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Littérature scientifique sur le sujet « Data centers management »

Auteur : Grafiati

Publié le 8 mars 2025

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Articles de revues sur le sujet "Data centers management"

El-Sayed, Nosayba, Ioan A. Stefanovici, George Amvrosiadis, Andy A. Hwang et Bianca Schroeder. « Temperature management in data centers ». ACM SIGMETRICS Performance Evaluation Review 40, n^o 1 (7 juin 2012) : 163–74. http://dx.doi.org/10.1145/2318857.2254778.

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ZHANG, Wei, Ying SONG, Li RUAN, Ming-Fa ZHU et Li-Min XIAO. « Resource Management in Internet-Oriented Data Centers ». Journal of Software 23, n^o 2 (6 mars 2012) : 179–99. http://dx.doi.org/10.3724/sp.j.1001.2012.04146.

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Mukherjee, Tridib, Ayan Banerjee, Georgios Varsamopoulos et Sandeep K. S. Gupta. « Model-driven coordinated management of data centers ». Computer Networks 54, n^o 16 (novembre 2010) : 2869–86. http://dx.doi.org/10.1016/j.comnet.2010.08.011.

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TAKANO, Ryousei, et Kuniyasu SUZAKI. « Disaggregated Accelerator Management System for Cloud Data Centers ». IEICE Transactions on Information and Systems E104.D, n^o 3 (1 mars 2021) : 465–68. http://dx.doi.org/10.1587/transinf.2020edl8040.

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Assi, Chadi, Sara Ayoubi, Samir Sebbah et Khaled Shaban. « Towards Scalable Traffic Management in Cloud Data Centers ». IEEE Transactions on Communications 62, n^o 3 (mars 2014) : 1033–45. http://dx.doi.org/10.1109/tcomm.2014.012614.130747.

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Kumar, Sanjay, Vanish Talwar, Vibhore Kumar, Parthasarathy Ranganathan et Karsten Schwan. « Loosely coupled coordinated management in virtualized data centers ». Cluster Computing 14, n^o 3 (7 mars 2010) : 259–74. http://dx.doi.org/10.1007/s10586-010-0124-9.

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Chu, Wen-Xiao, et Chi-Chuan Wang. « A review on airflow management in data centers ». Applied Energy 240 (avril 2019) : 84–119. http://dx.doi.org/10.1016/j.apenergy.2019.02.041.

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Wu, H., A. N. Tantawi, Y. Diao et W. Wang. « Adaptive memory load management in cloud data centers ». IBM Journal of Research and Development 55, n^o 6 (novembre 2011) : 5:1–5:10. http://dx.doi.org/10.1147/jrd.2011.2170869.

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Mishra, Mayank, et Umesh Bellur. « Unified resource management in cloud based data centers ». CSI Transactions on ICT 5, n^o 4 (17 avril 2017) : 361–74. http://dx.doi.org/10.1007/s40012-017-0168-6.

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Sunday Adeola Oladosu, Adebimpe Bolatito Ige, Christian Chukwuemeka Ike, Peter Adeyemo Adepoju, Olukunle Oladipupo Amoo et Adeoye Idowu Afolabi. « Revolutionizing data center security : Conceptualizing a unified security framework for hybrid and multi-cloud data centers ». Open Access Research Journal of Science and Technology 5, n^o 2 (30 août 2022) : 086–76. https://doi.org/10.53022/oarjst.2022.5.2.0065.

