade-offs between the coefficient of performance and the volumetric heating capacity. The single-stage cycle with internal heat exchanger + economizer/parallel-compressor using R1234ze(Z) is recommended for utilizing Fronter’s waste heat in district heating. A one mega-watt high-temperature heat pump will reduce 33,100–33,200 metric tons of CO2 emission annually, corresponding to 85.4 %–85.6 % of equivalent CO2 emissions from natural gas boilers. This study provides good guidelines for designing and deploying high-temperature heat pumps to support sustainable data centers and decarbonize district heating in the US.

Variational quantum circuit learning-enabled robust optimization for AI data center energy control and decarbonization

As the demand for artificial intelligence (AI) models and applications continues to grow, data centers that handle AI workloads are experiencing a rise in energy consumption and associated carbon footprint. This work proposes a variational quantum computing-based robust optimization (VQC-RO) framework for control and energy management in large-scale data centers to address the computational challenges and overcome limitations of conventional model-based and model-free strategies. The VQC-RO framework integrates variational quantum circuits (VQCs) with classical optimization to enable efficient and uncertainty-aware control of energy systems in AI data centers. Quantum algorithms executed on noisy intermediate-scale quantum (NISQ) devices are used for value function estimation trained with Q-learning, leading to the formulation of a robust optimization problem with uncertain coefficients. The quantum computing-based robust control strategy is designed to address uncertainties associated with weather conditions and renewable energy generation while optimizing energy consumption in AI data centers. This work also outlines the computational experiments conducted at various AI data center locations in the United States to analyze the reduction in power consumption and carbon emission levels associated with the proposed quantum computing-based robust control framework. This work contributes a novel approach to energy-efficient and sustainable data center operation, promising to reduce carbon emissions and energy consumption in large-scale data centers handling AI workloads by 9.8 % and 12.5 %, respectively.

Data Center Sustainability: Revisits and Outlooks

As energy-intensive entities, data centers are associated with significant environmental impacts, making their sustainability a subject of growing interest in recent years. In this article, we revisit data center sustainability and propose a forward-looking vision for improving data center sustainability. We argue that data center sustainability encompasses more than just energy efficiency and must be evaluated and optimized through a multi-faceted approach. To this end, we first present an overview of the sustainability metrics from five aspects. After that, we demonstrate the sustainability status of the latest data centers utilizing publicly available data center sustainability ratings. Furthermore, we examine the evolution of data center sustainability standards in Singapore to highlight several trending features. Based on the analysis, we identify several key elements of sustainable data centers. We then propose the Cognitive Digital Twin (CDT) architecture, which incorporates a digital twin engine for system-wide simulation and a decision engine for optimal control to improve data center sustainability. A case study is performed to optimize the chiller plant efficiency of a production data center in Singapore. The results demonstrate that the CDT can improve chiller plant energy efficiency by 5%, indicating around 140 metric tons of annual carbon emission savings.

Balancing the trade-off between data center development and its environmental impacts: A comparative analysis of Data Center Policymaking in Singapore, Netherlands, Ireland, Germany, USA, and the UK

Data centers' deployment rate has raised concerns about high natural resource consumption. The scholarship has examined the scale of negative environmental impacts but focused to a lesser degree on policy responses enacted to improve data center sustainability. We analyze these policy responses in terms of (i) the degree of policy change undertaken, (ii) the duration of policy changes, and (iii) the effects on data center deployment rates. This work investigates six data center hubs - Singapore, Netherlands(Amsterdam), Ireland, Germany(Frankfurt), USA(Virginia), and the UK(London) – identifying three policy responses: moratorium, adjustment, and continuation. All six locations changed their data center deployment strategies. In three cases, data center policy adjustments were preceded by significant short-term disruptions of digital infrastructure growth (e.g., halts to new permits), demonstrating the challenges in managing high-growth sectors under environmental and infrastructural constraints. Among the three cases, only two have introduced long-term measures, i.e., adjustments through stricter environmental standards, showing that moratoriums better work as a temporary measure to stabilize natural resource consumption while buying time for long-term adjustments. Conversely, in the two cases opting for a continuation of data center deployment (e.g., redirecting new permits to less resource-constrained areas), the strategy lowered implementation costs but has been subject to a lingering opposition process. Beyond response specificities, the six cases also present general policymaking patterns. First, stricter energy accuracy standards may facilitate planning sustainable data center growth. Second, policy responses may yield better results when paired with multi-regional infrastructural strategies matching IT infrastructure needs with available energy capacity.

