Relevant bibliographies by topics / On demand computing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'On demand computing'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "On demand computing"

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Ouf, Shimaa, and Mona Nasr. "Cloud Computing." International Journal of Cloud Applications and Computing 5, no. 2 (2015): 53–61. http://dx.doi.org/10.4018/ijcac.2015040104.

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The population is living in a complex world at information explosion age. Today in Enterprises, the size and complexity of managing information have been increasing significantly and the demand for cost efficient information storage and processing grows higher. Enterprises need to optimize their IT management and minimize server maintenance costs become greater as usage demands prove to be increasingly unpredictable. Cloud computing offers a promising solution. Cloud computing is a computing model that relies on a large, centralized data center to store and process a great wealth of informatio
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Bashir, Noman, David Irwin, Prashant Shenoy, and Abel Souza. "Sustainable Computing - Without the Hot Air." ACM SIGEnergy Energy Informatics Review 3, no. 3 (2023): 47–52. http://dx.doi.org/10.1145/3630614.3630623.

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The demand for computing is continuing to grow exponentially. This growth will translate to exponential growth in computing's energy consumption unless improvements in its energy-efficiency can outpace increases in its demand. Yet, after decades of research, further improving energy-efficiency is becoming increasingly challenging, as it is already highly optimized. As a result, at some point, increases in computing demand are likely to outpace increases in its energy-efficiency, potentially by a wide margin. Such exponential growth, if left unchecked, will position computing as a substantial c
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Smith, M., M. Schmidt, N. Fallenbeck, T. Dörnemann, C. Schridde, and B. Freisleben. "Secure on-demand grid computing." Future Generation Computer Systems 25, no. 3 (2009): 315–25. http://dx.doi.org/10.1016/j.future.2008.03.002.

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Farhi, E. "DARTS on-demand computing." Acta Crystallographica Section A Foundations and Advances 78, a2 (2022): a772. http://dx.doi.org/10.1107/s2053273322090143.

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Zhao, Wei, and Hong Tao Zhang. "E-Commerce Recommendation System Based on Cloud Computing." Advanced Materials Research 926-930 (May 2014): 2570–73. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.2570.

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According to the Present Situations were that there is an urgent demand for large data analysis in Electronic Commerce, by using cloud computings advantage in storing and analyzing mass data, the solution of new project which can analysis data was proposed, based on the cloud computing. Firstly, aiming to the advantages of the cloud computing platform, the novel data-analysis architecture was designed, then the business flow chart by using cloud computing analysis on the architecture. Finally the project is validated by practical application and possesses certain reference meaning in data exch
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Wang, Shuai, Xin Ming Li, Fang Du, and Yun Fei Cui. "Research on the Model of Cloud Computing Individuation Portal Management." Advanced Materials Research 271-273 (July 2011): 525–30. http://dx.doi.org/10.4028/www.scientific.net/amr.271-273.525.

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As a computing mode, cloud computing has applied on the manufacture and life, its advantage such as convenience, shortcut and on-demand service is emerging gradually. This paper researches that how to make the usage of cloud computing high-efficiency. It summarizes the characteristic of cloud computing portal management model by analyzing the demands of security, visualization and individuation services. It researches the models both from customer entrance & management and individuation service stocking & management.
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Aniyikaiye, Jide, and Emmanuel Udoh. "Web Services Gateway." International Journal of Grid and High Performance Computing 8, no. 1 (2016): 85–92. http://dx.doi.org/10.4018/ijghpc.2016010108.

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Cloud computing has many advantages and is being used increasingly as an efficient and safe solution for web based services. This on-demnd self-service provides network access to a shared pooi of redundant computing resources. Software applications are being developed in the cloud and there are demands for the interoperability of these applications. A common way to meet this demand is the development of Web services (applications), taking advantage of Service-oriented architecture principles. These loosely coupled Web base components pose some security challenges. This paper examines Security
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Singh, Hardeep. "Cloud Computing: An Internet Based Computing." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 2, no. 3 (2012): 116–21. http://dx.doi.org/10.24297/ijct.v2i3b.2701.

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Present paper focuses on the overview of Cloud Computing. Cloud Computing is an Internet-based computing; where resources, software and information are provided to computers on-demand, like a public utility; is emerging as a platform for sharing resources like infrastructure, software and various applications. This paper does a literature survey on some of the prominent applications of Cloud Computing, and how they meet the requirements of reliability, availability of data, scalability of software and hardware systems and overall customer satisfaction.
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Lyu, Minghai. "The Intelligent Optimization of Energy Systems and Social Value: The Contribution of Edge Computing Technology to Sustainable Development Goals." International Journal of Computer Science and Information Technology 6, no. 2 (2025): 84–89. https://doi.org/10.62051/ijcsit.v6n2.08.

