期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到6,775篇文章
< 1 2 … 250 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Green scheduling for LLM workloads with model and data reuse across geo-distributed data centers 认领 引用
1
作者 Hao Liu Xiaonyu Hu +3 位作者 Ran Wang Jie Hao Qiang Wu Hongke Zhang 《Digital Communications and Networks》 SCIE EI CSCD 2026年第2期236-251,共16页
The explosive proliferation of Large Language Models(LLMs)imposes significant energy and operational burdens on Geographically Distributed Data Centers(GDDCs),thereby demanding an efficient mechanism for LLMs task sch... The explosive proliferation of Large Language Models(LLMs)imposes significant energy and operational burdens on Geographically Distributed Data Centers(GDDCs),thereby demanding an efficient mechanism for LLMs task scheduling.While prior geo-distributed scheduling methods reduce cost and carbon emissions by exploiting regional heterogeneity,they largely overlook model and data reuse opportunities and the uncertainty of LLM execution times.In this paper,we introduce GCOS,to the best of our knowledge,the first green scheduling framework that incorporates a dual-cache system for both data and models,while jointly optimizing task assignment and cache migration.We firstly propose a dual-cache mechanism that decouples model and data caching to enable fine-grained reuse and minimize redundant transmissions.Subsequently,we propose the Multi-Agent Cache-aware Cooperative Scheduling(MACCS)algorithm,which leverages reinforcement learning to optimize task placement with a focus on minimizing both carbon emissions and cost.Additionally,we design a lightweight execution time predictor,DiPTree,to address the high variability in task execution times.Extensive experiments on real-world datasets demonstrate that GCOS reduces overall cost by up to 92.6%and carbon emissions by 90.3%,significantly outperforming existing baselines. 展开更多
关键词 Large language model Geographically distributed data center Green communication Task scheduling Multi-agent reinforcement learning
暂未订购 下载PDF
Building Accurate Energy-Use Statistics for Data Centers 认领 引用
2
作者 Yong-Zhen Wang Te Han Yi-Ming Wei 《Engineering》 SCIE EI CSCD 2026年第5期336-342,共7页
With the rapid expansion of cloud computing and large-scale artificial intelligence models,building accurate and transparent energy-use statistics for data centers has become a critical challenge for global energy sys... With the rapid expansion of cloud computing and large-scale artificial intelligence models,building accurate and transparent energy-use statistics for data centers has become a critical challenge for global energy systems and climate governance.Existing studies report strikingly divergent estimates of global data center electricity consumption,ranging from 196 to 1200 TW·h in 2020,a more than sixfold difference.Such discrepancies reveal profound uncertainties and structural deficiencies in current energy accounting frameworks.Conventional estimation approaches rely heavily on indirect assumptions,proxy indicators,or highly aggregated regional and national statistics,obscuring the true electricity demand of data centers.This lack of statistical transparency distorts energy and carbon accounting,weakens power system planning,and constrains the effective integration of renewable energy with rapidly growing computing demand.This paper highlights that data centers should be treated as a distinct and strategically important end-use energy sector.It emphasizes the need for grid-informed energy registration,enhanced artificial intelligence identification techniques to improve the accuracy and verifiability of energy statistics.Furthermore,the paper emphasizes that policymakers should establish coordinated policy frameworks,enforce standardized energy reporting,and design appropriate incentive mechanisms to encourage data centers to participate in demand response programs and electricity markets,thereby unlocking load flexibility and supporting a secure,low-carbon energy transition. 展开更多
关键词 Data center Energy-use statistics Flexibility AI technologies
暂未订购 下载PDF
Multi-Time Scale Optimization Scheduling of Data Center Considering Workload Shift and Refrigeration Regulation 认领 引用
3
作者 Luyao Liu Xiao Liao +1 位作者 Yiqian Li Shaofeng Zhang 《Energy Engineering》 EI 2026年第2期451-486,共36页