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The rapid shift towards hybrid and multi-cloud environments has introduced significant security challenges for data centers, as traditional security models struggle to meet the demands of modern infrastructures. This review conceptualizes a unified security framework aimed at revolutionizing data center security in the context of hybrid and multi-cloud architectures. The proposed framework integrates on-premise and cloud security controls into a cohesive, scalable solution that addresses the complexities of modern data centers, ensuring robust protection against increasingly sophisticated cyber threats. At the core of the framework is a centralized security management platform that enables real-time monitoring, policy enforcement, and incident response across diverse environments. The integration of Zero Trust Architecture ensures that security is applied rigorously, with continuous authentication and authorization for all access requests, irrespective of the user's location. Additionally, the framework leverages artificial intelligence (AI) and machine learning (ML) to enhance threat detection and response capabilities. AI-driven analytics enable the identification of anomalous activities, vulnerability scanning, and predictive threat intelligence, offering faster and more accurate responses to emerging security threats. The framework also emphasizes data protection through advanced encryption methods, securing sensitive information both in transit and at rest across hybrid and multi-cloud environments. Automated compliance management tools ensure that data centers remain compliant with industry standards and regulations, such as GDPR and CCPA, through continuous monitoring and real-time auditing. By incorporating automation, the framework reduces operational complexity, minimizing human error and ensuring consistency in policy enforcement across various platforms. This unified security framework promises to enhance the security posture of hybrid and multi-cloud data centers, reduce operational overhead, and improve compliance management, ultimately providing organizations with a scalable, adaptable, and proactive solution for safeguarding their digital infrastructure in an increasingly complex cyber landscape.

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Thèses sur le sujet "Data centers management"

Tudoran, Radu-Marius. « High-Performance Big Data Management Across Cloud Data Centers ». Electronic Thesis or Diss., Rennes, École normale supérieure, 2014. http://www.theses.fr/2014ENSR0004.

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La puissance de calcul facilement accessible offerte par les infrastructures clouds, couplés à la révolution du "Big Data", augmentent l'échelle et la vitesse auxquelles l'analyse des données est effectuée. Les ressources de cloud computing pour le calcul et le stockage sont répartis entre plusieurs centres de données de par le monde. Permettre des transferts de données rapides devient particulièrement important dans le cadre d'applications scientifiques pour lesquels déplacer le traitement proche de données est coûteux voire impossible. Les principaux objectifs de cette thèse consistent à analyser comment les clouds peuvent devenir "Big Data - friendly", et quelles sont les meilleures options pour fournir des services de gestion de données aptes à répondre aux besoins des applications. Dans cette thèse, nous présentons nos contributions pour améliorer la performance de la gestion de données pour les applications exécutées sur plusieurs centres de données géographiquement distribués. Nous commençons avec les aspects concernant l'échelle du traitement de données sur un site, et poursuivons avec le développements de solutions de type MapReduce permettant la distribution des calculs entre plusieurs centres. Ensuite, nous présentons une architecture de service de transfert qui permet d'optimiser le rapport coût-performance des transferts. Ce service est exploité dans le contexte de la diffusion de données en temps-réel entre des centres de données de clouds. Enfin, nous étudions la viabilité, pour une fournisseur de cloud, de la solution consistant à intégrer cette architecture comme un service basé sur un paradigme de tarification flexible, qualifiée de "Transfert-as-a-Service"
The easily accessible computing power offered by cloud infrastructures, coupled with the "Big Data" revolution, are increasing the scale and speed at which data analysis is performed. Cloud computing resources for compute and storage are spread across multiple data centers around the world. Enabling fast data transfers becomes especially important in scientific applications where moving the processing close to data is expensive or even impossible. The main objectives of this thesis are to analyze how clouds can become "Big Data - friendly", and what are the best options to provide data management services able to meet the needs of applications. In this thesis, we present our contributions to improve the performance of data management for applications running on several geographically distributed data centers. We start with aspects concerning the scale of data processing on a site, and continue with the development of MapReduce type solutions allowing the distribution of calculations between several centers. Then, we present a transfer service architecture that optimizes the cost-performance ratio of transfers. This service is operated in the context of real-time data streaming between cloud data centers. Finally, we study the viability, for a cloud provider, of the solution consisting in integrating this architecture as a service based on a flexible pricing paradigm, qualified as "Transfer-as-a-Service"

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Mahmud, A. S. M. Hasan. « Sustainable Resource Management for Cloud Data Centers ». FIU Digital Commons, 2016. http://digitalcommons.fiu.edu/etd/2634.

