Energy Consumption Of Data Centers Research Articles

Green Cloud Computing: Innovating for Sustainable, Low-Impact Data Management.

Cloud computing is the internet-based provision of computer services. The usage of some cloud services by individuals and businesses is expanding quickly. The rising demand for cloud infrastructure has led to a notable rise in the energy consumption of data centers. Reducing energy usage is essential to lowering carbon dioxide emissions, hence it must be addressed. Given that green cloud computing's main objective is to maximize energy efficiency, it is acknowledged as a potential solution to this issue. This paper presents green cloud computing alternatives for energy savings in addition to defining cloud computing with the key cloud service models, deployment strategies, and computing platforms. After that, these solutions were categorized based on how well they reduced energy.

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.

Climate zones for the application of water-side economizer in a data center cooling system

Water-side economizers have been widely adopted in response to the growing energy consumption in data centers. Although many studies have recognized the energy-saving effects of water-side economizers under different climate conditions, their water utilization is still unclear. This study combines energy savings and water consumption to assess the applicability of water-side economizers in 34 cities in China under two sets of chilled water supply and return water temperatures. The energy savings when the supply and return water temperature is 21/27 °C are 3.17 times higher than when it is 12/18 °C. Nine cities can reduce water consumption while pursuing energy savings. To further quantify the applicability of water-side economizers, this work introduces a new indicator, water to energy saving ratio, and utilizes K-means clustering to conduct zoning studies on different cities. The results show that the applicability of water-side economizers in different regions can be divided into four zones. In Zone I, the applicability of water-side economizers is the lowest, and it is recommended that they be combined with high-efficiency heat transfer technologies. Zone II comprises 12 cities, with moderate applicability of water-side economizers. In Zone III, there is a need to balance the relationship between water and energy. Zone IV has the lowest energy and water consumption, with an average of 23% water resource savings compared to Zone I. However, attention should be paid to preventing the freezing of cooling towers in winter.

Evaluation of energy performance and ecological benefit of free-cooling system for data centers in worldwide climates

Water-side economizer (WSE) is an effective free-cooling solution aiming to enhance the natural cooling duration and in turn reduce the cooling-energy consumption of data centers. However, the system's performance is constrained by the limited understanding of its control mechanisms and the influence of local climatic conditions on its free-cooling capabilities. To overcome these limitations, a thermodynamic model of the WSE system with control strategies is provided and its adaptability performance in diverse worldwide locations is systematically evaluated. Modelica-based models of chiller plants and control strategies are firstly developed, and then the energy consumption and ecological benefit of the WSE system are investigated via large-scale simulations in worldwide climates. The performance indicators and application potential of the system are demonstrated through visualization maps. The analysis reveals significant energy savings potential of the WSE system, with the capacity to reduce annual cooling energy consumption of data centers by 4.4–33.7 % in hot to marine zones, and even by a remarkable 62.6 % in plateaus and cold zones. Furthermore, it is observed that every 1 °C increase in outdoor air temperature can reduce energy efficiency ratio by 1.3–8.7 % while simultaneously increase PUE by 0.3–1.0 %. The ecological benefit results revealed that the WSE system can effectively harness larger quantities of stable free-cooling resources in mixed and cool climate zones, exceeding its capabilities in hot and warm regions. These results offer valuable insights for the impact of climate on the energy and ecological benefits of WSE system for data center, which can be helpful to guide the design or retrofit of such cooling systems in specific locations, presenting an immense opportunity to maximize cooling-energy savings.