Data center industries have been facing huge energy challenges due to escalating power consumption and associated carbon emissions.In the context of carbon neutrality,the integration of data centers with renewable ene... Data center industries have been facing huge energy challenges due to escalating power consumption and associated carbon emissions.In the context of carbon neutrality,the integration of data centers with renewable energy has become a prevailing trend.To advance the renewable energy integration in data centers,it is imperative to thoroughly explore the data centers’operational flexibility.Computing workloads and refrigeration systems are recognized as two promising flexible resources for power regulationwithin data centermicro-grids.This paper identifies and categorizes delay-tolerant computing workloads into three types(long-running non-interruptible,long-running interruptible,and short-running)and develops mathematical time-shifting models for each.Additionally,this paper examines the thermal dynamics of the computer room and derives a time-varying temperature model coupled to refrigeration power.Building on these models,this paper proposes a two-stage,multi-time scale optimization scheduling framework that jointly coordinates computing workloads time-shift in day-ahead scheduling and refrigeration power control in intra-day dispatch to mitigate renewable variability.A case study demonstrates that the framework effectively enhances the renewable-energy utilization,improves the operational economy of the data center microgrid,and mitigates the impact of renewable power uncertainty.The results highlight the potential of coordinated computing workloads and thermal system flexibility to support greener,more cost-effective data center operation. 展开更多
关键词 Data center renewable energy load shift multi-time scale optimization
暂未订购 下载PDF
Collaborative Scheduling Strategy for Computation and Power among Multiple Data Centers Based on New Energy State Recognition 认领 引用
4
作者 Qian Yang Shenglei Du +3 位作者 Boyang Chen Yalu Sun Ding Li Zhiheng Zhang 《Energy Engineering》 EI 2026年第8期346-373,共28页
In recent years,as the core infrastructure of the digital economy,data centers have witnessed increasingly prominent issues of energy consumption and carbon emissions.To achieve the goals of“carbon peak”and“carbon ... In recent years,as the core infrastructure of the digital economy,data centers have witnessed increasingly prominent issues of energy consumption and carbon emissions.To achieve the goals of“carbon peak”and“carbon neutrality”,data centers have gradually introduced new energy power such as wind and photovoltaic power.However,the randomness and volatility of their output pose challenges to efficient absorption.Based on the spatiotemporal complementary characteristics of new energy output in multiple data centers and the spatiotemporal migration capability of computing tasks,this paper proposes a new energy-aware adaptive collaborative scheduling strategy for computation and power.The strategy first constructs a regionally differentiated load model to accurately depict the characteristic differences among the Jiangsu-Zhejiang-Shanghai mixed computing power hub,the Gansu highefficiency computing power base,and the coastal green computing power nodes.Then,a dual-mode scheduling algorithm based on Lyapunov optimization is designed,integrating a prediction-reaction mechanism to achieve dynamic balance between system stability and new energy absorption rate.Furthermore,a V-parameter adaptive adjustment mechanism and a hierarchical fault-tolerant guarantee system are proposed to cope with new energy fluctuations and improve system robustness.Simulation results show that the proposed strategy achieves an average new energy absorption rate of 62.3%and 52.8%in normal weather and severe weather scenarios,respectively.The carbon emission per unit computing power is reduced by 20.9%,and the computing power-electricity efficiency is improved by 9.1%,which is significantly better than the static scheduling strategy.This verifies its effectiveness and practicability in improving new energy utilization,ensuring service quality,and reducing carbon emissions. 展开更多
关键词 Collaborative scheduling for computation and power data centers Lyapunov optimization V-parameter adjustment mechanism wind-solar volatility
暂未订购 下载PDF
Mountain Science Data Center in IMHE 认领 引用
5
《Journal of Mountain Science》 SCIE CSCD 2026年第3期F0002-F0002,共1页
