Literatura académica sobre el tema "Cloud feedback"
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Artículos de revistas sobre el tema "Cloud feedback"
Zhou, Chen, Mark D. Zelinka, Andrew E. Dessler y Ping Yang. "An Analysis of the Short-Term Cloud Feedback Using MODIS Data". Journal of Climate 26, n.º 13 (1 de julio de 2013): 4803–15. http://dx.doi.org/10.1175/jcli-d-12-00547.1.
Texto completoZelinka, Mark D., Stephen A. Klein, Karl E. Taylor, Timothy Andrews, Mark J. Webb, Jonathan M. Gregory y Piers M. Forster. "Contributions of Different Cloud Types to Feedbacks and Rapid Adjustments in CMIP5*". Journal of Climate 26, n.º 14 (12 de julio de 2013): 5007–27. http://dx.doi.org/10.1175/jcli-d-12-00555.1.
Texto completoZelinka, Mark D., Stephen A. Klein y Dennis L. Hartmann. "Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part I: Cloud Radiative Kernels". Journal of Climate 25, n.º 11 (junio de 2012): 3715–35. http://dx.doi.org/10.1175/jcli-d-11-00248.1.
Texto completoDawson, Emma y Kathleen A. Schiro. "Tropical High Cloud Feedback Relationships to Climate Sensitivity". Journal of Climate 38, n.º 2 (15 de enero de 2025): 583–96. https://doi.org/10.1175/jcli-d-24-0218.1.
Texto completoYoshimori, Masakazu, F. Hugo Lambert, Mark J. Webb y Timothy Andrews. "Fixed Anvil Temperature Feedback: Positive, Zero, or Negative?" Journal of Climate 33, n.º 7 (1 de abril de 2020): 2719–39. http://dx.doi.org/10.1175/jcli-d-19-0108.1.
Texto completoZhu, Ping, James J. Hack y Jeffrey T. Kiehl. "Diagnosing Cloud Feedbacks in General Circulation Models". Journal of Climate 20, n.º 11 (1 de junio de 2007): 2602–22. http://dx.doi.org/10.1175/jcli4140.1.
Texto completoZelinka, Mark D., Stephen A. Klein y Dennis L. Hartmann. "Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part II: Attribution to Changes in Cloud Amount, Altitude, and Optical Depth". Journal of Climate 25, n.º 11 (junio de 2012): 3736–54. http://dx.doi.org/10.1175/jcli-d-11-00249.1.
Texto completoSun, De-Zheng, Yongqiang Yu y Tao Zhang. "Tropical Water Vapor and Cloud Feedbacks in Climate Models: A Further Assessment Using Coupled Simulations". Journal of Climate 22, n.º 5 (1 de marzo de 2009): 1287–304. http://dx.doi.org/10.1175/2008jcli2267.1.
Texto completoZhang, Minghua y Christopher Bretherton. "Mechanisms of Low Cloud–Climate Feedback in Idealized Single-Column Simulations with the Community Atmospheric Model, Version 3 (CAM3)". Journal of Climate 21, n.º 18 (15 de septiembre de 2008): 4859–78. http://dx.doi.org/10.1175/2008jcli2237.1.
Texto completoLohmann, Ulrike y David Neubauer. "The importance of mixed-phase and ice clouds for climate sensitivity in the global aerosol–climate model ECHAM6-HAM2". Atmospheric Chemistry and Physics 18, n.º 12 (22 de junio de 2018): 8807–28. http://dx.doi.org/10.5194/acp-18-8807-2018.
Texto completoTesis sobre el tema "Cloud feedback"
Davis, Michael A. "Cloud-Radiative Feedback and Ocean-Atmosphere Feedback In the Southeast Pacific Ocean Simulated by IPCC AR4 GCMs". The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1313350254.
Texto completoUllah, Amjad. "Towards a novel biologically-inspired cloud elasticity framework". Thesis, University of Stirling, 2017. http://hdl.handle.net/1893/26064.
Texto completoKropf, Dorothy Cortez. "Applying UTAUT to Determine Intent to Use Cloud Computing in K-12 Classrooms". ScholarWorks, 2018. https://scholarworks.waldenu.edu/dissertations/5212.
Texto completoHygate, Alexander P. S. [Verfasser] y Diederik [Akademischer Betreuer] Kruijssen. "The Physics of Cloud-scale Star Formation and Feedback Across Cosmic Time / Alexander Philip Stuart Hygate ; Betreuer: J. M. Diederik Kruijssen". Heidelberg : Universitätsbibliothek Heidelberg, 2020. http://nbn-resolving.de/urn:nbn:de:bsz:16-heidok-279482.