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In recent years, the demand for data center computing has increased significantly due to the growing popularity of cloud applications and Internet-based services. Today's large data centers host hundreds of thousands of servers and the peak power rating of a single data center may even exceed 100MW. The combined electricity consumption of global data centers accounts for about 3% of worldwide production, raising serious concerns about their carbon footprint. The utility providers and governments are consistently pressuring data center operators to reduce their carbon footprint and energy consumption. While these operators (e.g., Apple, Facebook, and Google) have taken steps to reduce their carbon footprints (e.g., by installing on-site/off-site renewable energy facility), they are aggressively looking for new approaches that do not require expensive hardware installation or modification. This dissertation focuses on developing algorithms and systems to improve the sustainability in data centers without incurring significant additional operational or setup costs. In the first part, we propose a provably-efficient resource management solution for a self-managed data center to cap and reduce the carbon emission while maintaining satisfactory service performance. Our solution reduces the carbon emission of a self-managed data center to net-zero level and achieves carbon neutrality. In the second part, we consider minimizing the carbon emission in a hybrid data center infrastructure that includes geographically distributed self-managed and colocation data centers. This segment identifies and addresses the challenges of resource management in a hybrid data center infrastructure and proposes an efficient distributed solution to optimize the workload and resource allocation jointly in both self-managed and colocation data centers. In the final part, we explore sustainable resource management from cloud service users' point of view. A cloud service user purchases computing resources (e.g., virtual machines) from the service provider and does not have direct control over the carbon emission of the service provider's data center. Our proposed solution encourages a user to take part in sustainable (both economical and environmental) computing by limiting its spending on cloud resource purchase while satisfying its application performance requirements.

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Le, Tuan Anh. « Workload prediction for resource management in data centers ». Thesis, Umeå universitet, Institutionen för datavetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-124985.

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Resource management is to arrange and allocate resources for computing operations and applications. In large scale data centers that contain thousands of servers, resource management is critical for efficient operation. To know workload characteristics in advance helps us proactively control resources in data centers, leading to benefits such as power savings and improved service performance. Workload prediction can be used, e.g., to decide how many resources to allocate for each application in a data center in the future. The accuracy of workload prediction varies depending on the used prediction methods and the characteristics of the workload. In this thesis work, we investigate three different methods: Linear Regression (LR), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Nonlinear Autoregressive Network with Exogenous Inputs (NARX). These methods are used to build models of resource consumption such as memory, CPU, and disk. Based on these models, future workload resource usage is predicted, and the accuracy of prediction is assessed. We analyze a trace from a production cluster at Google, predict resource consumption for different time intervals, and compute the error between predicted and actual values. The results show that NARX gives higher accuracy than ANFIS and LR when forecasting one-step ahead prediction, and that the ANFIS method provides the best result with multi-step ahead prediction compared to the others. Finally, time to train and re-train LR, ANFIS and NARX are computed. The running times are short, suggesting that the methods can be used in real-time operation.

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Lu, Lei. « Effective Resource and Workload Management in Data Centers ». W&M ScholarWorks, 2014. https://scholarworks.wm.edu/etd/1539623637.