The Mountain Science Data Center(MSDC),founded in 2021 under the Institute of Mountain Hazards and Environment(IMHE),Chinese Academy of Sciences,manages the entire lifecycle of mountain science data.It integrates data... The Mountain Science Data Center(MSDC),founded in 2021 under the Institute of Mountain Hazards and Environment(IMHE),Chinese Academy of Sciences,manages the entire lifecycle of mountain science data.It integrates data from diverse sources,including debris flows,landslides,soils,ecology,geology,natural resources,basic geographic information,and socio-economic data.The center provides comprehensive services,including data collection,processing,analysis tools,modeling,and application support,offering reliable data backing for numerous research projects within the institute.Data management and services are accessible via the Mountain Science Data Center Portal(http://gffzz69a63cefd7d14bf7s00qq50pvbxpf6ffv.ffgz.tsg.suse.edu.cn/),ensuring long-term,stable,and trustworthy access to facilitate scientific research and institutional development. 展开更多
关键词 application supportoffering soils natural resources debris flows geology debris flowslandslidessoilsecologygeologynatural mountain science data center landslides
暂未订购 下载PDF
Gas Turbine Shortage Could Derail Data Center Expansion 认领 引用
6
作者 Mitch Leslie 《Engineering》 SCIE EI CSCD 2026年第3期3-5,共3页
In August 2025,a 15 m long,318 t Mitsubishi Power M501JAC simple-cycle gas turbine arrived at the Plant Yates electrical power facility near Newnan,GA,USA(Fig.1)[1].Once this turbine and two others like it are install... In August 2025,a 15 m long,318 t Mitsubishi Power M501JAC simple-cycle gas turbine arrived at the Plant Yates electrical power facility near Newnan,GA,USA(Fig.1)[1].Once this turbine and two others like it are installed and operating in 2027,they will allow the plant to generate an additional 1.3 GW of electricity[1]. 展开更多
关键词 gas turbine simple cycle gas turbine electricity generation usa mitsubishi power m jac data center expansion newnan plant expansion
暂未订购 下载PDF
Dynamic geographies and locational factors of techno-environmentally heterogeneous data centers in Chinese cities,2006-2021 认领 引用 被引量：1
7
作者 XU Jili LIU Xiangjie +1 位作者 HUANG Guan YE Yuyao 《Journal of Geographical Sciences》 SCIE CSCD 2025年第9期1845-1862,共18页
Data centers operate as physical digital infrastructure for generating,storing,computing,transmitting,and utilizing massive data and information,constituting the backbone of the flourishing digital economy across the ... Data centers operate as physical digital infrastructure for generating,storing,computing,transmitting,and utilizing massive data and information,constituting the backbone of the flourishing digital economy across the world.Given the lack of a consistent analysis for studying the locational factors of data centers and empirical deficiencies in longitudinal investigations on spatial dynamics of heterogeneous data centers,this paper develops a comprehensive analytical framework to examine the dynamic geographies and locational factors of techno-environmentally heterogeneous data centers across Chinese cities in the period of 2006–2021.First,we develop a“supply-demand-environment trinity”analytical framework as well as an accompanying evaluation indicator system with Chinese characteristics.Second,the dynamic geographies of data centers in Chinese cities over the last decades are characterized as spatial polarization in economically leading urban agglomerations alongside persistent interregional gaps across eastern,central,and western regions.Data centers present dual spatial expansion trajectories featuring outward radiation from eastern core urban agglomerations to adjacent peripheries and leapfrog diffusion to strategic central and western digital infrastructural hubs.Third,it is empirically verified that data center construction in Chinese cities over the last decades has been jointly influenced by supply-,demand-,and environment-side locational factors,echoing the efficacy of the trinity analytical framework.Overall,our findings demonstrate the temporal variance,contextual contingency,and attribute-based differentiation of locational factors underlying techno-environmentally heterogeneous data centers in Chinese cities. 展开更多
关键词 data center digital infrastructure digital economy locational factor China
暂未订购 下载PDF
DSP-free coherent receivers in frequency-synchronous optical networks for next-generation data center interconnects 认领 引用 被引量：1
8
作者 Lei Liu Feng Liu +2 位作者 Cheng Peng Bo Xue William Shieh 《Advanced Photonics Nexus》 CSCD 2025年第3期141-148,共8页
