Energy Consumption Of Data Centers Research Articles

Improving energy efficiency and fault tolerance of mission-critical cloud task scheduling: A mixed-integer linear programming approach

Cloud services have become indispensable in critical sectors such as healthcare, drones, digital twins, and autonomous vehicles, providing essential infrastructure for data processing and real-time analytics. These systems operate across multiple layers, including edge, fog, and cloud, requiring efficient resource management to ensure reliability and energy efficiency. However, increasing computational demands have led to rising energy consumption and frequent faults in cloud data centers. Inefficient task scheduling exacerbates these issues, causing resource overutilization, execution delays, and redundant processing. Current approaches struggle to optimize energy consumption, execution time, and fault tolerance simultaneously. While some methods offer partial solutions, they suffer from high computational complexity and fail to effectively balance the workloads or manage redundancy. Therefore, a comprehensive task scheduling solution is needed for mission-critical applications. In this article, we introduce a novel scheduling algorithm based on Mixed Integer Linear Programming (MILP) that optimizes task allocation across edge, fog, and cloud environments. Our solution reduces energy consumption, execution time, and failure rates while ensuring balanced distribution of computational loads across virtual machines. Additionally, it incorporates a fault tolerance mechanism that reduces the overlap between primary and backup tasks by distributing them across multiple availability zones. The scheduler’s efficiency is further enhanced by a custom-designed heuristic, ensuring scalability and practical applicability. The proposed MILP-based scheduler demonstrates significant average improvements over the best state-of-the-art algorithms evaluated. It achieves a 9.63% increase in task throughput, reduces energy consumption by 18.20%, shortens execution times by 9.35%, and lowers failure probabilities by 11.50% across all layers of the distributed cloud system. These results highlight the scheduler’s effectiveness in addressing key challenges in energy-efficient and reliable cloud computing for mission-critical applications.

Energy-efficient joint performance optimization of cloud data centre users/operator using memetic algorithm

ABSTRACT The use of cloud computing as a cost-effective and flexible method has currently drawn the attention of cloud service providers. When offering various cloud services to users, a certain level of Service Level Agreement (SLA) must be guaranteed by the cloud operator, based on the service received at the user level. Considering the non-linear dependency of network users’ Quality of Experience (QoE) on cloud resources (RAM, disk memory, network bandwidth, and CPU core usage) as well as the non-linear and time-varying dependency of cloud operator efficiency on the level of resources consumed by users, joint optimization of user experience and cloud operator efficiency poses significant computational challenges. This paper focuses on the green optimization of both user experience and the benefits of cloud data centre operators, employing a metaheuristic algorithm, which essentially combines a genetic algorithm with an innovative approach. In this approach, the efficiency of the cloud data centre operator, energy consumption of data centres, and user experience quality are jointly optimized. Simulation results demonstrate the improvement of the combined user/operator utility function compared to traditional methods, considering energy consumption constraints.

Energy-efficient Cloud Infrastructure for IoT Device Management: A Comprehensive Analysis of Edge-Cloud Workload Distribution Strategies

The rapid expansion of the Internet of Things (IoT) has led to significant increases in data traffic and computational demands on cloud infrastructures, raising concerns about energy consumption in data centers. This article explores innovative approaches to creating energy-efficient cloud infrastructures for managing IoT device fleets through optimized workload distribution between edge devices and cloud resources. It proposes implementing energy-aware algorithms that dynamically determine the optimal location for data processing based on energy consumption, latency requirements, and workload characteristics. The system employs advanced machine learning techniques for workload prediction and resource allocation, demonstrating substantial improvements in energy efficiency while maintaining high-performance standards. Case studies in smart cities, agricultural monitoring, and transportation networks validate the effectiveness of this approach in real-world scenarios. The results indicate that intelligent workload distribution across edge and cloud platforms can significantly reduce energy consumption while enhancing system performance and operational efficiency, providing a sustainable pathway for large-scale IoT deployments.

Optimizing data center energy consumption via energy complementarity scheduling

Considering the degree of flexible energy supply and demand matching, four types of energy sources were combined and a dynamic multi-energy combination optimization scheduling model was established for data centers to realize the optimal scheduling of multiple energy sources to meet the minimum overall energy consumption of data centers. Model construction and simulation analysis were used to conduct the research. To maximize the use of wind power and photovoltaic renewable energy, a multi-objective optimization scheduling model was constructed with the objectives of maximum flexibility of supply and demand matching in a single day, minimum penalty for predicted photovoltaic power consumption, minimum average service time of data centers, and minimum overall energy consumption of data centers. The NSGA-III framework was used to solve the constructed model. The results showed that even in areas rich in renewable energy, because of the limited power of renewable energy generation, the purchase of electric energy and natural gas as the main energy source remained necessary to ensure the stable operation of data centers. Data centers can use the complementarity of photovoltaic and wind power generation times to achieve basic complementarity of power generation. In terms of cooling energy consumption, natural gas waste heat-driven absorption refrigerator output accounted for a significant proportion, approximately twice that of the electric refrigerator and could effectively reduce the energy consumption of electric refrigeration. The simulation results of energy scheduling model show that it is necessary to increase the proportion of natural gas and green energy in data center energy supply, maximize the use of renewable energy such as wind power and photovoltaic power generation, give full play to the characteristics of wind power and photovoltaic power generation time complementarity, and build a green energy complementary data center energy supply mode.