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The increasing demand for storage, computation, and business continuity has driven the growth of data centers. Managing data centers efficiently is a difficult task because of the wide variety of datacenter applications, their ever-changing intensities, and the fact that application performance targets may differ widely. Server virtualization has been a game-changing technology for IT, providing the possibility to support multiple virtual machines (VMs) simultaneously. This dissertation focuses on how virtualization technologies can be utilized to develop new tools for maintaining high resource utilization, for achieving high application performance, and for reducing the cost of data center management.;For multi-tiered applications, bursty workload traffic can significantly deteriorate performance. This dissertation proposes an admission control algorithm AWAIT, for handling overloading conditions in multi-tier web services. AWAIT places on hold requests of accepted sessions and refuses to admit new sessions when the system is in a sudden workload surge. to meet the service-level objective, AWAIT serves the requests in the blocking queue with high priority. The size of the queue is dynamically determined according to the workload burstiness.;Many admission control policies are triggered by instantaneous measurements of system resource usage, e.g., CPU utilization. This dissertation first demonstrates that directly measuring virtual machine resource utilizations with standard tools cannot always lead to accurate estimates. A directed factor graph (DFG) model is defined to model the dependencies among multiple types of resources across physical and virtual layers.;Virtualized data centers always enable sharing of resources among hosted applications for achieving high resource utilization. However, it is difficult to satisfy application SLOs on a shared infrastructure, as application workloads patterns change over time. AppRM, an automated management system not only allocates right amount of resources to applications for their performance target but also adjusts to dynamic workloads using an adaptive model.;Server consolidation is one of the key applications of server virtualization. This dissertation proposes a VM consolidation mechanism, first by extending the fair load balancing scheme for multi-dimensional vector scheduling, and then by using a queueing network model to capture the service contentions for a particular virtual machine placement.

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Sarker, Tusher Kumer. « Cost-efficient virtual machine management in data centers ». Thesis, Queensland University of Technology, 2016. https://eprints.qut.edu.au/94743/1/Tusher%20Kumer_Sarker_Thesis.pdf.

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Virtual Machine (VM) management is an obvious need in today's data centers for various management activities and is accomplished in two phases— finding an optimal VM placement plan and implementing that placement through live VM migrations. These phases result in two research problems— VM placement problem (VMPP) and VM migration scheduling problem (VMMSP). This research proposes and develops several evolutionary algorithms and heuristic algorithms to address the VMPP and VMMSP. Experimental results show the effectiveness and scalability of the proposed algorithms. Finally, a VM management framework has been proposed and developed to automate the VM management activity in cost-efficient way.

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Rincon, Mateus Cesar Augusto. « Dynamic resource allocation for energy management in data centers ». [College Station, Tex. : Texas A&M University, 2008. http://hdl.handle.net/1969.1/ETD-TAMU-3182.

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Yanggratoke, Rerngvit. « Contributions to Performance Modeling and Management of Data Centers ». Licentiate thesis, KTH, Kommunikationsnät, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-129296.

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Over the last decade, Internet-based services, such as electronic-mail, music-on-demand, and social-network services, have changed the ways we communicate and access information. Usually, the key functionality of such a service is in backend components, which are located in a data center, a facility for hosting computing systems and related equipment. This thesis focuses on two fundamental problems related to the management, dimensioning, and provisioning of such backend components. The first problem centers around resource allocation for a large-scale cloud environment. Data centers have become very large; they often contain hundreds of thousands of machines and applications. In such a data center, resource allocation cannot be efficiently achieved through a traditional management system that is centralized in nature. Therefore, a more scalable solution is needed. To address this problem, we have developed and evaluated a scalable and generic protocol for resource allocation. The protocol is generic in the sense that it can be instantiated for different management objectives through objective functions. The protocol jointly allocates CPU, memory, and network resources to applications that are hosted by the cloud. We prove that the protocol converges to a solution, if an objective function satisfies a certain property. We perform a simulation study of the protocol for realistic scenarios. Simulation results suggest that the quality of the allocation is independent of the system size, up to 100,000 machines and applications, for the management objectives considered. The second problem is related to performance modeling of a distributed key-value store. The specific distributed key-value store we focus on in this thesis is the Spotify storage system. Understanding the performance of the Spotify storage system is essential for achieving a key quality of service objective, namely that the playback latency of a song is sufficiently low. To address this problem, we have developed and evaluated models for predicting the performance of a distributed key-value store for a lightly loaded system. First, we developed a model that allows us to predict the response time distribution of requests. Second, we modeled the capacity of the distributed key-value store for two different object allocation policies. We evaluate the models by comparing model predictions with measurements from two different environments: our lab testbed and a Spotify operational environment. We found that the models are accurate in the sense that the prediction error, i.e., the difference between the model predictions and the measurements from the real systems, is at most 11%.