Propelled by the rise of artificial intelligence,cloud services,and data center applications,next-generation,low-power,local-oscillator-less,digital signal processing(DSP)-free,and short-reach coherent optical communi... Propelled by the rise of artificial intelligence,cloud services,and data center applications,next-generation,low-power,local-oscillator-less,digital signal processing(DSP)-free,and short-reach coherent optical communication has evolved into an increasingly prominent area of research in recent years.Here,we demonstrate DSP-free coherent optical transmission by analog signal processing in frequency synchronous optical network(FSON)architecture,which supports polarization multiplexing and higher-order modulation formats.The FSON architecture that allows the numerous laser sources of optical transceivers within a data center can be quasi-synchronized by means of a tree-distributed homology architecture.In conjunction with our proposed pilot-tone assisted Costas loop for an analog coherent receiver,we achieve a record dual-polarization 224-Gb/s 16-QAM 5-km mismatch transmission with reset-free carrier phase recovery in the optical domain.Our proposed DSP-free analog coherent detection system based on the FSON makes it a promising solution for next-generation,low-power,and high-capacity coherent data center interconnects. 展开更多
关键词 digital signal processing-free data center interconnect frequency synchronous optical network analog signal processing
暂未订购 下载PDF
Thermo-Hydrodynamic Characteristics of Hybrid Nanofluids for Chip-Level Liquid Cooling in Data Centers: A Review of Numerical Investigations 认领 引用 被引量：1
9
作者 Yifan Li Congzhe Zhu +2 位作者 Zhihan Lyu Bin Yang Thomas Olofsson 《Energy Engineering》 EI 2025年第9期3525-3553,共29页
The growth of computing power in data centers(DCs)leads to an increase in energy consumption and noise pollution of air cooling systems.Chip-level cooling with high-efficiency coolant is one of the promising methods t... The growth of computing power in data centers(DCs)leads to an increase in energy consumption and noise pollution of air cooling systems.Chip-level cooling with high-efficiency coolant is one of the promising methods to address the cooling challenge for high-power devices in DCs.Hybrid nanofluid(HNF)has the advantages of high thermal conductivity and good rheological properties.This study summarizes the numerical investigations of HNFs in mini/micro heat sinks,including the numerical methods,hydrothermal characteristics,and enhanced heat transfer technologies.The innovations of this paper include:(1)the characteristics,applicable conditions,and scenarios of each theoretical method and numerical method are clarified;(2)the molecular dynamics(MD)simulation can reveal the synergy effect,micro motion,and agglomeration morphology of different nanoparticles.Machine learning(ML)presents a feasiblemethod for parameter prediction,which provides the opportunity for the intelligent regulation of the thermal performance of HNFs;(3)the HNFs flowboiling and the synergy of passive and active technologies may further improve the overall efficiency of liquid cooling systems in DCs.This review provides valuable insights and references for exploring the multi-phase flow and heat transport mechanisms of HNFs,and promoting the practical application of HNFs in chip-level liquid cooling in DCs. 展开更多
关键词 Data centers chip-level liquid cooling hybrid nanofluid energy transport characteristic hydrodynamic performance numerical investigation
暂未订购 下载PDF
A Newly Established Air Pollution Data Center in China 认领 引用
10
作者 Mei ZHENG Tianle ZHANG +11 位作者 Yaxin XIANG Xiao TANG Yinan WANG Guannan GENG Yuying WANG Yingjun LIU Chunxiang YE Caiqing YAN Yingjun CHEN Jiang ZHU Qiang ZHANG Tong ZHU 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2025年第4期597-604,共8页
Air pollution in China covers a large area with complex sources and formation mechanisms,making it a unique place to conduct air pollution and atmospheric chemistry research.The National Natural Science Foundation of ... Air pollution in China covers a large area with complex sources and formation mechanisms,making it a unique place to conduct air pollution and atmospheric chemistry research.The National Natural Science Foundation of China’s Major Research Plan entitled“Fundamental Researches on the Formation and Response Mechanism of the Air Pollution Complex in China”(or the Plan)has funded 76 research projects to explore the causes of air pollution in China,and the key processes of air pollution in atmospheric physics and atmospheric chemistry.In order to summarize the abundant data from the Plan and exhibit the long-term impacts domestically and internationally,an integration project is responsible for collecting the various types of data generated by the 76 projects of the Plan.This project has classified and integrated these data,forming eight categories containing 258 datasets and 15 technical reports in total.The integration project has led to the successful establishment of the China Air Pollution Data Center(CAPDC)platform,providing storage,retrieval,and download services for the eight categories.This platform has distinct features including data visualization,related project information querying,and bilingual services in both English and Chinese,which allows for rapid searching and downloading of data and provides a solid foundation of data and support for future related research.Air pollution control in China,especially in the past decade,is undeniably a global exemplar,and this data center is the first in China to focus on research into the country’s air pollution complex. 展开更多