Texto completoHygate, Alexander P. S. [Verfasser] y J. M. Diederik [Akademischer Betreuer] Kruijssen. "The Physics of Cloud-scale Star Formation and Feedback Across Cosmic Time / Alexander Philip Stuart Hygate ; Betreuer: J. M. Diederik Kruijssen". Heidelberg : Universitätsbibliothek Heidelberg, 2020. http://d-nb.info/1205883878/34.
Texto completoHygate, Alexander Philip Stuart [Verfasser] y J. M. Diederik [Akademischer Betreuer] Kruijssen. "The Physics of Cloud-scale Star Formation and Feedback Across Cosmic Time / Alexander Philip Stuart Hygate ; Betreuer: J. M. Diederik Kruijssen". Heidelberg : Universitätsbibliothek Heidelberg, 2020. http://d-nb.info/1205883878/34.
Texto completoNobile, Pedro Northon. "Projeto de um broker de gerenciamento adaptativo de recursos em computação em nuvem baseado em técnicas de controle realimentado". Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-21062013-110725/.
Texto completoCLoud computing refers to a computer resource deployment model in which software and hardware infrastructure are offered as a service. Cloud computing has become a successful paradigm due to the versatility and cost-effectiveness involved in that business model, making it possible to share a cluster of physical resources between several users and applications. With the advent of cloud computing and the computer elastic resource, dynamic allocation of virtualized resources is becoming more prominent, and along with it, the issues concerning the establishment of quality of service parameters. Historically, research on QoS has focused on solutions for problems involving two entities: users and servers. However, in cloud environments, a third party becomes part of this interaction, the cloud consumer, that uses the infrastructure to provide some kind of service to endusers, and which has received fewer attention, especially regarding the development of autonomic mechanisms for dynamic resource allocation under time-varying demand. This work aims at the development of an architecture for dynamic adaptive resource allocation involving three entities, focused on consumer revenue. The research outcome is a consumer broker architecture based on feedback control, a respective architecture prototype and a computer system feedback control methodology which may be applied in this class of problems
DECATALDO, DAVIDE. "The Effect of Stellar and Quasar Feedback on the Interstellar Medium: Structure and Lifetime of Molecular Clouds". Doctoral thesis, Scuola Normale Superiore, 2020. http://hdl.handle.net/11384/90712.
Texto completoBerekmeri, Mihaly. "La modélisation et le contrôle des services BigData : application à la performance et la fiabilité de MapReduce". Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAT126/document.
Texto completoThe amount of raw data produced by everything from our mobile phones, tablets, computers to our smart watches brings novel challenges in data storage and analysis. Many solutions have arisen in the industry to treat these large quantities of raw data, the most popular being the MapReduce framework. However, while the deployment complexity of such computing systems is steadily increasing, continuous availability and fast response times are still the expected norm. Furthermore, with the advent of virtualization and cloud solutions, the environments where these systems need to run is becoming more and more dynamic. Therefore ensuring performance and dependability constraints of a MapReduce service still poses significant challenges. In this thesis we address this problematic of guaranteeing the performance and availability of MapReduce based cloud services, taking an approach based on control theory. We develop the first dynamic models of a MapReduce service running a concurrent workload. Furthermore, we develop several control laws to ensure different quality of service objectives. First, classical feedback and feedforward controllers are developed to guarantee service performance. To further adapt our controllers to the cloud, such as minimizing the number of reconfigurations and costs, a novel event-based control architecture is introduced for performance management. Finally we develop the optimal control architecture MR-Ctrl, which is the first solution to provide guarantees in terms of both performance and dependability for MapReduce systems, meanwhile keeping cost at a minimum. All the modeling and control approaches are evaluated both in simulation and experimentally using MRBS, a comprehensive benchmark suite for evaluating the performance and dependability of MapReduce systems. Validation experiments were run in a real 60 node Hadoop MapReduce cluster, running a data intensive Business Intelligence workload. Our experiments show that the proposed techniques can successfully guarantee performance and dependability constraints
Wu, Fei. "Ultra-Low Delay in Complex Computing and Networked Systems: Fundamental Limits and Efficient Algorithms". The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu155559337777619.
Texto completoLibros sobre el tema "Cloud feedback"
Atlidakis, Evangelos. Structure and Feedback in Cloud Service API Fuzzing. [New York, N.Y.?]: [publisher not identified], 2021.
Buscar texto completoWorkshop on Cloud Processes and Cloud Feedbacks in Large-scale Models (1999 Reading, Berkshire, United Kingdom). Workshop on Cloud Processes and Cloud Feedbacks in Large-scale Models, European Centre for Medium-range Weather Forecasts, Reading, Berkshire, United Kingdom, 9-13 November 1999. Geneva, Switzerland: Joint Planning Staff for WCRP, World Meteorological Organization, 2000.