QC 20131001

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Somani, Ankit. « Advanced thermal management strategies for energy-efficient data centers ». Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/26527.

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Thesis (M. S.)--Mechanical Engineering, Georgia Institute of Technology, 2009.
Committee Chair: Joshi, Yogendra; Committee Member: ghiaasiaan, mostafa; Committee Member: Schwan, Karsten. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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Kekelishvili, Rebecca. « DHISC : Disk Health Indexing System for Centers of Data Management ». Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/113181.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 85-88).
If we want to have reliable data centers, we must improve reliability at the lowest level of data storage at the disk level. To improve reliability, we need to convert storage systems from reactive mechanisms that handle disk failures to a proactive mechanism that predict and address failures. Because the definition of disk failure is specific to a customer rather than defined by a standard, we developed a relative disk health metric and proposed a customer-oriented disk-maintenance pipeline. We designed a program that processes data collected from data center disks into a format that is easy to analyze using machine learning. Then, we used a neural network to recognize disks that show signs of oncoming failure with 95.4-98.7% accuracy, and used the result of the network to produce a rank of most and least reliable disks at the data center, enabling customers to perform bulk disk maintenance, decreasing system downtime.
by Rebecca Kekelishvili.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science

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Kundu, Sajib. « Improving Resource Management in Virtualized Data Centers using Application Performance Models ». FIU Digital Commons, 2013. http://digitalcommons.fiu.edu/etd/874.

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The rapid growth of virtualized data centers and cloud hosting services is making the management of physical resources such as CPU, memory, and I/O bandwidth in data center servers increasingly important. Server management now involves dealing with multiple dissimilar applications with varying Service-Level-Agreements (SLAs) and multiple resource dimensions. The multiplicity and diversity of resources and applications are rendering administrative tasks more complex and challenging. This thesis aimed to develop a framework and techniques that would help substantially reduce data center management complexity. We specifically addressed two crucial data center operations. First, we precisely estimated capacity requirements of client virtual machines (VMs) while renting server space in cloud environment. Second, we proposed a systematic process to efficiently allocate physical resources to hosted VMs in a data center. To realize these dual objectives, accurately capturing the effects of resource allocations on application performance is vital. The benefits of accurate application performance modeling are multifold. Cloud users can size their VMs appropriately and pay only for the resources that they need; service providers can also offer a new charging model based on the VMs performance instead of their configured sizes. As a result, clients will pay exactly for the performance they are actually experiencing; on the other hand, administrators will be able to maximize their total revenue by utilizing application performance models and SLAs. This thesis made the following contributions. First, we identified resource control parameters crucial for distributing physical resources and characterizing contention for virtualized applications in a shared hosting environment. Second, we explored several modeling techniques and confirmed the suitability of two machine learning tools, Artificial Neural Network and Support Vector Machine, to accurately model the performance of virtualized applications. Moreover, we suggested and evaluated modeling optimizations necessary to improve prediction accuracy when using these modeling tools. Third, we presented an approach to optimal VM sizing by employing the performance models we created. Finally, we proposed a revenue-driven resource allocation algorithm which maximizes the SLA-generated revenue for a data center.

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Livres sur le sujet "Data centers management"

Jayaswal, Kailash. Administering Data Centers. New York : John Wiley & Sons, Ltd., 2005.

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Scriba, Albrecht, et Volker Herminghaus. Storage Management in Data Centers. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-85023-6.

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Marx Gómez, Jorge, Manuel Mora, Mahesh S. Raisinghani, Wolfgang Nebel et Rory V. O'Connor, dir. Engineering and Management of Data Centers. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65082-1.

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Joshi, Yogendra, et Pramod Kumar, dir. Energy Efficient Thermal Management of Data Centers. Boston, MA : Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-7124-1.

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Joshi, Yogendra. Energy Efficient Thermal Management of Data Centers. Boston, MA : Springer US, 2012.