关键词 air pollution data center platform multi-source data China
暂未订购 下载PDF
Swarm learning anomaly detection framework for cloud data center using multi-channel BiWGAN-GTN and CEEMDAN 认领 引用
11
作者 Lun Tang Yuchen Zhao +4 位作者 Chengcheng Xue Zhiwei Jiang Wei Zou Yanping Liang Qianbin Chen 《Digital Communications and Networks》 SCIE EI CSCD 2025年第6期1883-1896,共14页
Anomaly detection is an important task for maintaining the performance of cloud data center.Traditional anomaly detection primarily examines individual Virtual Machine(VM)behavior,neglecting the impact of interactions... Anomaly detection is an important task for maintaining the performance of cloud data center.Traditional anomaly detection primarily examines individual Virtual Machine(VM)behavior,neglecting the impact of interactions among multiple VMs on Key Performance Indicator(KPI)data,e.g.,memory utilization.Furthermore,the nonstationarity,high complexity,and uncertain periodicity of KPI data in VM also bring difficulties to deep learningbased anomaly detection tasks.To settle these challenges,this paper proposes MCBiWGAN-GTN,a multi-channel semi-supervised time series anomaly detection algorithm based on the Bidirectional Wasserstein Generative Adversarial Network with Graph-Time Network(BiWGAN-GTN)and the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN).(a)The BiWGAN-GTN algorithm is proposed to extract spatiotemporal information from data.(b)The loss function of BiWGAN-GTN is redesigned to solve the abnormal data intrusion problem during the training process.(c)MCBiWGAN-GTN is designed to reduce data complexity through CEEMDAN for time series decomposition and utilizes BiWGAN-GTN to train different components.(d)To adapt the proposed algorithm for the entire cloud data center,a cloud data center anomaly detection framework based on Swarm Learning(SL)is designed.The evaluation results on a real-world cloud data center dataset show that MCBiWGAN-GTN outperforms the baseline,with an F1-score of 0.96,an accuracy of 0.935,a precision of 0.954,a recall of 0.967,and an FPR of 0.203.The experiments also verify the stability of MCBiWGAN-GTN,the impact of parameter configurations,and the effectiveness of the proposed SL framework. 展开更多
关键词 Cloud data center Anomaly detection Bi WGAN-GTN Time series decomposition Swarm learning
暂未订购 下载PDF
Estimating the carbon emission reduction potential of using carbonoriented demand response for data centers:A case study in China 认领 引用
12
作者 Bojun Du Hongyang Jia +3 位作者 Yaowang Li Ershun Du Ning Zhang Dong Liang 《iEnergy》 EI 2025年第1期54-64,共11页
The rapid advancement of artificial intelligence(AI)has significantly increased the computational load on data centers.AI-related computational activities consume considerable electricity and result in substantial car... The rapid advancement of artificial intelligence(AI)has significantly increased the computational load on data centers.AI-related computational activities consume considerable electricity and result in substantial carbon emissions.To mitigate these emissions,future data centers should be strategically planned and operated to fully utilize renewable energy resources while meeting growing computational demands.This paper aims to investigate how much carbon emission reduction can be achieved by using a carbonoriented demand response to guide the optimal planning and operation of data centers.A carbon-oriented data center planning model is proposed that considers the carbon-oriented demand response of the AI load.In the planning model,future operation simulations comprehensively coordinate the temporal‒spatial flexibility of computational loads and the quality of service(QoS).An empirical study based on the proposed models is conducted on real-world data from China.The results from the empirical analysis show that newly constructed data centers are recommended to be built in Gansu Province,Ningxia Hui Autonomous Region,Sichuan Province,Inner Mongolia Autonomous Region,and Qinghai Province,accounting for 57%of the total national increase in server capacity.33%of the computational load from Eastern China should be transferred to the West,which could reduce the overall load carbon emissions by 26%. 展开更多