Buscar texto completoAbbot, Dorian Schuyler. A high-latitude convective cloud feedback. 2008.
Buscar texto completoRavetto-Biagioli, Kriss. Digital Uncanny. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190853990.001.0001.
Texto completoCloud radiation forcings and feedbacks: General circulation model tests and observational validation. [Washington, DC: National Aeronautics and Space Administration, 1997.
Buscar texto completoObservation of local cloud and moisture feedbacks over high ocean and desert surface temperatures. [Washington, DC: National Aeronautics and Space Administration, 1995.
Buscar texto completoWang, Bin. Intraseasonal Modulation of the Indian Summer Monsoon. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.616.
Texto completoLiu, Xiaodong y Libin Yan. Elevation-Dependent Climate Change in the Tibetan Plateau. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228620.013.593.
Texto completoCapítulos de libros sobre el tema "Cloud feedback"
Ahmed, Riaz. "Application Feedback". En Cloud Computing Using Oracle Application Express, 277–80. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2502-8_30.
Texto completoAhmed, Riaz. "Application Feedback". En Cloud Computing Using Oracle Application Express, 321–24. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-4243-8_30.
Texto completoLi, Jia, Lei Yao y James Z. Wang. "Photo Composition Feedback and Enhancement". En Mobile Cloud Visual Media Computing, 113–44. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24702-1_5.
Texto completoYan, Xuejun, Hongyu Yan, Jingjing Wang, Hang Du, Zhihong Wu, Di Xie, Shiliang Pu y Li Lu. "FBNet: Feedback Network for Point Cloud Completion". En Lecture Notes in Computer Science, 676–93. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20086-1_39.
Texto completoIndrusiak, Leandro Soares, Piotr Dziurzanski y Amit Kumar Singh. "Feedback-Based Admission Control Heuristics". En Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing, 25–49. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003337997-3.
Texto completoAnson, Chris M. "Teacher Feedback Tools". En Digital Writing Technologies in Higher Education, 183–202. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-36033-6_12.
Texto completoIndrusiak, Leandro Soares, Piotr Dziurzanski y Amit Kumar Singh. "Feedback-Based Allocation and Optimisation Heuristics". En Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing, 51–72. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003337997-4.
Texto completoWang, Xuan, Yi Li, Linna Wang y Li Lu. "CFNet: Point Cloud Upsampling via Cascaded Feedback Network". En Artificial Neural Networks and Machine Learning – ICANN 2023, 317–29. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44207-0_27.
Texto completoZhang, M. H., J. T. Kiehl y J. J. Hack. "Cloud-Radiative Feedback as Produced by Different Parameterizations of Cloud Emissivity in CCM2". En Climate Sensitivity to Radiative Perturbations, 213–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-61053-0_16.
Texto completoLi, Jinhai, Yunlei Ma, Huisheng Zhu y Youshi He. "Research on Feedback Effects Between Perception of Internet Word of Mouth and Online Reviews Based on Dynamic Endogeneity". En Cloud Computing and Security, 658–69. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00018-9_58.
Texto completoActas de conferencias sobre el tema "Cloud feedback"
Cappelletti, Andrea y Mark Grechanik. "Feedback-Directed Cross-Layer Optimization of Cloud-Based Functional Actor Applications". En 2024 IEEE 35th International Symposium on Software Reliability Engineering (ISSRE), 605–16. IEEE, 2024. https://doi.org/10.1109/issre62328.2024.00063.
Texto completoLi, Xinyi y Ganesh Gopalakrishnan. "FBTuner: A Feedback-Directed Approach for Safe Mixed-Precision Tuning". En 2024 IEEE 24th International Symposium on Cluster, Cloud and Internet Computing (CCGrid), 1–2. IEEE, 2024. http://dx.doi.org/10.1109/ccgrid59990.2024.00077.
Texto completoJayanthi, L. N., Kannan Shanmugam, Challapalli Suma, Sagar B S, S. P. Maniraj y T. R. GaneshBabu. "Optimizing Patient Satisfaction Surveys and Feedback with Cloud Computing and NLP Techniques". En 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 1038–44. IEEE, 2024. http://dx.doi.org/10.1109/i-smac61858.2024.10714624.
Texto completoKarpagam, G. R., Harsha Vardhan V M, Kabilan K K, Pranav P, Prednya Ramesh y Suvan Sathyendira B. "Physiological Data-Based Stress Detection: From Wrist Sensors to Cloud Computing and User Feedback Integration". En 2024 International Conference on Smart Systems for Electrical, Electronics, Communication and Computer Engineering (ICSSEECC), 386–91. IEEE, 2024. http://dx.doi.org/10.1109/icsseecc61126.2024.10649521.
