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Auswahl der wissenschaftlichen Literatur zum Thema „Cloud feedback“
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Zeitschriftenartikel zum Thema "Cloud feedback"
Zhou, Chen, Mark D. Zelinka, Andrew E. Dessler und Ping Yang. „An Analysis of the Short-Term Cloud Feedback Using MODIS Data“. Journal of Climate 26, Nr. 13 (01.07.2013): 4803–15. http://dx.doi.org/10.1175/jcli-d-12-00547.1.
Der volle Inhalt der QuelleZelinka, Mark D., Stephen A. Klein, Karl E. Taylor, Timothy Andrews, Mark J. Webb, Jonathan M. Gregory und Piers M. Forster. „Contributions of Different Cloud Types to Feedbacks and Rapid Adjustments in CMIP5*“. Journal of Climate 26, Nr. 14 (12.07.2013): 5007–27. http://dx.doi.org/10.1175/jcli-d-12-00555.1.
Der volle Inhalt der QuelleZelinka, Mark D., Stephen A. Klein und Dennis L. Hartmann. „Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part I: Cloud Radiative Kernels“. Journal of Climate 25, Nr. 11 (Juni 2012): 3715–35. http://dx.doi.org/10.1175/jcli-d-11-00248.1.
Der volle Inhalt der QuelleDawson, Emma, und Kathleen A. Schiro. „Tropical High Cloud Feedback Relationships to Climate Sensitivity“. Journal of Climate 38, Nr. 2 (15.01.2025): 583–96. https://doi.org/10.1175/jcli-d-24-0218.1.
Der volle Inhalt der QuelleYoshimori, Masakazu, F. Hugo Lambert, Mark J. Webb und Timothy Andrews. „Fixed Anvil Temperature Feedback: Positive, Zero, or Negative?“ Journal of Climate 33, Nr. 7 (01.04.2020): 2719–39. http://dx.doi.org/10.1175/jcli-d-19-0108.1.
Der volle Inhalt der QuelleZhu, Ping, James J. Hack und Jeffrey T. Kiehl. „Diagnosing Cloud Feedbacks in General Circulation Models“. Journal of Climate 20, Nr. 11 (01.06.2007): 2602–22. http://dx.doi.org/10.1175/jcli4140.1.
Der volle Inhalt der QuelleZelinka, Mark D., Stephen A. Klein und 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, Nr. 11 (Juni 2012): 3736–54. http://dx.doi.org/10.1175/jcli-d-11-00249.1.
Der volle Inhalt der QuelleSun, De-Zheng, Yongqiang Yu und Tao Zhang. „Tropical Water Vapor and Cloud Feedbacks in Climate Models: A Further Assessment Using Coupled Simulations“. Journal of Climate 22, Nr. 5 (01.03.2009): 1287–304. http://dx.doi.org/10.1175/2008jcli2267.1.
Der volle Inhalt der QuelleZhang, Minghua, und 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, Nr. 18 (15.09.2008): 4859–78. http://dx.doi.org/10.1175/2008jcli2237.1.
Der volle Inhalt der QuelleLohmann, Ulrike, und 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, Nr. 12 (22.06.2018): 8807–28. http://dx.doi.org/10.5194/acp-18-8807-2018.
Der volle Inhalt der QuelleDissertationen zum Thema "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.
Der volle Inhalt der QuelleUllah, Amjad. „Towards a novel biologically-inspired cloud elasticity framework“. Thesis, University of Stirling, 2017. http://hdl.handle.net/1893/26064.
Der volle Inhalt der QuelleKropf, Dorothy Cortez. „Applying UTAUT to Determine Intent to Use Cloud Computing in K-12 Classrooms“. ScholarWorks, 2018. https://scholarworks.waldenu.edu/dissertations/5212.
Der volle Inhalt der QuelleHygate, Alexander P. S. [Verfasser], und 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.
Der volle Inhalt der QuelleHygate, Alexander P. S. [Verfasser], und 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.
Der volle Inhalt der QuelleHygate, Alexander Philip Stuart [Verfasser], und 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.
Der volle Inhalt der QuelleNobile, 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/.
Der volle Inhalt der QuelleCLoud 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.
Der volle Inhalt der QuelleBerekmeri, 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.
Der volle Inhalt der QuelleThe 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.
Der volle Inhalt der QuelleBücher zum Thema "Cloud feedback"
Atlidakis, Evangelos. Structure and Feedback in Cloud Service API Fuzzing. [New York, N.Y.?]: [publisher not identified], 2021.