A novel two-stage energy sharing method for data center cluster considering ‘Carbon-Green Certificate’ coupling mechanism

The energy consumption of data centers is on a sharp upward trend, and how to achieve joint optimal scheduling and energy management of multiple data centers is the key to the economic and low-carbon operation of data center prosumers (DCPs). This study introduces shared electric-hydrogen energy storage (SEHES) for DCPs, constructs a data center cluster (DCC) framework, and proposes a two-stage energy sharing model for DCC considering the ‘Carbon-Green Certificate’ coupling mechanism (C-GCCM). First, the DCC framework containing DCC operator (DCCO) and DCPs is established, and the C-GCCM based on conversion coefficients is designed to improve the energy saving and emission reduction level of DCC. In the day-ahead optimization stage, a stochastic multi-objective scheduling model for SEHES considering economy and environmental protection is developed. In the real-time optimization stage, considering the C-GCCM and the difference in on-grid prices, the Stackelberg game model of DCC is constructed according to the internal energy price set by DCCO and the trading strategies of DCPs. Finally, the proposed model is solved using the normal boundary intersection algorithm, the particle swarm algorithm and CPLEX. Simulation results demonstrate that the proposed DCC framework can reduce the daily operating cost of DCPs by 67.05%. The introduction of C-GCCM has resulted in a shift from negative to positive benefits on carbon emission trading for DCC and a 42.33% increase in benefits on green certificate trading.

Study of cooling performances and energy saving potential for separator enhanced thermosiphon/vapor compression hybrid system

With the advancement of technology, the energy consumption of data centers has dramatically increased. Among them, the energy consumption of cooling equipment accounts for more than 40% of the total energy consumption of data centers. To reduce energy consumption and enhance performance, A novel separator enhanced thermosiphon/Vapor compression hybrid cooling system (STPVC) has been proposed. The cooling performances of the STPVC system are assessed through experimental test and comparisons in the conventional hybrid system (CTPVC) are shown in this paper as well. The experimental results indicate that compared to the CTPVC, the STPVC system can achieves a maximum improvement of 30.91% in thermosiphon mode and 19.3% improvement in vapor compression mode Additionally, to evaluate the energy saving potential of the STPVC, the annual energy efficiency ratios (AEER) of the system in various latitude cities of China have been theoretically calculated. A high AEER of the system about 15.3 can be achieved in the high latitude area. Even in the low-latitude region, the system also can gain the value of AEER about 5.45. The results of this paper are expected to provide a direction of performances enhancement and energy consumption reduction of the cooling equipment in the data center.

Techno-economic analysis of combined photovoltaic cells and hydrogen energy systems for data center energy consumption

Techno-economic analysis of combined photovoltaic cells and hydrogen energy systems for data center energy consumption

Experimental investigation on the application of cold-mist direct evaporative cooling in data centers

The rapid development of the internet era has driven the construction of numerous data centers worldwide. In hot climate, data centers consume significant energy for cooling. Cold-mist direct evaporative cooling offers a natural cooling solution that can help reduce this energy consumption. This study investigates the temperature and relative humidity changes of natural high-temperature air after being cooled using a cold-mist direct evaporative cooling test bench. The effects of several control factors such as spray angle, spray flow rate, and air speed on the cold-mist direct evaporative cooling performance was systematically examined. The findings revealed that: (1) the optimal spray angle for cold-mist direct evaporative cooling treatment air is 65°; (2) high-temperature air between 27 °C and 37 °C can be cooled to 23.57 °C – 25.58 °C after cold-mist direct evaporative cooling treatment, with relative humidity levels of 67.0 % – 78.8 %, meeting the air supply requirements for data centers; (3) the proposed approach could reduce the data center energy consumption by 14 % – 41 %, while extending the annual natural cooling period by 3.16 % – 20.45 %.