Global optimization strategy of prosumer data center system operation based on multi-agent deep reinforcement learning

The escalating issues of high energy consumption and carbon emissions in data centers (DCs) necessitate the optimization of system operations. However, early optimization strategies were overly simplistic and lacked automated updating and iterative capabilities. With the evolution of artificial intelligence (AI), researchers have applied deep reinforcement learning (DRL) algorithms to system operations. However, the optimization focus has been limited to the internal systems, lacking global optimization. In this paper, a global optimization control strategy based on the Dueling double-deep Q network (D3QN) and value decomposition network (VDN) algorithms is proposed to make the DCs system operate more closely with the upstream, midstream, and downstream. By adjusting battery charging/discharging capacity, computational workload, and waste heat utilization heating temperature global synergistic optimization is achieved. Compared with without optimization, renewable energy waste, operation cost, total electricity consumption, and grid electricity consumption are reduced by 18.37%, 9.78%, 4.01%, and 29.74%, respectively. Additionally, a detailed comparison between non-algorithmic optimization and algorithmic optimization is provided, offering valuable insights for substantial energy savings and emissions reduction in DCs. The results demonstrate the importance of fully exploring the interactive potential between upstream energy supply, midstream computational workload, and downstream waste heat recovery to achieve synergistic global optimization of "computing power", "thermal energy" and "electrical energy" for the sustainable and green development of DCs or other prosumer buildings.

A multi-objective optimization of a single-phase immersion cooling system at cabinet level

With the rapid development and urgent requirement of high computing power, the energy consumption of data center (DC) is constantly increasing. The issue of high energy consumption constrains rapid development of DCs and becomes a major challenge that urgently needs to be solved in the transformation of DCs towards large-scale, intelligent, and energy-saving. In the present study, a cabinet level single-phase immersion cooling (SPIC) system was experimentally investigated with coolant of Noah 3000D. The cooling capacity and energy consumption level of the SPIC system were investigated. The impacts of the heat load, coolant flow rate, and cooling water flow rate on the thermal performance of the SPIC unit were quantitatively analyzed. Based on experimental data fitting, the heat transfer criterion correlation and pressure difference calculation formula were obtained. The error caused by the heat transfer criterion correlation and pressure difference calculation formula is basically within ± 5 %. Based on the multi-objective optimization, both the coolant flow rate and cooling water flow rate cause obvious influences on the heat transfer coefficient and energy consumption of SPIC systems. The temperature changes on both sides of the heat exchanger can be ignored, and the error caused by the assumption does not exceed ± 1 %. With some valuable findings obtained, this work can supply some technique references for engineering design and operation management of SPIC systems.

Towards Contrastive and Data Driven Cloud Data Center Energy Prediction using Regression Analysis

Abstract: Data centers are the backbone of todays Internet and cloud computing systems. Due to the increasing demand for electrical energy by data centers, it is necessary to account for the vast amount of energy they consume. Energy modeling and prediction of data centers plays a pivotal role in this context. In this study, we address the challenge of predicting energy consumption in cloud data centers, crucial for managing their significant electricity demand. Despite numerous existing methods, there remains a lack of robust methodologies. To fill this gap, we propose a novel approach that incorporates aleatoric uncertainty estimation. Our method utilizes regression distributions to model this uncertainty, with parameters derived from regressive techniques. This yields energy consumption predictions as random variables drawn from these distributions. Additionally, we illustrate how these random variables can be aggregated to form probabilistic forecasts for diverse data center portfolios. Our methodology achieves three key advancements: 1) introducing a simple multiple linear regression model for fundamental series; 2) devising a unique method that combines quantile regression and empirical copulas to estimate joint distribution; 3) enhancing prediction accuracy through a weighted correction technique based on constrained quantile regression.

Ternary logic in the optical controlled-SWAP gate based on Laguerre-Gaussian modes of light

The need set by a computational industry to increase processing power, while simultaneously reducing the energy consumption of data centers, became a challenge for modern computational systems. In this work, we propose an optical communication solution, that could serve as a building block for future computing systems, due to its versatility. The solution arises from Landauer’s principle and utilizes reversible logic, manifested as an optical logical gate with structured light, here represented as Laguerre-Gaussian modes. We introduced a phase-shift-based encoding technique and incorporated multi-valued logic in the form of a ternary numeral system to determine the similarity between two images through the free space communication protocol.