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As global energy demand continues to grow and environmental challenges escalate, traditional energy management models face significant challenges. Smart optimization of energy systems has emerged as a critical approach to drive energy transition and sustainable development. Edge computing, as an emerging technology, decentralizes data processing to the edge nodes of energy systems, enabling real-time data collection, processing, and intelligent decision-making, which enhances the efficiency and accuracy of energy management. This study focuses on the application of edge computing in energy sys
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Chen, Jianyong, Yang Wang, and Xiaomin Wang. "On-Demand Security Architecture for Cloud Computing." Computer 45, no. 7 (2012): 73–78. http://dx.doi.org/10.1109/mc.2012.120.

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Dissertations / Theses on the topic "On demand computing"

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Wong, Wai Ho. "Predicting Demand in Cloud Computing Environments." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9497.

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Cloud computing is a new computing paradigm that enables elastic on-demand pay-per-use access to shared computational resources. However, there are current limitations on the elasticity of cloud resources specifically deployment delays and mismatches in pricing granularity. Demand prediction - the estimation of future demand - serves to mitigate these limitations of elasticity. The focus of this thesis is the demand predictor that operates within the context of cloud resource management. It fulfils three tasks: 1) learn demand patterns from past demand behaviour; 2) predict upcoming demand
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Ahmed, Kishwar. "Energy Demand Response for High-Performance Computing Systems." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3569.

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The growing computational demand of scientific applications has greatly motivated the development of large-scale high-performance computing (HPC) systems in the past decade. To accommodate the increasing demand of applications, HPC systems have been going through dramatic architectural changes (e.g., introduction of many-core and multi-core systems, rapid growth of complex interconnection network for efficient communication between thousands of nodes), as well as significant increase in size (e.g., modern supercomputers consist of hundreds of thousands of nodes). With such changes in architect
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Alemany, Juan A. "Data placement algorithms for news-on-demand servers /." Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/6982.

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LEE, TAI-CHUN. "AN EVENT-BASED APPROACH TO DEMAND-DRIVEN DYNAMIC RECONFIGURABLE COMPUTING." University of Cincinnati / OhioLINK, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin990821256.

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Xia, Chunqiu. "Energy Demand Response Management in Smart Home Environments." Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/20182.

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ENABLING DEMAND RESPONSE ON ENERGY MANAGEMENT IN SMART HOME With the penetration of the Internet of Things (IoT) paradigm into the household scenario, an increasing number of smart appliances have been deployed to improve the comfort of living in the household. At present, most smart home devices are adopting the Cloud-based paradigm. The increasing electricity overhead from these smart appliances, however, has caused issues, as existing home energy management systems are unable to reduce electricity consumption effectively. To address this issue, we propose the use of an Edge-based computing
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Retana, Solano German F. "Technology support and demand for cloud infrastructure services: the role of service providers." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50236.

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Service providers have long recognized that their customers play a vital role in the service delivery process since they are not only recipients but also producers, or co-producers, of the service delivered. Moreover, in the particular context of self-service technology (SST) offerings, it is widely recognized that customers’ knowledge, skills and abilities in co-producing the service are key determinants of the services’ adoption and usage. However, despite the importance of customers’ capabilities, prior research has not yet paid much attention to the mechanisms by which service providers ca
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Khalifa, Ahmed Abdelmonem Abuelfotooh Ali. "Collaborative Computing Cloud: Architecture and Management Platform." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/72866.

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We are witnessing exponential growth in the number of powerful, multiply-connected, energy-rich stationary and mobile nodes, which will make available a massive pool of computing and communication resources. We claim that cloud computing can provide resilient on-demand computing, and more effective and efficient utilization of potentially infinite array of resources. Current cloud computing systems are primarily built using stationary resources. Recently, principles of cloud computing have been extended to the mobile computing domain aiming to form local clouds using mobile devices sharing the
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He, Jingzhu. "Video file distribution among geo-distributed cloud servers." HKBU Institutional Repository, 2016. https://repository.hkbu.edu.hk/etd_oa/304.