An energy-environment coupled simulation framework for multi-scale and multi-facet evaluation of data center

This paper focuses on analyzing the thermal environment and optimizing energy consumption in data centers, which has largely omitted from previous studies. The thermal environmental of data centers is simulated and analyzed by utilizing the established “server-rack-room” multi-scale heat transfer numerical model. Based on this, the coupling simulation model of thermal environment and energy consumption in data centers is proposed to explore the relationship between them, and the corresponding optimization strategy is put forward. The energy consumption simulated by energy-environment coupled model and non-coupled model can reach a discrepancy of over 30 %, which indicates that the thermal environment impacts the power consumption of the data center significantly. Besides, the effect of several operational parameters of air conditioning system on the thermal environment and energy consumption of data center is analyzed. Through the particle swarm optimization algorithm, the optimal system parameters, which meet the requirements of thermal environment and lead to the lowest energy consumption, are found for typical cities in different climatic regions. The recommended settings include the supply air temperature of about 24 ∼ 26 ℃, the air supply volume of 8 m3/s, and the temperature difference of about 3.5 ℃ between the supply air temperature and the supply chilled water temperature. Under the optimized parameter setting, the highest temperature of server decreases to below 71 ℃, and the energy saving rate is more than 6 %.

Carbon emission scenario analysis of data centers in China under the carbon neutrality target

The low-carbon transformation of data centers is of great significance to achieve the goals of carbon peaking and carbon neutrality. This study compared and analyzed the overall situation of data centers in China. Based on China's CO2 emission and intensity targets in key years, the four variables of energy efficiency improvement rate, nonfossil energy consumption proportion, negative emission technology intensity, and waste energy utilization rate were introduced, and a net zero emission path model of data centers was established. Using scenario analysis to predict the total CO2 emissions and emission intensity from 2021 to 2060, three emission reduction path scenarios were obtained. Results showed that the energy consumption of data centers increased gradually, the carbon emissions first increased and then decreased, and the power usage effectiveness (PUE) of the data centers decreased gradually. The carbon peak time of the three scenarios is 2030, and the time for carbon neutrality is 2055, 2053, and 2051 in three scenarios. The data center industry should further improve the energy efficiency utilization rate, increase the proportion of nonfossil energy consumption, strengthen the technological innovation of carbon capture and storage, enhance the level of carbon sink, and optimize the utilization rate of waste energy.

Towards Energy-Efficient Data Centers: A Comprehensive Review of Passive and Active Cooling Strategies

With the rapid growth of cloud computing, the number of data centers (DCs) continuously increases, leading to a high-energy consumption dilemma. Cooling, apart from IT equipment, represents the largest energy consumption in DCs. Passive design (PD) and active design (AD) are two important approaches in architectural design to reduce energy consumption. However, for DC cooling, few studies have summarized AD, and there are almost no studies on PD. Based on existing international research (2005-2024), this paper summarizes the current state of cooling strategies for DCs. PD encompasses floors, ceilings, and layout and zoning of racks. Additionally, other passive strategies not yet studied in DCs are critically examined. AD includes air, liquid, free, and two-phase cooling. This paper systematically compares the performance of different AD technologies on various KPIs, including energy, economic, and environmental indicators. This paper also explores the application of different cooling design strategies through best-practice examples and presents advanced algorithms for energy management in operational DCs. This study reveals that free cooling is widely employed, with Artificial Neural Networks emerging as the most popular algorithm for managing cooling energy. Finally, this paper suggests four future directions for reducing cooling energy in DCs, with a focus on the development of passive strategies. This paper provides an overview and guide to DC energy-consumption issues, emphasizes the importance of implementing passive and active design strategies to reduce DC cooling energy consumption, and provides directions and references for future energy-efficient DC designs.

EETS: An energy-efficient task scheduler in cloud computing based on improved DQN algorithm

The huge energy consumption of data centers in cloud computing leads to increased operating costs and high carbon emissions to the environment. Deep Reinforcement Learning (DRL) technology combines of deep learning and reinforcement learning, which has an obvious advantage in solving complex task scheduling problems. Deep Q Network(DQN)-based task scheduling has been employed for objective optimization. However, training the DQN algorithm may result in value overestimation, which can negatively impact the learning effectiveness. The replay buffer technique, while increasing sample utilization, does not distinguish between sample importance, resulting in limited utilization of valuable samples. This study proposes an enhanced task scheduling algorithm based on the DQN framework, which utilizes a more optimized Dueling-network architecture as well as Double DQN strategy to alleviate the overestimation bias and address the shortcomings of DQN. It also incorporates a prioritized experience replay technique to achieve importance sampling of experience data, which overcomes the problem of low utilization due to uniform sampling from replay memory. Based on these improved techniques, we developed an energy-efficient task scheduling algorithm called EETS (Energy-Efficient Task Scheduling). This algorithm automatically learns the optimal scheduling policy from historical data while interacting with the environment. Experimental results demonstrate that EETS exhibits faster convergence rates and higher rewards compared to both DQN and DDQN. In scheduling performance, EETS outperforms other baseline algorithms in key metrics, including energy consumption, average task response time, and average machine working time. Particularly, it has a significant advantage when handling large batches of tasks.