Rock-hyrax: An energy efficient job scheduling using cluster of resources in cloud computing environment

In a cloud computing environment, job scheduling allows the service provider to schedule resources based on demand. Job scheduling must also ensure QoS, end-user satisfaction, and the efficient usage of resources. Cloud computing vendors assign virtualized computing resources to end-users based on job requirements that are dynamically scalable and pay-per-use. The assignment of jobs requires proper investigation and mapping of available resources. In this paper, we have proposed a novel job scheduling scheme based on Rock Hyrax. Our Rock Hyrax approach uses objective functions to map jobs to available resources. The objective function considers a variety of QoS parameters like makespan, response time and energy efficiency. Our method employs two key QoS parameters: makespan and energy consumption. The node behavior and characteristics, such as processing power, storage, and network connectivity to cluster similar resources, have also been considered for scheduling. An experimental setup is created for a thorough study of the proposal using CloudSim simulator. For both the jobs and virtual machines, static and dynamic scenarios for performance evaluation have been developed. To compare our work with existing scheduling algorithms like ACO, PSO, BFO, and ABC has been considered and we have found that the proposal reduces makespan by 2–9% as increased in jobs. Furthermore, the proposed method reduces total energy consumption in data centers by 7–23% as jobs request increases. The findings support the claim that the proposed method surpasses the existing methods and significantly shortens the time needed to determine the resource required for the job.

Immersion Cooling Technology Development Status of Data Center

With the continuous development of data centers and information technology equipment, data center energy consumption continues to increase, China's data center energy consumption has accounted for 4% of the power consumption of society as a whole, while the data center cooling system accounts for 30%-50% of the data center energy consumption. At the same time, with the development of new high-energy chips, the traditional cooling methods have been unable to meet the requirements of IT equipment cooling, so how to reduce the energy consumption of data centers, especially the cooling system energy consumption, and to meet the cooling requirements of data centers with high heat flux density is becoming the focus of research in the field of data centers. In order to solve the data center cooling system of high energy consumption and high heat flow density needs, immersion cooling technology came into being, this paper is mainly on the data center immersion cooling technology research and use of the current situation is introduced and explained, compared to the single-phase immersion cooling technology and two-phase immersion cooling technology cooling principle, the current state of development and use of the prospects for elaboration, while the data center immersion cooling system waste heat utilization is explored, providing readers with a wide range of data center cooling system. At the same time, the utilization of waste heat in data center immersion cooling system is discussed, providing readers with extensive and detailed background knowledge of data center immersion cooling technology.

A global optimization method for data center air conditioning water systems based on predictive optimization control

As the national digitization process accelerates, the number and energy consumption of data centers are increasing year by year. Want to reduce the PUE of the data center. It is correct to start from the perspective of optimizing the energy consumption of air conditioning system operation. In this paper, a predictive optimization control method that is different from previous real-time optimization control is proposed. Predict, optimize, control and adjust the operating parameters of the air conditioning water system in the data center. At the same time, it solves the energy saving problem of the air conditioning system and the lag problem of previous adjustments. Predictive optimization control has broad prospects in research on operation optimization of data center air conditioning water systems.

A new shared energy storage business model for data center clusters considering energy storage degradation

In recent years, the energy consumption of data centers (DCs) has shown a sharp upward trend. Given the high investment cost of energy storage, this study introduces the concept of energy sharing within a data center cluster (DCC) and proposes a novel shared energy storage (SES) business model. The model realizes the co-optimization for DCC and SES and is divided into three main layers. Firstly, considering the SES's economy and the DCC's cleanliness, the upper layer establishes a novel multi-objective optimization configuration scheme for SES with lifetime degradation. In addition, a two-stage robust optimization technique for DCC is proposed in the middle layer, considering the demand response mechanism for data loads. Subsequently, the concept of contribution degree (CD) is defined, and a novel benefit distribution mechanism based on cooperative game and CD is presented in the lower layer. The simulation results demonstrate that the proposed business model positively enhances the economic benefits of the SES and DCC. The constructed uncertainty optimization model exhibits strong robustness, and the proposed benefit allocation scheme is fairer and more reasonable.