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With the emergence of cloud computing, many applications are migrated onto clouds. Video-on-demand (VoD) can be implemented on the cloud platform with geo-distributed cloud servers to serve worldwide users. New videos are distributed to these geo-distributed cloud servers. This distribution should be properly scheduled based on the videos' sizes, videos' popularities and the available network bandwidth, so that the mean completion time is minimized. We formulate this problemas a preemptive scheduling problem, prove that it is NP-hard, and design a heuristic scheduling algorithm to solve it. Th
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McClurg, Josiah. "Fast demand response with datacenter loads: a green dimension of big data." Diss., University of Iowa, 2017. https://ir.uiowa.edu/etd/5811.

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Demand response is one of the critical technologies necessary for allowing large-scale penetration of intermittent renewable energy sources in the electric grid. Data centers are especially attractive candidates for providing flexible, real-time demand response services to the grid because they are capable of fast power ramp-rates, large dynamic range, and finely-controllable power consumption. This thesis makes a contribution toward implementing load shaping with server clusters through a detailed experimental investigation of three broadly-applicable datacenter workload scenarios. We experim
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Radcliffe, Nicholas Ryan. "Adjusting Process Count on Demand for Petascale Global Optimization." Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/36349.

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There are many challenges that need to be met before efficient and reliable computation at the petascale is possible. Many scientific and engineering codes running at the petascale are likely to be memory intensive, which makes thrashing a serious problem for many petascale applications. One way to overcome this challenge is to use a dynamic number of processes, so that the total amount of memory available for the computation can be increased on demand. This thesis describes modifications made to the massively parallel global optimization code pVTdirect in order to allow for a dynamic number o
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Books on the topic "On demand computing"

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Hoskins, Jim. Building an On Demand Computing Environment. Maximum Press, 2003.

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Mike, Ransom, and International Business Machines Corporation. International Technical Support Organization., eds. The solution designer's guide to IBM On Demand business solutions. 3rd ed. IBM, International Technical Support Organization, 2005.

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Beard, Haley. Cloud computing best practices: For managing and measuring processes for on-demand computing, applications and data centers in the cloud with SLAs. Art of Service, 2008.

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Siddiqui, Mumtaz. Grid resource management: On-demand provisioning, advance reservation, and capacity planning of Grid resources. Springer, 2010.

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Italy) Annual Conference on Wireless On-Demand Network Systems and Services (9th 2012 Courmayeur. 2012 9th Annual Conference on Wireless On-Demand Network Systems and Services (WONS 2012): Courmayeur, Italy, 9-11 January 2012. IEEE, 2012.

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Reenen, John Van. Is there a market for work group servers?: Evaluating market level demand elasticities using micro and macro models. Centre for Economic Performance, London School of Economics and Political Science, 2004.

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WONS 2007 (2007 Obergurgl, Austria). 2007 Fourth Annual Conference on Wireless on Demand Network Systems and Services: 24-26 January, 2007 : Obergurgl, Tyrol, Austria. IEEE, 2007.

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WONS 2008 (2008 Garmisch-Partenkirchen, Germany). 2008 Fifth Annual Conference on Wireless on Demand Network Systems and Services: January 23-25, 2008, Garmisch-Partenkirchen, Germany. IEEE, 2008.

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University of Western Ontario. Senate Committee on University Planning. Enrolment Forecasting and External Reporting Subcommittee. Faculty renewal, capital funding, future enrolment pressures and student computing costs: The response of the University of Western Ontario to questions from the Ontario Council on University Affairs. s.n.], 1985.

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1937-, Leff Harvey S., and Rex Andrew F. 1956-, eds. Maxwell's demon: Entropy, information, computing. Princeton University Press, 1990.

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Book chapters on the topic "On demand computing"

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Ocampo, Roel, and Hermann de Meer. "Smart Wireless Access Points for Pervasive Computing." In Wireless On-Demand Network Systems. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24614-5_24.

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Gao, Boquan, and Jingjing Cao. "GCN-ARIMA Based Sales Demand Prediction." In Human Centered Computing. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23741-6_5.

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Song, LianJun, FengQiang Li, and Bo Cong. "Power Demand Side Management Strategy Based on Power Demand Response." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31129-2_91.

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Ben-Arieh, D., and A. M. Choubey. "Platform Formation Under Stochastic Demand." In Evolutionary Computing in Advanced Manufacturing. John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118161883.ch12.

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Ciuffoletti, Augusto, Yari Marchetti, Antonis Papadogiannakis, and Michalis Polychronakis. "Prototype Implementation Of A Demand Driven Network Monitoring Architecture." In Grid Computing. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-09457-1_8.

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Kurze, Tobias, Lizhe Wang, Gregor von Laszewski, et al. "Cyberaide Virtual Applicance: On-Demand Deploying Middleware for Cyberinfrastructure." In Cloud Computing. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12636-9_10.