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Francett, Barbara. Data center management : Planning report. Charleston, S.C : Computer Technology Research Corp., 1992.

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United States. Office of Management and Budget. Consolidation of agency data centers. Washington, D.C : Executive Office of the President, Office of Management and Budget, 1995.

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Arghode, Vaibhav K., et Yogendra Joshi. Air Flow Management in Raised Floor Data Centers. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-25892-8.

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(Firm), Price Waterhouse. Data processing service centers financial management standards review. Seattle, WA : Price Waterhouse, 1986.

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Systems, Communications/Information, dir. Managing corporate data centers : COBOL restructuring. Boston, MA (200 Portland St., Boston 02114) : Yankee Group, 1987.

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Chapitres de livres sur le sujet "Data centers management"

Herminghaus, Volker. « Dual Data Centers ». Dans Storage Management in Data Centers, 191–231. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-85023-6_8.

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Das, Sudipto. « Data Management in Data Centers ». Dans Encyclopedia of Database Systems, 1–7. New York, NY : Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_80638-1.

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Das, Sudipto. « Data Management in Data Centers ». Dans Encyclopedia of Database Systems, 786–92. New York, NY : Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_80638.

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Zahoor, Babar, Bibrak Qamar et Raihan ur Rasool. « Central Management of Datacenters ». Dans Handbook on Data Centers, 1155–70. New York, NY : Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2092-1_39.

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Bunse, Christian, Hagen Höpfner, Sonja Klingert, Essam Mansour et Suman Roychoudhury. « Energy Aware Database Management ». Dans Energy-Efficient Data Centers, 40–53. Berlin, Heidelberg : Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55149-9_4.

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de Teodoro, Pilar, Alexander Hutton, Benoit Frezouls, Alain Montmory, Jordi Portell, Rosario Messineo, Marco Riello et Krzysztof Nienartowicz. « Data Management at Gaia Data Processing Centers ». Dans Astrostatistics and Data Mining, 107–15. New York, NY : Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3323-1_10.

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Sacco, Peter. « Computerized Maintenance Management System in Data Centers ». Dans Data Center Handbook, 619–38. Hoboken, NJ : John Wiley & Sons, Inc, 2014. http://dx.doi.org/10.1002/9781118937563.ch34.

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Klingert, Sonja, Andreas Berl, Michael Beck, Radu Serban, Marco di Girolamo, Giovanni Giuliani, Hermann de Meer et Alfons Salden. « Sustainable Energy Management in Data Centers through Collaboration ». Dans Energy Efficient Data Centers, 13–24. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33645-4_2.

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Bhattacharya, Prajesh. « Data Center Monitoring ». Dans Energy Efficient Thermal Management of Data Centers, 199–236. Boston, MA : Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-7124-1_5.

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Ahmed, Kishwar, Shaolei Ren, Yuxiong He et Athanasios V. Vasilakos. « Online Resource Management for Carbon-Neutral Cloud Computing ». Dans Handbook on Data Centers, 607–30. New York, NY : Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2092-1_20.

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Actes de conférences sur le sujet "Data centers management"

Liu, Chuyi, Jianxiong Wan, Leixiao Li, Guanyu Ren et Xiaolei Wang. « Distributed Energy Management for Carbon Neutral Data Centers ». Dans 2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA), 1443–38. IEEE, 2024. https://doi.org/10.1109/ispa63168.2024.00193.

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Klein, L. J., P. J. Singh, M. Schappert, Marc Griffel et H. F. Hamann. « Corrosion management for data centers ». Dans Management Symposium (SEMI-THERM). IEEE, 2011. http://dx.doi.org/10.1109/stherm.2011.5767173.

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El-Sayed, Nosayba, Ioan A. Stefanovici, George Amvrosiadis, Andy A. Hwang et Bianca Schroeder. « Temperature management in data centers ». Dans the 12th ACM SIGMETRICS/PERFORMANCE joint international conference. New York, New York, USA : ACM Press, 2012. http://dx.doi.org/10.1145/2254756.2254778.