Texto completoKaneko, Yu y Toshio Ito. "A Reliable Cloud-Based Feedback Control System". En 2016 IEEE 9th International Conference on Cloud Computing (CLOUD). IEEE, 2016. http://dx.doi.org/10.1109/cloud.2016.0128.
Texto completoKuchi, Kiran, Maaz M. Mohiuddin, T. V. Sreejith, Rijul Bansal, G. V. V. Sharma y Shahriar Emami. "Cloud radios with limited feedback". En 2014 International Conference on Signal Processing and Communications (SPCOM). IEEE, 2014. http://dx.doi.org/10.1109/spcom.2014.6983978.
Texto completoOu, S. C., K. N. Liou, W. Gooch, N. Rao y Y. Takano. "Remote Sensing of Cirrus Cloud Parameters Using AVHRR 3.7 and 10.9 μm Channel Data". En Optical Remote Sensing of the Atmosphere. Washington, D.C.: Optica Publishing Group, 1993. http://dx.doi.org/10.1364/orsa.1993.mc.4.
Texto completoHamadache, Kahina y Paraskevi Zerva. "Provenance of Feedback in Cloud Services". En 2014 IEEE 8th International Symposium on Service Oriented System Engineering (SOSE). IEEE, 2014. http://dx.doi.org/10.1109/sose.2014.10.
Texto completoEberhard, W. L., R. E. Cupp, R. M. Hardesty, J. M. Intrieri y R. J. Willis. "Design and Preliminary Results from the Cloud Lidar And Radar Exploratory Test (CLARET)". En Optical Remote Sensing of the Atmosphere. Washington, D.C.: Optica Publishing Group, 1990. http://dx.doi.org/10.1364/orsa.1990.tua5.
Texto completoMachhi, Sandip y G. B. Jethava. "Feedback based Trust Management for Cloud Environment". En the Second International Conference. New York, New York, USA: ACM Press, 2016. http://dx.doi.org/10.1145/2905055.2905330.
Texto completoInformes sobre el tema "Cloud feedback"
Dipankar, Anurag y Bjorn Stevens. Cloud feedback studies with a physics grid. Office of Scientific and Technical Information (OSTI), febrero de 2013. http://dx.doi.org/10.2172/1076961.
Texto completoBenedict, James, Amy Clement, B. Medeiros y S. Klein. Cloud-Feedback Model Intercomparison Project: Tier 2 Simulations (Final Report). Office of Scientific and Technical Information (OSTI), febrero de 2021. http://dx.doi.org/10.2172/1769119.
Texto completoGeorge Tselioudis. A Study to Investigate Cloud Feedback Processes and Evaluate GCM Cloud Variations Using Statistical Cloud Property Composites From ARM Data. Office of Scientific and Technical Information (OSTI), agosto de 2009. http://dx.doi.org/10.2172/962208.
Texto completoDel Genio, Anthony D. Constraints on cloud feedback from analysis of ARM observations and models. Office of Scientific and Technical Information (OSTI), abril de 2015. http://dx.doi.org/10.2172/1178045.
Texto completoLacis, A. Analysis of cloud radiative forcing and feedback in a climate GCM. Final report. Office of Scientific and Technical Information (OSTI), diciembre de 1996. http://dx.doi.org/10.2172/465803.
Texto completoHartmann, Dennis. Understanding and Constraining the Midlatitude Cloud Optical Depth Feedback in Climate Models (Final Report). Office of Scientific and Technical Information (OSTI), agosto de 2019. http://dx.doi.org/10.2172/1558113.
Texto completoKogan, Yefim L. Midlatitude Aerosol-Cloud-Radiation Feedbacks in Marine Boundary Layer Clouds. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 2008. http://dx.doi.org/10.21236/ada532932.
Texto completoKogan, Yefim L. Midlatitude Aerosol-Cloud-Radiation Feedbacks in Marine Boundary Layer Clouds. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 2010. http://dx.doi.org/10.21236/ada541931.
Texto completoKogan, Yefim L. Midlatitude Aerosol-Cloud-Radiation Feedbacks in Marine Boundary Layer Clouds. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 2011. http://dx.doi.org/10.21236/ada557145.
Texto completoMitra, Sayan. Continuous Integration and Deployment Infrastructure for Rapid Testing of Autonomous Transportation Systems. Illinois Center for Transportation, junio de 2024. http://dx.doi.org/10.36501/0197-9191/24-017.
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