关键词 Data center temporal and spatial flexibility carbon-oriented demand response carbon reduction planning and operation simulation
暂未订购 下载PDF
A Traffic Scheduling Strategy in SDN Data Center Based on Fibonacci Tree Optimization Algorithm 认领 引用
13
作者 Wang Yaomin Hu Ping +3 位作者 Zeng Jing Li Donghong Yuan Lu Long Hua 《China Communications》 SCIE EI CSCD 2025年第11期176-191,共16页
To improve the traffic scheduling capability in operator data center networks,an analysis prediction and online scheduling mechanism(APOS)is designed,considering both the network structure and the network traffic in t... To improve the traffic scheduling capability in operator data center networks,an analysis prediction and online scheduling mechanism(APOS)is designed,considering both the network structure and the network traffic in the operator data center.Fibonacci tree optimization algorithm(FTO)is embedded into the analysis prediction and the online scheduling stages,the FTO traffic scheduling strategy is proposed.By taking the global optimal and the multi-modal optimization advantage of FTO,the traffic scheduling optimal solution and many suboptimal solutions can be obtained.The experiment results show that the FTO traffic scheduling strategy can schedule traffic in data center networks reasonably,and improve the load balancing in the operator data center network effectively. 展开更多
关键词 Fibonacci tree optimization algorithm(FTO) multi-modal optimization SDN data center traffic scheduling
暂未订购 下载PDF
Numerical Investigation on Air Distribution of Cabinet with Backplane Air Conditioning in Data Center 认领 引用
14
作者 Yiming Rongyang Chengyu Ji +2 位作者 Xiangdong Ding Jun Gao Jianjian Wei 《Frontiers in Heat and Mass Transfer》 EI CAS 2025年第2期685-701,共17页
The effect of gradient exhaust strategy and blind plate installation on the inhibition of backflow and thermal stratification in data center cabinets is systematically investigated in this study through numericalmetho... The effect of gradient exhaust strategy and blind plate installation on the inhibition of backflow and thermal stratification in data center cabinets is systematically investigated in this study through numericalmethods.The validated Re-Normalization Group(RNG)k-ε turbulence model was used to analyze airflow patterns within cabinet structures equipped with backplane air conditioning.Key findings reveal that server-generated thermal plumes induce hot air accumulation at the cabinet apex,creating a 0.8℃ temperature elevation at the top server’s inlet compared to the ideal situation(23℃).Strategic increases in backplane fan exhaust airflow rates reduce server 1’s inlet temperature from 26.1℃(0%redundancy case)to 23.1℃(40%redundancy case).Gradient exhaust strategies achieve equivalent server temperature performance to uniform exhaust distributions while requiring 25%less redundant airflow.This approach decreases the recirculation ratio from1.52%(uniformexhaust at 15%redundancy)to 0.57%(gradient exhaust at equivalent redundancy).Comparative analyses demonstrate divergent thermal behaviors:in bottom-server-absent configurations,gradient exhaust reduces top server inlet temperatures by 1.6℃vs.uniformexhaust,whereas top-serverabsent configurations exhibit a 1.8℃ temperature increase under gradient conditions.The blind plate implementation achieves a 0.4℃ top server temperature reduction compared to 15%-redundancy uniform exhaust systems without requiring additional airflow redundancy.Partially installed server arrangements with blind plates maintain thermal characteristics comparable to fully populated cabinets.This study validates gradient exhaust and blind plate technologies as effective countermeasures against cabinet-scale thermal recirculation,providing actionable insights for optimizing backplane air conditioning systems in mission-critical data center environments. 展开更多
关键词 Blind plate gradient exhaust computational fluid dynamics backflow data center cabinet thermal buoyancy
暂未订购 下载PDF
Assessing the capacity value of demand flexibility from aggregated small Internet data centers in power distribution systems 认领 引用
15
作者 Bo Zeng Xinzhu Xu Fulin Yang 《Global Energy Interconnection》 EI CSCD 2025年第3期460-473,共14页