A systematic review of green-aware management techniques for sustainable data center

Cloud computing is one of the powerful engines driving global industrial upgrading and the booming digital economy. However, the explosive growth of cloud data centers (DCs) has resulted in inevitable energy consumption and carbon emission problems. Therefore, constructing energy-efficient and sustainable DCs will be essential for green cloud computing. This review makes several efforts to thoroughly investigate and track the research progress and routes to sustainable DCs. Firstly, we construct a new conceptual model of sustainable DCs to cover cutting-edge research results and indicate future evolutionary directions. Secondly, this review provides a comprehensive survey of five topics from a technical perspective: workload management, virtual resource management, energy management, thermal management, and waste heat recovery. Subsequently, some real-world datasets relevant to the topics, including workload traces, renewable energy data, and electricity price traces, have been specifically collected to support researchers in conducting further research. Finally, based on observations of existing works, we highlight some salient technical challenges and promising solutions to provide sensible energy and carbon reduction suggestions in sustainable DCs.

Net Zero Carbon Emission

Abstract: With the rapid growth of digital services and the increasing reliance on data centers, energy consumption and carbon emissions from server infrastructure have become significant environmental concerns. This abstract outlines a novel approach for carbon detection from servers, aiming to improve the sustainability of data centers. Carbon emissions from data centers are increasingly a cause for concern, as the world grapples with the realities of climate change. According to recent studies, data centers are responsible for a significant share of global electricity consumption, and subsequently, carbon emissions. It’s evident that addressing the sustainability of data centers is not just a technological challenge; it’s a moral and environmental imperative.

Energy-Efficient Cloud Data Centers Strategies for Sustainability and Environmental Impact

As the insatiable thirst for cloud computing services intensifies, data centers grapple with an exponentially growing energy appetite, casting a long shadow on environmental sustainability. This research paper delves into the critical issue of energy efficiency within cloud data centers, aiming to identify strategies that mitigate environmental consequences while ensuring optimal performance. The study reviews current practices in cloud infrastructure, analyzes the challenges associated with energy consumption, and proposes innovative solutions for enhancing the sustainability of data centers. By examining the intersection of technology, environmental responsibility, and operational efficiency, this paper contributes valuable insights into the development of energy-efficient cloud data centers, addressing both industry and environmental concerns. The findings of this research provide a foundation for the implementation of sustainable practices, contributing to the ongoing discourse on responsible and environmentally conscious cloud computing.

Carbon Footprint Reduction for Sustainable Data Centers in Real-Time

As machine learning workloads are significantly increasing energy consumption, sustainable data centers with low carbon emissions are becoming a top priority for governments and corporations worldwide. This requires a paradigm shift in optimizing power consumption in cooling and IT loads, shifting flexible loads based on the availability of renewable energy in the power grid, and leveraging battery storage from the uninterrupted power supply in data centers, using collaborative agents. The complex association between these optimization strategies and their dependencies on variable external factors like weather and the power grid carbon intensity makes this a hard problem. Currently, a real-time controller to optimize all these goals simultaneously in a dynamic real-world setting is lacking. We propose a Data Center Carbon Footprint Reduction (DC-CFR) multi-agent Reinforcement Learning (MARL) framework that optimizes data centers for the multiple objectives of carbon footprint reduction, energy consumption, and energy cost. The results show that the DC-CFR MARL agents effectively resolved the complex interdependencies in optimizing cooling, load shifting, and energy storage in real-time for various locations under real-world dynamic weather and grid carbon intensity conditions. DC-CFR significantly outperformed the industry-standard ASHRAE controller with a considerable reduction in carbon emissions (14.5%), energy usage (14.4%), and energy cost (13.7%) when evaluated over one year across multiple geographical regions.