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Blume, William, and Rudolf Eigenmann. "Demand-driven, symbolic range propagation." In Languages and Compilers for Parallel Computing. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/bfb0014197.

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Ossman, Mazen, and Yaxin Bi. "Electricity Demand Forecast with LSTMs." In Advances in Intelligent Systems and Computing. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-55568-8_31.

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Chong, Z. J., B. Qin, T. Bandyopadhyay, et al. "Autonomy for Mobility on Demand." In Advances in Intelligent Systems and Computing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33926-4_64.

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Li, Zhaoming, Wei He, Ning Liu, Yonghui Xu, Lizhen Cui, and Kaiyuan Qi. "Balancing Supply and Demand for Mobile Crowdsourcing Services." In Service-Oriented Computing. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20984-0_20.

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Conference papers on the topic "On demand computing"

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Vyhmeister, Eduardo, Lorenzo Reyes-Bozo, Dimitrios Bikoulis, Panagiotis E. Kyziropoulos, Gabriel G. Castañé, and Andrea Visentin. "Data Monetization Through Tailored Demand Representation." In 2025 IEEE International Conference on Smart Computing (SMARTCOMP). IEEE, 2025. https://doi.org/10.1109/smartcomp65954.2025.00098.

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Gong, Jianfeng, Yi Qi, Wentao Xu, et al. "Deep-learning-based demand response optimization and prediction model." In International Conference on Cloud Computing, Performance Computing, and Deep Learning, edited by Wanyang Dai and Xiangjie Kong. SPIE, 2024. http://dx.doi.org/10.1117/12.3051186.

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Coskun, Ayse K., Fatih Acun, Quentin Clark, Can Hankendi, and Daniel C. Wilson. "Data Center Demand Response for Sustainable Computing: Myth or Opportunity?" In 2024 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2024. http://dx.doi.org/10.23919/date58400.2024.10546839.

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Tang, Wei, and Weikun Li. "Study on Power Demand Estimation Methods for Computing Power Infrastructure." In 2024 International Conference on Electronics and Devices, Computational Science (ICEDCS). IEEE, 2024. https://doi.org/10.1109/icedcs64328.2024.00034.

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Deepthisre, G., M. Malarvizhi, B. Saravanaprabhu, and V. Santhossh. "IoT Based Demand Side Management System." In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2024. http://dx.doi.org/10.1109/icaccs60874.2024.10716914.

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Bhamare, Archana, Aarti Pawar, Umesh Amate, Omkar Sarolkar, and Prajwal Nalawade. "Machine Learning-based Energy Demand Forecasting." In 2024 8th International Conference on Computing, Communication, Control and Automation (ICCUBEA). IEEE, 2024. https://doi.org/10.1109/iccubea61740.2024.10774655.

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Saradha, K. R., Susila Sakthy S, Arivoli A, and Dhanush M. "Urban Water Demand Prediction System Using LSTM." In 2025 International Conference on Computing and Communication Technologies (ICCCT). IEEE, 2025. https://doi.org/10.1109/iccct63501.2025.11020461.

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Sohan Singh Yadav and Zeng Wen Hua. "CLOUD: A computing infrastructure on demand." In 2010 2nd International Conference on Computer Engineering and Technology. IEEE, 2010. http://dx.doi.org/10.1109/iccet.2010.5486068.

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Zhao, Tianhai, and Yunlan Wang. "Virtual High Performance Computing Environments for Science Computing On-Demand." In 2011 Sixth Chinagrid Annual Conference (ChinaGrid). IEEE, 2011. http://dx.doi.org/10.1109/chinagrid.2011.27.

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Athas, Bill. "Power on demand for mobile computing devices." In the 2008 international conference. ACM Press, 2008. http://dx.doi.org/10.1145/1450095.1450097.

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Reports on the topic "On demand computing"

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Cully, John, and Susie Wright. Cloud computing. Parliamentary Office of Science and Technology, 2020. http://dx.doi.org/10.58248/pn629.

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Cloud computing refers to the delivery of computing services on-demand over the internet. This POSTnote describes the different types of cloud computing before outlining issues relating to security, regulation, energy use and barriers to the adoption of this technology.
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Dutt, Nikil, Rajesh Gupta, Alex Nicolau, and Alex Veidenbaum. COPPER: Compiler-Controlled On-Demand Approach to Power-Efficient Computing. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada418787.