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Haas, Zygmunt J., et Shuyang Gu. « On Power Management Policies for Data Centers ». Dans 2015 IEEE International Conference on Data Science and Data Intensive Systems (DSDIS). IEEE, 2015. http://dx.doi.org/10.1109/dsdis.2015.82.

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Karakostas, Vasileios, Georgios Goumas, Ewnetu Bayuh Lakew, Erik Elmroth, Stefanos Gerangelos, Simon Kolberg, Konstantinos Nikas et al. « Efficient resource management for data centers ». Dans SAMOS XVIII : Architectures, Modeling, and Simulation. New York, NY, USA : ACM, 2018. http://dx.doi.org/10.1145/3229631.3236095.

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Sirer, Emin Gün. « Session details : Data centers : resource management ». Dans SIGCOMM '12 : ACM SIGCOMM 2012 Conference. New York, NY, USA : ACM, 2012. http://dx.doi.org/10.1145/3259305.

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Mulay, Veerendra, Dereje Agonafer et Roger Schmidt. « Liquid Cooling for Thermal Management of Data Centers ». Dans ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-68743.

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The power trend for Server systems continues to grow thereby making thermal management of Data centers a very challenging task. Although various configurations exist, the raised floor plenum with Computer Room Air Conditioners (CRACs) providing cold air is a popular operating strategy. The air cooling of data center however, may not address the situation where more energy is expended in cooling infrastructure than the thermal load of data center. Revised power trend projections by ASHRAE TC 9.9 predict heat load as high as 5000W per square feet of compute servers’ equipment footprint by year 2010. These trend charts also indicate that heat load per product footprint has doubled for storage servers during 2000–2004. For the same period, heat load per product footprint for compute servers has tripled. Amongst the systems that are currently available and being shipped, many racks exceed 20kW. Such high heat loads have raised concerns over limits of air cooling of data centers similar to air cooling of microprocessors. Thermal management of such dense data center clusters using liquid cooling is presented.

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Ozawa, Yoji, Satoshi Kaneko, Taku Okamura, Tetsu Moriwake, Yuichi Nabetani, Daiki Shimizu et Soichiro Kumagai. « Grid-aware Energy Management by Data Center Workload Control Across Multiple Data Centers ». Dans MIDDLEWARE '24 : 25th International Middleware Conference, 13–14. New York, NY, USA : ACM, 2024. https://doi.org/10.1145/3704440.3704781.

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Lv, Zuoliang, et Baolong Yang. « The Operation Center of the Smart Community Centers on Data ». Dans 7th International Conference on Management, Education, Information and Control (MEICI 2017). Paris, France : Atlantis Press, 2017. http://dx.doi.org/10.2991/meici-17.2017.128.

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Viswanathan, K., L. Choudur, V. Talwar, Chengwei Wang, G. Macdonald et W. Satterfield. « Ranking anomalies in data centers ». Dans 2012 IEEE/IFIP Network Operations and Management Symposium (NOMS 2012). IEEE, 2012. http://dx.doi.org/10.1109/noms.2012.6211885.

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Rapports d'organisations sur le sujet "Data centers management"

Mittal, Sparsh. Power Management Techniques for Data Centers : A Survey. Office of Scientific and Technical Information (OSTI), juillet 2014. http://dx.doi.org/10.2172/1150909.

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Hendrik Hamann, Levente Klein. A Measurement Management Technology for Improving Energy Efficiency in Data Centers and Telecommunication Facilities. Office of Scientific and Technical Information (OSTI), juin 2012. http://dx.doi.org/10.2172/1044604.

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Moon, Ron. RECOVERY ACT : DYNAMIC ENERGY CONSUMPTION MANAGEMENT OF ROUTING TELECOM AND DATA CENTERS THROUGH REAL-TIME OPTIMAL CONTROL (RTOC) : Final Scientific/Technical Report. Office of Scientific and Technical Information (OSTI), juin 2011. http://dx.doi.org/10.2172/1018478.