With the advent of the digital economy,there has been a rapid proliferation of small-scale Internet data centers(SIDCs).By leveraging their spatiotemporal load regulation potential through data workload balancing,aggr... With the advent of the digital economy,there has been a rapid proliferation of small-scale Internet data centers(SIDCs).By leveraging their spatiotemporal load regulation potential through data workload balancing,aggregated SIDCs have emerged as promising demand response(DR)resources for future power distribution systems.This paper presents an innovative framework for assessing capacity value(CV)by aggregating SIDCs participating in DR programs(SIDC-DR).Initially,we delineate the concept of CV tailored for aggregated SIDC scenarios and establish a metric for the assessment.Considering the effects of the data load dynamics,equipment constraints,and user behavior,we developed a sophisticated DR model for aggregated SIDCs using a data network aggregation method.Unlike existing studies,the proposed model captures the uncertainties associated with end tenant decisions to opt into an SIDC-DR program by utilizing a novel uncertainty modeling approach called Z-number formulation.This approach accounts for both the uncertainty in user participation intentions and the reliability of basic information during the DR process,enabling high-resolution profiling of the SIDC-DR potential in the CV evaluation.Simulation results from numerical studies conducted on a modified IEEE-33 node distribution system confirmed the effectiveness of the proposed approach and highlighted the potential benefits of SIDC-DR utilization in the efficient operation of future power systems. 展开更多
关键词 Aggregated Small internet data center Demand response Capacity value Z-number
暂未订购 下载PDF
More Data Centers May Boast Their Own Power Plants 认领 引用 被引量：1
16
作者 Mitch Leslie 《Engineering》 SCIE EI CSCD 2025年第9期3-5,共3页
Most data centers currently tap into existing power grids to draw the immense amount of electricity they need to operate.But many of the data centers that Google(Mountain View,CA,USA)plans to open in the next few year... Most data centers currently tap into existing power grids to draw the immense amount of electricity they need to operate.But many of the data centers that Google(Mountain View,CA,USA)plans to open in the next few years will boast their own power plants,an arrangement known as colocation[1].Under an agreement announced in December 2024,the company will site data centers in industrial parks where its partner Intersect Power of Houston,TX,USA,has installed clean power facilities[1,2].The first of these complexes is scheduled to come online in 2026[1]. 展开更多
关键词 intersect power power plants colocation data centers clean power facilities google power grids
暂未订购 下载PDF
A dynamic load-balanced scheme based on flow classification for software-defined data center 认领 引用
17
作者 YU Yang JING Zhengjun +1 位作者 CHEN Qianmin WANG Xiumei 《High Technology Letters》 EI CAS 2025年第4期338-346,共9页
Aiming at the problems such as low throughput and unbalanced load of data center network caused by traditional multipath routing strategy,a dynamic load balancing strategy for flow classification oriented to Fat-Tree ... Aiming at the problems such as low throughput and unbalanced load of data center network caused by traditional multipath routing strategy,a dynamic load balancing strategy for flow classification oriented to Fat-Tree topology based on the software defined network(SDN)architecture is proposed,named DLB-FC.Multi-index evaluation methods such as link state information and network traffic characteristics are considered.DLB-FC mechanism can dynamically adjust the flow classification threshold to differentiate between large and small flows.The scheme selects different forwarding paths to meet the transmission performance requirements of different flow characteristics.On this basis,an SDN simulation platform is built for performance testing.The simulation results show that DLB-FC algorithm can dynamically distinguish large flows from small flows and achieve load balancing effectively.Compared with equal-cost multi-path(ECMP),global first fit(GFF)and minmum total delay load routing(MTDLR)algorithms,DLB-FC scheme improves the network throughput and link utilization of the data center network effectively.The transmission delay is also reduced with better load balance. 展开更多
关键词 data center software defined network load balance multi-index evaluation Fat-Tree
暂未订购 下载PDF
National Population Health Data Center 认领 引用
18
《Chinese Medical Sciences Journal》 CAS CSCD 2025年第1期F0003-F0003,共1页
National Population Health Data Center(NPHDC)is one of China's 20 national-level science data centers,jointly designated by the Ministry of Science and Technology and the Ministry of Finance.Operated by the Chines... National Population Health Data Center(NPHDC)is one of China's 20 national-level science data centers,jointly designated by the Ministry of Science and Technology and the Ministry of Finance.Operated by the Chinese Academy of Medical Sciences under the oversight of the National Health Commission,NPHDC adheres to national regulations including the Scientific Data Management Measures and the National Science and Technology Infrastructure Service Platform Management Measures,and is committed to collecting,integrating,managing,and sharing biomedical and health data through openaccess platform,fostering open sharing and engaging in international cooperation. 展开更多