Sustainability of Data Center Digital Twins with Reinforcement Learning

The rapid growth of machine learning (ML) has led to an increased demand for computational power, resulting in larger data centers (DCs) and higher energy consumption. To address this issue and reduce carbon emissions, intelligent design and control of DC components such as IT servers, cabinets, HVAC cooling, flexible load shifting, and battery energy storage are essential. However, the complexity of designing and controlling them in tandem presents a significant challenge. While some individual components like CFD-based design and Reinforcement Learning (RL) based HVAC control have been researched, there's a gap in the holistic design and optimization covering all elements simultaneously. To tackle this, we've developed DCRL-Green, a multi-agent RL environment that empowers the ML community to design data centers and research, develop, and refine RL controllers for carbon footprint reduction in DCs. It is a flexible, modular, scalable, and configurable platform that can handle large High Performance Computing (HPC) clusters. Furthermore, in its default setup, DCRL-Green provides a benchmark for evaluating single as well as multi-agent RL algorithms. It easily allows users to subclass the default implementations and design their own control approaches, encouraging community development for sustainable data centers. Open Source Link: https://github.com/HewlettPackard/dc-rl

Project management strategies for implementing energy-efficient cooling solutions in emerging data center markets

Project management strategies play a pivotal role in implementing energy-efficient cooling solutions in emerging data center markets. This review explores key strategies for successful implementation, focusing on the unique challenges and opportunities in these markets. Emerging data center markets are characterized by rapid growth and evolving infrastructure needs. As such, implementing energy-efficient cooling solutions requires a strategic approach that addresses local regulatory requirements, environmental conditions, and technological capabilities. Effective project management in these contexts involves several key strategies. Firstly, thorough planning is essential. This includes conducting feasibility studies, assessing site-specific requirements, and developing detailed project plans. Understanding the local context is crucial, as it can impact the selection and implementation of cooling solutions. Secondly, stakeholder engagement is critical. Engaging with local authorities, communities, and industry stakeholders can help ensure regulatory compliance and garner support for the project. Additionally, involving local experts and partners can provide valuable insights and enhance the project's success. Thirdly, implementing robust monitoring and evaluation mechanisms is essential. Monitoring energy consumption, cooling system performance, and environmental impacts allows for timely adjustments and optimizations, ensuring the long-term sustainability of the cooling solutions. Lastly, knowledge sharing and capacity building are key components of successful project management in emerging data center markets. Providing training and education on energy-efficient practices and technologies can help build local capacity and ensure the continued success of the cooling solutions. In conclusion, project management strategies for implementing energy-efficient cooling solutions in emerging data center markets require a holistic approach that considers local context, engages stakeholders, implements robust monitoring mechanisms, and promotes knowledge sharing. By adopting these strategies, data center operators can effectively address the challenges and opportunities in these markets, contributing to a more sustainable and resilient data center infrastructure.

Geospatial assessment of water footprints for hyperscale data centers in the United States

The sustainability of water usage in data centers is of growing importance considering rapid digitalization and environmental constraints on freshwater resources. However, the water footprint of data centers remains understudied, posing challenges to achieving sustainable data center operations. In this study, we assessed the water footprint of hyperscale data centers in the U.S. and identified major drivers of water use in data centers. Our findings offer valuable insights for designing water-efficient data centers, implementing sustainable practices, and formulating effective policies and technology incentives.

Dual-mutation mechanism-driven snake optimizer for scheduling multiple budget constrained workflows in the cloud

Scheduling workflows in cloud computing is to find the most appropriate mapping from a series of dependent tasks to a set of available virtual resources so as to minimize or maximize some user concerned objectives, which is very significant to a sustainable and high-efficient cloud data center. However, it still faces challenges since its NP-hardness and the diversified requirements of both the cloud service consumers and cloud service providers need to be satisfied at the same time, especially in addressing multiple applications requested simultaneously. This work proposes a dual-mutation mechanism-driven snake optimizer for scheduling multiple workflows in the cloud for minimizing the makespan of each workflow under user pre-defined budget constraints. Firstly, a continuous optimization algorithm, namely Snake Optimizer (SO) is adopted into discrete optimization, i.e., workflow scheduling. Secondly, a task execution order aware fitness function is designed to reduce the gaps or waiting time between parent and child tasks within a workflow and thus reduce the total execution time of the workflow. Besides, we analyze the existing snake optimizer and adjust the parameters corresponding to different evolutionary stages to adapt to our considered problem. Finally, a dual-mutation mechanism is developed by introducing a non-improvement iteration number for each snake and applying a standard bit mutation operation to prematurely converging snakes and the individuals randomly selected from the remaining snakes so that the population diversity can be enhanced and more potential solutions can be explored. Experimental results on a set of real-world scientific workflows show that our proposed algorithm is of great superiority in constraint satisfiability, meaning that compared with its peers, it is always the first to find feasible solutions.