Den vollen Inhalt der Quelle findenWorkshop 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.
Den vollen Inhalt der Quelle findenAbbot, Dorian Schuyler. A high-latitude convective cloud feedback. 2008.
Den vollen Inhalt der Quelle findenRavetto-Biagioli, Kriss. Digital Uncanny. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190853990.001.0001.
Der volle Inhalt der QuelleCloud radiation forcings and feedbacks: General circulation model tests and observational validation. [Washington, DC: National Aeronautics and Space Administration, 1997.
Den vollen Inhalt der Quelle findenObservation of local cloud and moisture feedbacks over high ocean and desert surface temperatures. [Washington, DC: National Aeronautics and Space Administration, 1995.
Den vollen Inhalt der Quelle findenWang, Bin. Intraseasonal Modulation of the Indian Summer Monsoon. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.616.
Der volle Inhalt der QuelleLiu, Xiaodong, und Libin Yan. Elevation-Dependent Climate Change in the Tibetan Plateau. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228620.013.593.
Der volle Inhalt der QuelleBuchteile zum Thema "Cloud feedback"
Ahmed, Riaz. „Application Feedback“. In Cloud Computing Using Oracle Application Express, 277–80. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2502-8_30.
Der volle Inhalt der QuelleAhmed, Riaz. „Application Feedback“. In Cloud Computing Using Oracle Application Express, 321–24. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-4243-8_30.
Der volle Inhalt der QuelleLi, Jia, Lei Yao und James Z. Wang. „Photo Composition Feedback and Enhancement“. In Mobile Cloud Visual Media Computing, 113–44. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24702-1_5.
Der volle Inhalt der QuelleYan, Xuejun, Hongyu Yan, Jingjing Wang, Hang Du, Zhihong Wu, Di Xie, Shiliang Pu und Li Lu. „FBNet: Feedback Network for Point Cloud Completion“. In Lecture Notes in Computer Science, 676–93. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20086-1_39.
Der volle Inhalt der QuelleIndrusiak, Leandro Soares, Piotr Dziurzanski und Amit Kumar Singh. „Feedback-Based Admission Control Heuristics“. In 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.
Der volle Inhalt der QuelleAnson, Chris M. „Teacher Feedback Tools“. In 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.
Der volle Inhalt der QuelleIndrusiak, Leandro Soares, Piotr Dziurzanski und Amit Kumar Singh. „Feedback-Based Allocation and Optimisation Heuristics“. In 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.
Der volle Inhalt der QuelleWang, Xuan, Yi Li, Linna Wang und Li Lu. „CFNet: Point Cloud Upsampling via Cascaded Feedback Network“. In 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.
Der volle Inhalt der QuelleZhang, M. H., J. T. Kiehl und J. J. Hack. „Cloud-Radiative Feedback as Produced by Different Parameterizations of Cloud Emissivity in CCM2“. In 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.
Der volle Inhalt der QuelleLi, Jinhai, Yunlei Ma, Huisheng Zhu und Youshi He. „Research on Feedback Effects Between Perception of Internet Word of Mouth and Online Reviews Based on Dynamic Endogeneity“. In Cloud Computing and Security, 658–69. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00018-9_58.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Cloud feedback"
Cappelletti, Andrea, und Mark Grechanik. „Feedback-Directed Cross-Layer Optimization of Cloud-Based Functional Actor Applications“. In 2024 IEEE 35th International Symposium on Software Reliability Engineering (ISSRE), 605–16. IEEE, 2024. https://doi.org/10.1109/issre62328.2024.00063.
Der volle Inhalt der QuelleLi, Xinyi, und Ganesh Gopalakrishnan. „FBTuner: A Feedback-Directed Approach for Safe Mixed-Precision Tuning“. In 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.
Der volle Inhalt der QuelleJayanthi, L. N., Kannan Shanmugam, Challapalli Suma, Sagar B S, S. P. Maniraj und T. R. GaneshBabu. „Optimizing Patient Satisfaction Surveys and Feedback with Cloud Computing and NLP Techniques“. In 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.
Der volle Inhalt der QuelleKarpagam, G. R., Harsha Vardhan V M, Kabilan K K, Pranav P, Prednya Ramesh und Suvan Sathyendira B. „Physiological Data-Based Stress Detection: From Wrist Sensors to Cloud Computing and User Feedback Integration“. In 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.
Der volle Inhalt der QuelleKaneko, Yu, und Toshio Ito. „A Reliable Cloud-Based Feedback Control System“. In 2016 IEEE 9th International Conference on Cloud Computing (CLOUD). IEEE, 2016. http://dx.doi.org/10.1109/cloud.2016.0128.