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Pasupuleti, Murali Krishna. Neuromorphic Nanotech: 2D Materials for Energy-Efficient Edge Computing. National Education Services, 2025. https://doi.org/10.62311/nesx/rr325.

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Abstract The demand for energy-efficient, real-time computing is driving the evolution of neuromorphic computing and edge AI systems. Traditional silicon-based processors struggle with power inefficiencies, memory bottlenecks, and scalability limitations, making them unsuitable for next-generation low-power AI applications. This research report explores how 2D materials, such as graphene, transition metal dichalcogenides (TMDs), black phosphorus, and MXenes, are enabling the development of neuromorphic architectures that mimic biological neural networks for high-speed, ultra-low-power computat
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Beyeler, Walter Eugene, Thomas Frank, Jr Corbet, and Jacob A. Hobbs. A demand-driven, capacity-constrained, adaptive algorithm for computing steady-state and transient flows in a petroleum transportation network. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1055878.

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Hale, Elaine, Matt Leach, Brady Cowiestoll, Yashen Lin, and Daniel Levie. Methods for Computing Physically Realistic Estimates of Electric Water Heater Demand Response Resource Suitable for Bulk Power System Planning Models. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1899987.

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Albert, Jose Ramon, Ana Maria Tabunda, Carlos Primo David, et al. Future S&T Human Resource Requirements in the Philippines: A Labor Market Analysis. Philippine Institute for Development Studies, 2020. https://doi.org/10.62986/dp2020.32.

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This study aims to describe the prevailing supply and demand conditions, patterns and recent trends regarding science and technology (S&T) human capital in the Philippines by examining secondary data from various data sources. Overall, the results show that although the scientific workforce constitutes only a minute share of the total workforce, and that the variegated occupations across S&T do not have uniform growth potentials as regards employment, both the government and the private sector need to support most of S&T disciplines, especially in the wake of new tasks from emergin
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Tarko, Andrew P., Jose Thomaz, and Mario Romero. SNIP Light User Manual. Purdue University, 2020. http://dx.doi.org/10.5703/1288284317136.

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A systemic approach to identifying road locations that exhibit safety problems was provided by the Safety Needs Identification Program (SNIP and SNIP2) developed by the Purdue University Center for Road Safety (CRS). The new version SNIP Light has been developed to provide other uses with planning level traffic safety analysis capability for a wider range of uses including Metropolitan Planning Agencies (MPOs) who want the tool for planning cost-effective safety programs in their metropolitan areas. The SNIP Light reduces the demand of computing and data storage resources and replaces the SQL
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Briones, Roehlano. Food and Nutrient Intake Response to Food Prices and Government Programs: Implications for the Recent Economic Shocks. Philippine Institute for Development Studies, 2022. https://doi.org/10.62986/dp2022.44.

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This study aims to estimate the short-term impact of the recent rounds of food price inflation and the COVID-19 crisis by estimating a household food demand system converted to energy and nutrient intakes. It demonstrates that recent food and nutrition surveys conducted by the Philippine government are a valuable source of information about household behavior and the impact of economic shocks. It offers a novel methodology for incorporating computing selection effects in determinants in estimating price and income elasticities. The study was nevertheless able to determine that the COVID-19 soc
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Bailey Bond, Robert, Pu Ren, James Fong, Hao Sun, and Jerome F. Hajjar. Physics-informed Machine Learning Framework for Seismic Fragility Analysis of Steel Structures. Northeastern University, 2024. http://dx.doi.org/10.17760/d20680141.

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The seismic assessment of structures is a critical step to increase community resilience under earthquake hazards. This research aims to develop a Physics-reinforced Machine Learning (PrML) paradigm for metamodeling of nonlinear structures under seismic hazards using artificial intelligence. Structural metamodeling, a reduced-fidelity surrogate model to a more complex structural model, enables more efficient performance-based design and analysis, optimizing structural designs and ease the computational effort for reliability fragility analysis, leading to globally efficient designs while maint
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Abell, Thomas, Husar Arndt, and May-Ann Lim. Cloud Computing as a Key Enabler for Digital Government across Asia and the Pacific. Asian Development Bank, 2021. http://dx.doi.org/10.22617/wps210196-2.

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Governments are responding to rapid change and growing demands by citizens and businesses by accelerating the digitalization of public services. They are updating their e-government capabilities, adding new digital tools and services, augmenting their data analytics capabilities, and putting in place digital economy development plans. Many of these changes are enabled by cloud computing technologies that have become commonplace in the digitally connected world. The rapidly scalable computing resources that cloud computing delivers via the internet bring cost benefits, improve agility, ensure r
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