Workload prediction for enhancing power efficiency of cloud data centers using optimized self‐attention‐based progressive generative adversarial network

SummaryNowadays, future workload prediction is an important requirement in cloud data centers to maintain flexibility and scalability of resources. However, due to unexpected peaks, drops in workload, noise, and redundancy in user requests, there is a considerable variance in resource demands, making it difficult to accurately predict workloads. Therefore, a self‐attention‐based progressive generative adversarial network (SAPGAN) optimized with Giza Pyramids Construction Algorithm (GPCA)‐based workload prediction is proposed for Sustainable Cloud Data Centers (CDC). At first, redundant data in historical data obtained through CDC are filtered utilizing Markov chain random field (MCRF) co‐simulation method. These pre‐processed historical data are supplied to the SAPGAN. The SAPGAN weight parameters are optimized by GPCA. The proposed method is analyzed using 2 benchmark datasets: HTTP traces from Saskatchewan and NASA. The simulation is implemented in JAVA. The performance metrics is examined to verify the efficacy of the proposed technique. The performance of the proposed approach provides 28.70%, 11.87%, and 14.79% higher accuracy; 30.15%, 11.72%, and 18.34% lesser energy consume for the dataset of NASA; and 5.32%, 2.45%, and 5.67% higher accuracy; 12.36%, 24.24%, and 34.16% lesser energy consume for the dataset of Saskatchewan HTTP traces compared with existing methods, such as auto‐adaptive learning in a dynamic cloud environment (AADEA‐WLP‐CDC), a neural network model depending on biphase adaptive learning for anticipating cloud data center workload (BALNN‐WLP‐CDC) and multiple scale ensemble of deep learning framework for multistep‐ahead cloud workload prediction (EMD‐LSTM‐GAN‐WLP‐CDC).

Carbon Dependencies in Datacenter Design and Management

Building a sustainable datacenter requires coordinated decisions in its design and system management. Existing research work on datacenter sustainability often considers the design space in isolation and misses opportunities to minimize carbon footprint through coordinated design and management where sustainability is a first-class objective. Design decisions such as datacenter site selection, renewable energy investment portfolios, and the provisioning of energy storage are intertwined with complementary solutions for operation, including various forms of demand response and carbon-aware workload management. In this paper, we advocate for holistic frameworks that take into account both operational and embodied carbon to coordinate between datacenter design and system management decisions.

Systematic Mapping Study: Research Opportunities on Capacity Planning

The central idea of the research is to improve the efficiency and sustainability of data centers by implementing accurate capacity planning, which will also improve their performance and availability. Various literature reviews have been conducted to understand the current status of capacity planning implementation in different domains and perspectives. However, a more organized and systematic approach is required to map research and implementation results in the relevant areas of capacity planning that have the potential for further development. The present study aims to fill this gap by conducting a systematic mapping study that combines both quantitative and qualitative methodologies. The quantitative approach involved the collection of literature and the classification of topics using the Latent Dirichlet Allocation (LDA) method. On the contrary, the qualitative approach used content analysis to identify future research directions based on keyword trends and topics. The PRISMA framework was followed to guide the search for relevant studies in electronic research literature databases. The mapping results revealed 15 topics, with topics 8, 10, 11, and 15 showing significant potential for further research and exhibiting increasing trends. The identified topics encompass capacity planning, energy and resource management, computing and technology, data analysis and statistics, engineering, and industry, all crucial for businesses and industries to operate efficiently and sustainably. This study provides a comprehensive overview of the state of capacity planning implementation and highlights areas that require further investigation.

Sustainable Energy Data Centres: A Holistic Conceptual Framework for Design and Operations

Data Centres serve as the foundation for digital technologies in the energy sector, enabling advanced analytics, optimization, and automation. However, their rapid growth can exert a substantial influence on the environment due to their energy consumption, water utilization, and production of electronic waste. This research begins with an energy overview of the setup and operations of data centres, highlighting their key components and infrastructure, and emphasizing their crucial role in managing energy resources and driving the energy sector’s digital technologies. Building upon this understanding, a holistic framework is proposed to tackle energy sustainability concerns in data centres, with a focus on energy-related aspects. The framework places emphasis on three primary sustainability metrics, namely energy efficiency, water consumption, and waste management. It underscores the significance of green building design principles and energy-efficient equipment as crucial constituents of sustainable data centre infrastructure. The framework delineates optimal energy operational best practices encompassing virtualization and consolidation, effective cooling tactics, and energy management and monitoring, all aimed at reducing energy consumption and enhancing energy performance. Furthermore, the framework emphasizes the significance of incorporating energy-related sustainability metrics into decision-making procedures and adhering to regulatory standards for energy efficiency. Through adherence to this framework, data centres’ environmental impact can be mitigated and a positive contribution towards a sustainable future can be made, particularly in the realm of energy conservation and optimization.