Tools, techniques, and trends in sustainable software engineering: A critical review of current practices and future directions

The quest for sustainability has extended its reach into the realm of software engineering, prompting an exploration of tools, techniques, and emerging trends to mitigate the environmental impact of software development and operation. This review provides a critical review of current practices and future directions in sustainable software engineering. In recent years, the software industry has recognized the need to address the environmental footprint of software systems, considering factors such as energy consumption, resource utilization, and carbon emissions. Consequently, a plethora of tools and techniques have emerged to support sustainable software development processes. These range from energy-efficient programming languages and frameworks to eco-friendly software architectures and design patterns. Moreover, methodologies such as Green Software Engineering (GSE) and Sustainable Software Development (SSD) have gained traction, emphasizing the integration of sustainability considerations throughout the software development lifecycle. By adopting practices like green requirements engineering, energy-aware testing, and eco-design principles, organizations can optimize their software systems for reduced environmental impact without compromising functionality or performance. Furthermore, trends in sustainable software engineering extend beyond traditional development practices. The rise of cloud computing, edge computing, and Internet of Things (IoT) technologies presents both challenges and opportunities for sustainability. Techniques such as serverless computing and containerization offer potential benefits in terms of resource efficiency and scalability, while also introducing new considerations regarding data center energy consumption and electronic waste management. Looking ahead, the future of sustainable software engineering is marked by innovation and collaboration. Emerging technologies such as artificial intelligence (AI) and blockchain hold promise for optimizing resource allocation, enhancing energy efficiency, and fostering transparency in sustainability efforts. Additionally, interdisciplinary collaboration between software engineers, environmental scientists, and policymakers will be essential in shaping a more sustainable digital ecosystem. The journey towards sustainable software engineering involves a multifaceted approach encompassing tools, techniques, and ongoing adaptation to evolving trends. By critically evaluating current practices and embracing future directions, the software industry can contribute to a more environmentally responsible and resilient future.

Power system operational reliability assessment based on the data center energy consumption elastic space

In the era of big data, data centers with high energy consumption, interconnectivity, and load flexibility have developed rapidly. However, due to data privacy issues, the traditional power-system operational reliability assessment (ORA) struggles to precisely consider the load flexibility of data centers, leading to inaccurate evaluation. To this end, this article proposes an ORA method considering the load flexibility of data centers via the energy consumption elastic space. By transforming the inner operation constraints of data centers into an equivalent elastic space, the ORA does not require any private data to complete the evaluation. Specifically, the energy consumption model of data centers is established to accurately describe the load flexibility. Then, based on multi-parametric programming techniques, the energy consumption elastic space of data centers is characterized by data centers’ power demand constraints, which do not involve privacy data, and no privacy concerns exist. Finally, the ORA model and the evaluation method based on the energy consumption elastic space can be constructed. With a lot of data center operation constraints being replaced by power demand constraints, the proposed method can complete an evaluation faster without accuracy loss. Its effectiveness is validated through simulations using the IEEE RTS 24-bus system and a provincial 661-bus system.

A port consolidation model for data center network infrastructure energy efficiency

Technological advancements have increased the energy consumption of data center network infrastructure causing an increase in global carbon footprints. Consolidation of hardware is considered to be one of the acceptable techniques for reducing energy consumption in data centers. However, consolidation of hardware may result in scarce fault tolerance which consequently leads to performance degradation. Therefore, a technique is required that performs consolidation while data center performance remains unaffected. In this work, a port consolidation model has been proposed. The proposed model diverts the traffic of the ports in a switch to other ports so that the number of active ports can be reduced. The reduction of active ports will result in the energy-efficient utilization of the switch. The proposed diverts the traffic by evaluating the Arrival Rate of packets and the Forwarding Rate of the switch. The proposed algorithm used in the model has been examined through a case study. The proposed model is implemented in integration with our previously proposed network refresh model.