Der volle Inhalt der QuelleKuchi, Kiran, Maaz M. Mohiuddin, T. V. Sreejith, Rijul Bansal, G. V. V. Sharma und Shahriar Emami. „Cloud radios with limited feedback“. In 2014 International Conference on Signal Processing and Communications (SPCOM). IEEE, 2014. http://dx.doi.org/10.1109/spcom.2014.6983978.
Der volle Inhalt der QuelleOu, S. C., K. N. Liou, W. Gooch, N. Rao und Y. Takano. „Remote Sensing of Cirrus Cloud Parameters Using AVHRR 3.7 and 10.9 μm Channel Data“. In Optical Remote Sensing of the Atmosphere. Washington, D.C.: Optica Publishing Group, 1993. http://dx.doi.org/10.1364/orsa.1993.mc.4.
Der volle Inhalt der QuelleHamadache, Kahina, und Paraskevi Zerva. „Provenance of Feedback in Cloud Services“. In 2014 IEEE 8th International Symposium on Service Oriented System Engineering (SOSE). IEEE, 2014. http://dx.doi.org/10.1109/sose.2014.10.
Der volle Inhalt der QuelleEberhard, W. L., R. E. Cupp, R. M. Hardesty, J. M. Intrieri und R. J. Willis. „Design and Preliminary Results from the Cloud Lidar And Radar Exploratory Test (CLARET)“. In Optical Remote Sensing of the Atmosphere. Washington, D.C.: Optica Publishing Group, 1990. http://dx.doi.org/10.1364/orsa.1990.tua5.
Der volle Inhalt der QuelleMachhi, Sandip, und G. B. Jethava. „Feedback based Trust Management for Cloud Environment“. In the Second International Conference. New York, New York, USA: ACM Press, 2016. http://dx.doi.org/10.1145/2905055.2905330.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Cloud feedback"
Dipankar, Anurag, und Bjorn Stevens. Cloud feedback studies with a physics grid. Office of Scientific and Technical Information (OSTI), Februar 2013. http://dx.doi.org/10.2172/1076961.
Der volle Inhalt der QuelleBenedict, James, Amy Clement, B. Medeiros und S. Klein. Cloud-Feedback Model Intercomparison Project: Tier 2 Simulations (Final Report). Office of Scientific and Technical Information (OSTI), Februar 2021. http://dx.doi.org/10.2172/1769119.
Der volle Inhalt der QuelleGeorge 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), August 2009. http://dx.doi.org/10.2172/962208.
Der volle Inhalt der QuelleDel Genio, Anthony D. Constraints on cloud feedback from analysis of ARM observations and models. Office of Scientific and Technical Information (OSTI), April 2015. http://dx.doi.org/10.2172/1178045.
Der volle Inhalt der QuelleLacis, A. Analysis of cloud radiative forcing and feedback in a climate GCM. Final report. Office of Scientific and Technical Information (OSTI), Dezember 1996. http://dx.doi.org/10.2172/465803.
Der volle Inhalt der QuelleHartmann, Dennis. Understanding and Constraining the Midlatitude Cloud Optical Depth Feedback in Climate Models (Final Report). Office of Scientific and Technical Information (OSTI), August 2019. http://dx.doi.org/10.2172/1558113.
Der volle Inhalt der QuelleKogan, Yefim L. Midlatitude Aerosol-Cloud-Radiation Feedbacks in Marine Boundary Layer Clouds. Fort Belvoir, VA: Defense Technical Information Center, September 2008. http://dx.doi.org/10.21236/ada532932.
Der volle Inhalt der QuelleKogan, Yefim L. Midlatitude Aerosol-Cloud-Radiation Feedbacks in Marine Boundary Layer Clouds. Fort Belvoir, VA: Defense Technical Information Center, September 2010. http://dx.doi.org/10.21236/ada541931.
Der volle Inhalt der QuelleKogan, Yefim L. Midlatitude Aerosol-Cloud-Radiation Feedbacks in Marine Boundary Layer Clouds. Fort Belvoir, VA: Defense Technical Information Center, September 2011. http://dx.doi.org/10.21236/ada557145.
Der volle Inhalt der QuelleMitra, Sayan. Continuous Integration and Deployment Infrastructure for Rapid Testing of Autonomous Transportation Systems. Illinois Center for Transportation, Juni 2024. http://dx.doi.org/10.36501/0197-9191/24-017.
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