Physics-based machine learning optimization of thermoelectric assembly for maximizing waste heat recovery

With the increasing energy consumption of data centers around the world, the recovery of low-grade waste heat in data centers has become increasingly important. To maximize the waste heat recovery of thermoelectric modules in data centers, physics-based machine learning optimization of the thermoelectric assembly was carried out. A theoretical model and machine learning model of a fan-heat sink-thermoelectric generator system for waste heat recovery were proposed for predicting the performance of a thermoelectric assembly. Genetic algorithms were used to find the best heat sink geometric parameters for the possible greatest power generation. Given a temperature difference constraint of 75 °C between the heat source and the environment, the greatest output power from the thermoelectric assembly was 3.836 (watts), with the optimal plate fin height of 60 (mm), 50 fins, and 1.7 (mm) of fin pitch over a 120 mm × 120 mm area under the determined parameters of the thermoelectric module with a 120 mm cooling fan. In addition, by comparing different machine learning algorithms, the results revealed the superiority of random forest regression in solving optimal problems. This work provides a convenient and practical means for the fast optimization of thermoelectric assembly.

Performance Analysis of Lake Water Cooling Coupled with a Waste Heat Recovery System in the Data Center

Data centers (DCs) require continuous cooling throughout the year and produce a large amount of low-grade waste heat. Free cooling and waste heat recovery techniques are promising approaches to reduce DC energy consumption. Although previous studies have explored diverse waste heat utilization strategies, there is a significant gap in combining waste heat recovery with lake water cooling in DCs. Therefore, this study proposed a system integrating lake water cooling with waste heat recovery for DCs. To evaluate the energy-saving performance of the suggested system, the influence of waste heat recovery locations and volumes has been investigated. An analysis of the improvement in system parameters is also conducted. The study’s findings highlight that targeted recovery of waste heat from sources like chilled water or air in server rooms can significantly reduce the cooling energy demand of the system. The results show that recovering heat from the return air of IT equipment can yield a remarkable power usage effectiveness (PUE) and coefficient of performance (COP) of 1.19 and 10.17, and the energy consumption of the cooling system is reduced to 10.06%. Moreover, the outcomes reveal the potential for substantial energy savings of up to 26.05% within the proposed system by setting the chilled water and air supply temperatures to 16 and 20 °C, respectively.

Model-based control strategy with linear parameter-varying state-space model for rack-based cooling data centers

Cooling system energy consumption occupies approximately 40% of the total energy consumption in data centers, and efficient control is crucial for improving cooling efficiency and reducing the operational costs of data centers. This paper introduced a novel economic model predictive control (EMPC) strategy based on a linear parameter-varying state-space model. The primary objective was to maintain the thermal environment of data centers while minimizing the energy consumption of the cooling system. Therein, a linear parameter-varying state-space model was developed based on the parameter identification method to precisely predict the temperature field of the rack-based cooling data center in real-time. The energy management and thermal regulation performance of the EMPC and the proportion integration differentiation (PID) controller were comparatively analyzed through simulation experiments. Moreover, the impacts of the server workload level and optimization time domain on the performance of EMPC were comprehensively investigated. The results show that, the proposed EMPC was able to minimize the energy consumption by optimizing the supplied cold air temperature and airflow rate simultaneously. In comparison with the PID controller, EMPC not only achieves a 9.7% energy saving by preventing server over-cooling but also diminishes server temperature fluctuations, promoting the safe and stable operation of the server. The research shows that the EMPC reveals excellent performance along with all server workload levels. In addition, the length of the prediction horizon has a notable impact and three minutes is suggested for the rack-based cooling system.

Optimizing a dew point evaporative cooler in data center applications

Abstract The escalating energy consumption of data centers has led to a pressing need for energy-efficient cooling solutions. This paper presents a countercurrent dew point evaporative cooler (DPEC) for data center refrigeration. We developed and experimentally validated a numerical model for DPEC, then formulated regression models using the response surface method. These models link eight key design factors, including geometrical and operational factors, to three performance indices: cooling capacity per unit volume, coefficient of performance, and outlet primary air temperature. We assessed the extent of factor influence on these indices. By using these regression models as objective functions, we used the genetic algorithm for design optimization under two climatic conditions, resulting in various optimal parameter combinations. Our findings highlight the strong predictive accuracy of these models. In comparison to the original design, the optimal design achieved an improvement of 104.8%, an increase of 23.9%, and a reduction of 13.8% in the three indices.

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.

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.

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.

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.