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Ke, Jian-yu, Fynnwin Prager, Jose Martinez et Chris Cagle. Achieving Excellence for California’s Freight System : Developing Competitiveness and Performance Metrics ; Incorporating Sustainability, Resilience, and Workforce Development. Mineta Transportation Institute, décembre 2021. http://dx.doi.org/10.31979/mti.2021.2023.

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This study explores the question of whether California's freight system is staying competitive with other US regions. A novel analytical framework compares supply chain performance metrics across multiple US states and regions for seaports, airports, highways, freight rail service, and distribution centers by combining the Performance Evaluation Matrix (PEM), Competitive Position Matrix (CPM), and Business Process Management (BPM) approaches. Analysis of industry data and responses from structured interviews with 30 freight industry experts across 5 transportation sectors suggests that California's freight system is competitive for seaports, airports, and freight rail; however, highways and distribution centers have room for improvement with respect to travel time reliability and operation costs, and California should prioritize infrastructure investments here. To stay competitive with the Texas and North East regions, state investments could also expand seaport container terminals and air cargo handling facilities, improve intermodal port connections and management of flows of chassis, container trucks, empty containers to ameliorate cargo backlogs and congestion on highways, at the ports, and at warehouses. The state could also invest in inland ports, transporting goods by rail directly from seaports to the Inland Empire or Central Valley.

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Shoshani, Arie. The Scientific Data Management Center. Office of Scientific and Technical Information (OSTI), juin 2006. http://dx.doi.org/10.2172/886956.

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Critchlow, T. J., L. Liu, C. Pu, A. Gupta, B. Ludaescher, I. Altintas, M. Vouk, D. Bitzer, M. Singh et D. Rosnick. Scientific Data Management Center Scientific Data Integration. Office of Scientific and Technical Information (OSTI), janvier 2003. http://dx.doi.org/10.2172/15003250.

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Letcher, Theodore, Kent Sparrow et Sandra LeGrand. Establishing a series of dust event case studies for East Asia. Engineer Research and Development Center (U.S.), octobre 2023. http://dx.doi.org/10.21079/11681/47824.

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Dust aerosols have a wide range of effects on air quality, health, land-management decisions, aircraft operations, and sensor data interpretations. Therefore, the accurate simulation of dust plume initiation and transport is a priority for operational weather centers. Recent advancements have improved the performance of dust prediction models, but substantial capability gaps remain when forecasting the specific location and timing of individual dust events, especially extreme dust outbreaks. Operational weather forecasters and US Army Engineer Research and Development Center (ERDC) researchers established a series of reference case study events to enhance dust transport model evaluation. These reference case studies support research to improve modeled dust simulations, including efforts to increase simulation accuracy on when and where dust is lofted off the ground, dust aerosols transport, and dust-induced adverse air quality issues create hazardous conditions downstream. Here, we provide detailed assessments of four dust events for Central and East Asia. We describe the dust-event lifecycle from onset to end (or when dust transports beyond the area of interest) and the synoptic and mesoscale environ-mental conditions governing the process. Analyses of hourly reanalysis data, spaceborne lidar and aerosol optical depth retrievals, upper-air soundings, true-color satellite imagery, and dust-enhanced false-color imagery supplement the discussions.

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Vouk, Mladen A. Scientific Data Management Center for Enabling Technologies. Office of Scientific and Technical Information (OSTI), janvier 2013. http://dx.doi.org/10.2172/1095275.

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Choudhary, A., et W. K. Liao. Scientific Data Management Integrated Software Infrastructure Center. Office of Scientific and Technical Information (OSTI), octobre 2008. http://dx.doi.org/10.2172/940026.

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Choudhary, Alok, et Wei-keng Liao. Scientific Data Management Center for Enabling Technologies. Office of Scientific and Technical Information (OSTI), juillet 2011. http://dx.doi.org/10.2172/1073503.

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