关键词 science technology infrastructure population health data open access international cooperation national population health data center scientific data management biomedical data health data
暂未订购 下载PDF
Construction Technology of Electrical Projects in Data Centers 认领 引用
19
作者 Xiaoyong Zhong 《Journal of Electronic Research and Application》 2025年第7期235-240,共6页
With the continuous development of big data technology in China,large-scale enterprises and financial institutions have established data center computer rooms due to the need to process massive amounts of data.Due to ... With the continuous development of big data technology in China,large-scale enterprises and financial institutions have established data center computer rooms due to the need to process massive amounts of data.Due to the electrical system of this engineering project is relatively complex,the application of construction technology will directly affect the subsequent construction quality and the safety of the data center.Therefore,this paper studies the construction technology of data center electrical engineering projects.Based on a simple analysis of the design and construction content of data center electrical projects,it discusses the key points of preparatory work.At the same time,it analyzes the difficulties and key points in multiple aspects such as wire piping,distribution box installation,and lamp installation during the installation and construction process of data center electrical projects. 展开更多
关键词 Data center Electrical project Construction technology
暂未订购 下载PDF
AI-Based Power Distribution Optimization in Hyperscale Data Centers 认领 引用
20
作者 Chirag Devendrakumar Parikh 《Journal on Artificial Intelligence》 2025年第1期571-584,共14页
With the increasing complexity and scale of hyperscale data centers,the requirement for intelligent,real-time power delivery has never been more critical to ensure uptime,energy efficiency,and sustainability.Those tec... With the increasing complexity and scale of hyperscale data centers,the requirement for intelligent,real-time power delivery has never been more critical to ensure uptime,energy efficiency,and sustainability.Those techniques are typically static,reactive(since CPU and workload scaling is applied to performance events that occur after a request has been submitted,and is thus can be classified as a reactive response.),and require manual operation,and cannot cope with the dynamic nature of the workloads,the distributed architectures as well as the non-uniform energy sources in today’s data centers.In this paper,we elaborate on how artificial intelligence(AI)is revolutionizing power distribution in hyperscale data centers,making predictive load forecasting,real-time fault detection,and autonomous power optimization possible.We explain how ML(machine learning)and RL(reinforcement learning)-based models have been introduced in PDN(power delivery networks)for load balancing in three-phase systems,overprovisioning reduction,and energy flow optimization from the grid to the rack.The paper considers the architectural pieces of the AI-led systems,such as data ingestion pipelines,anomaly detection frameworks,and control algorithms to manage the power switching,cooling synchronization,and grid/microgrid interaction.Practical use cases show the value of these systems on PUE,infrastructure reliability,and environmental footprint.Key implementation challenges,including data quality,legacy systemintegration,and AI decision-making governance,are also discussed.Last,the paper speculates on the future of autonomous DC power infrastructure where AI becomes not only an assistive resource to the operator but really takes control over infrastructure behavior end-to-end,from procuring energy,to phase balancing,to predicting maintenance.Integrating technology innovation with operational sustainability,AI-powered power distribution is emerging as a core competence for the Smart Digital Power Facility of the Future. 展开更多
关键词 Artificial intelligence(AI) machine learning optimization power distribution management hyperscale data centers energy efficiency in computing infrastructure load forecasting and balancing sustainable computing
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 250 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