Enabling Practical Decision Making For Sustainable Green Data Center Planning

Data centers play a crucial role in storing and processing data in today's digital age, leading to a surge in demand for sustainable green data center planning. However, implementing practical measures to achieve sustainability remains a challenge for data center managers. This study aims to aid their informed decision-making in sustainable green data center planning. Previous research has identified seven green data center key components: ICT governance, infrastructure, energy, equipment lifecycle, green technology, benchmarking, and business continuity. Subsequently, the study expanded by utilizing the FAHP method to evaluate the perspectives of various experienced data center. Those green data center components were evaluated against each other regarding the three sustainability criteria: environment, economy, and corporate. Consequently, it was discovered that infrastructure, green technology, and business continuity consistently held the highest fuzzy weight in multiple sensitivity analysis scenarios. Thus, data center managers can allocate resources based on priority rankings and adjust accordingly.

A scalable micro-encapsulated phase change material and liquid metal integrated composite for sustainable data center cooling

In this paper, we utilized a eutectic gallium-indium liquid metal (LM) with high thermal conductivity to immerse carbon-based phase change material (PCM) capsules to acquire a PCM-LM composite. This produced a scalable, thermally stable, mechanically reliable, high thermally conductive, and leakage-free material. This composite was then applied as a heat mitigator for CPU cooling. Results demonstrate that clear improvements could be made compared to traditional pin-fin heat-sink cooling. Here the continuous full-load operating time could be prolonged by 414.3%. Moreover, the temperature difference between the idle and full-load operating states could be significantly decreased, and prevent damage from thermal stress. Significantly, a 23% energy-saving performance was attained because of the thermal buffering effect and high responsive ability of the composite. Guidelines for an effective match between the heat absorption of the composite and the heat generation from the CPU could also be established. Here the proposed PCM-LM composite can be applied to other PCM-based energy storage industries and efficient temperature control applications, contributing to carbon neutralization and global warming mitigation.

Design and Scalable Fabrication of Liquid Metal and Nano-Sheet Graphene Hybrid Phase Change Materials for Thermal Management.

Here, a paraffin/liquid metal (LM)/graphene hybrid thermal composite material with a high thermal-conductivity as well as high latent heat is developed. The paraffin is encapsulated in calcium alginate, which produces leakage-free phase change material (PCM) capsules. LM is filled amongthe gaps of PCM capsules to enhance overall heat conduction. Graphene nano-sheets coating attains efficient heat dissipation because of its high spectral emissivity (>91%) in the spectrum of the mid-infrared region. The developed material is verified to have strong compatibility and durable stability. The composite is utilized as a thermal buffer (TB) for central processing unit thermal management to demonstrate the synergy of these superior thermal properties. In certain cases, active cooling normally used could be replaced by the developed TB without any energy consumption for thermal management, demonstrating a completely passive cooling strategy. Compared to traditional heat sink active cooling, general energy savings of 10.4-26.3% could thus be achieved by the developed composite in wider operating conditions, proving itspotential for more efficient and sustainable data center cooling alongside thermal management of other ground-based electrical/electronic equipment.

A material social view on data center waste heat: Novel uses and metrics

Today's data centers use substantial amounts of the world's electrical supply. However, in line with circular economy concepts, much of this energy can be reused. Such reuse includes the heating of buildings, but also commodity dehydration, electricity production and energy storage. This multi-disciplinary paper presents several novel applications for data center waste heat. Next, the paper accounts for three case studies, taken from three different societal contexts: urban Malaysia, rural Costa Rica and semi-urban Sweden. A discussion on data center energy metrics leads to the development of a new metric, Datacenter Energy Sustainability Score (DESS), which is evaluated within the three use cases. Last, it is shown how a material social view on metrics provides a way past a problem that has haunted the data center industry for the last 15 years, whilst benefitting both data center owners who want to compete through sustainability as well as stakeholders from governments on local, regional and national levels. The paper makes clear that a sustainability strategy should be based on a material social view and stretch beyond the building itself. In fact, and as demonstrated by the relevance of DESS, modern data centers are so energy-efficient that data center sustainability is no longer mainly an engineering issue, but a matter requiring multi-disciplinary insights, approaches and collaboration.