Experimental research and energy effectiveness analysis on chip-level two-stage loop thermosyphon system for data centre free cooling

The development of 5G technology has put forward new requirements for computing the abilities of chips, and chip power has also significantly increased. Consequently, the heat load of the rack will increase rapidly in the future. Traditional computer room air conditioning (CRAC) systems cannot satisfy the rack’s high heat flux dissipation requirements, and new cooling forms such as chip-level cooling should be adopted. In this study, a two-stage chip-level cooling system, which is pumpless and waterless, offers flexible and detachable installation based on practical requirements, was proposed. The cooling system removes heat from the chips to the outdoors via two thermosiphon loops, where the heat is first transferred to cold plates tightly attached to the chip surfaces, then transferred through heat exchangers attached to the rack side, and finally dissipated outdoors through air cooling. In this study, a two-stage chip-cooling system was constructed and tested in an enthalpy-difference laboratory. The effects of the liquid filling ratio on the thermal resistance, total temperature difference, and fluid flow pattern under different heat loads were investigated. Second, a heat transfer model for analysing thermal resistance networks was constructed, and the proportions of different types of thermal resistance were determined. Third, an energy-saving analysis of the system is established. The experimental results showed that at single chip heat load 200 W and total heat load 2 kW, the heat transfer temperature difference between chips and outdoors is less than 20 °C at extremely high energy efficiency (cooling load factor reaches 0.05 at outdoor temperature 40 °C), which makes it feasible for free cooling throughout the year in most Chinese areas. The annual PUE in Beijing using the proposed system is 1.15, which indicates a 30 % reduction of the total energy consumption of data centres.

Joint optimization of operational cost and carbon emission in multiple data center micro-grids

As the internet data centers are mushrooming, the energy consumption and carbon emission of data centers are increasing rapidly. To cut down the electricity cost and carbon emission of the data centers, we proposed an optimization method to reduce the electricity cost and carbon emission in geo-distributed multiple data centers. In the proposed method, the carbon tax is considered in the overall operation cost to optimize the carbon emission. The spatial and temporal flexibility of computational workload is fully utilized by considering the difference in renewable energy power output, local electricity and carbon emission of multiple geo-distributed data centers to achieve a better performance. Furthermore, the nonlinear characteristics of the power loss of uninterruptible power supply (UPS) are considered in the optimization. To verify the proposed optimization method, simulation of six cases is carried out with realistic data, and results have proved the proposed method can reduce the operational costs by 4.93%–12.7% and decrease carbon emissions by up to 10%.

Exploring the Awareness Outcomes of Educating ICT Students on the Environmental Implications of e-Waste Recycling and Energy Consumption in Data Centers

Exploring the Awareness Outcomes of Educating ICT Students on the Environmental Implications of e-Waste Recycling and Energy Consumption in Data Centers

Smart traffic routing and service allocation strategy to reduce water consumption in data centers through power reduction

Due to the growth of communication networks, energy consumption in information and communication technology industries is increasing dramatically. Among these industries, data centers are operating with a large number of processors and other components, which, due to heavy processing and mass data transmission, in addition to consuming high electrical power, also cause high thermal losses. Since water cooling systems are used for cooling different parts of these centers as well as for cooling the electric power generation unit, these centers are among the major consumers of water and electricity resources. In this article, while examining the important factors affecting water consumption in data centers, useful methods are suggested to reduce the consumption of electrical energy and water. Optimizing energy consumption in data centers is possible in three parts: routing, servicing and use of cooling equipment. For all three parts, improvement methods are suggested in this article. For this purpose, an optimization problem is designed and an algorithm is presented to solve it. In the proposed solution, an energy-smart method based on SDN technology is used for routing, virtual machines equipped with the possibility of reducing power consumption in no-load conditions are used for servicing, and two types of water-cooled and air-cooled systems are used for cooling equipment. is used. The simulation results show that depending on the cooling system, the proposed method reduces water consumption between 24% and 32% compared to the case where the proposed solution is not used.

Retracted: Dynamic Combined Optimal Scheduling of Electric Energy and Natural Gas Energy Consumption in Data Center

Retracted: Dynamic Combined Optimal Scheduling of Electric Energy and Natural Gas Energy Consumption in Data Center