Academic literature on the topic 'Scalability'
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Journal articles on the topic "Scalability"
Katata, Hiroyuki. "Scalability." Journal of the Institute of Image Information and Television Engineers 51, no. 12 (1997): 1983. http://dx.doi.org/10.3169/itej.51.1983.
Full textMerrell, Ronald C., and Charles R. Doarn. "Scalability." Telemedicine and e-Health 25, no. 4 (April 2019): 261–62. http://dx.doi.org/10.1089/tmj.2019.29021.crd.
Full textMuslihaeny, Siti, Muhammad Ainul Yaqin, and Syahiduz Zaman. "Simulasi Pertumbuhan Scalable Business Process Model pada ERP Pondok Pesantren berbasis Production Rule Cellular Automata." ILKOMNIKA: Journal of Computer Science and Applied Informatics 1, no. 2 (December 31, 2019): 30–38. http://dx.doi.org/10.28926/ilkomnika.v1i2.16.
Full textHorrocks, Ian. "Semantics ⊓ scalability ⊨ ⊥?" Journal of Zhejiang University SCIENCE C 13, no. 4 (April 2012): 241–44. http://dx.doi.org/10.1631/jzus.c1101001.
Full textEick, Stephen G., and Alan F. Karr. "Visual Scalability." Journal of Computational and Graphical Statistics 11, no. 1 (March 2002): 22–43. http://dx.doi.org/10.1198/106186002317375604.
Full textACM Case Study. "Photoshop scalability." Communications of the ACM 53, no. 10 (October 2010): 32–38. http://dx.doi.org/10.1145/1831407.1831423.
Full textSurridge, Christopher. "Scale and scalability." New Phytologist 170, no. 3 (May 2006): 426–28. http://dx.doi.org/10.1111/j.1469-8137.2006.01734.x.
Full textMacri, Dean. "The Scalability Problem." Queue 1, no. 10 (February 2004): 66–73. http://dx.doi.org/10.1145/971564.971594.
Full textBrataas, Gunnar, and Peter Hughes. "Exploring architectural scalability." ACM SIGSOFT Software Engineering Notes 29, no. 1 (January 2004): 125–29. http://dx.doi.org/10.1145/974043.974064.
Full textFalatah, Maram Mohammed, and Omar Abdullah Batarfi. "Cloud Scalability Considerations." International Journal of Computer Science & Engineering Survey 5, no. 4 (August 31, 2014): 37–47. http://dx.doi.org/10.5121/ijcses.2014.5403.
Full textDissertations / Theses on the topic "Scalability"
Singh, Arjun. "The scalability of AspectJ." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/32349.
Full textScience, Faculty of
Computer Science, Department of
Graduate
Li, Yan. "Scalability of RAID systems." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/3382.
Full textDuong, Tuyet. "BLOCKCHAIN SCALABILITY AND SECURITY." VCU Scholars Compass, 2018. https://scholarscompass.vcu.edu/etd/5559.
Full textBen, Alaya Mahdi. "Towards interoperability, self-management, and scalability for scalability for machine-to-machine systems." Thesis, Toulouse, INSA, 2015. http://www.theses.fr/2015ISAT0052/document.
Full textMachine-to-Machine (M2M) is one of the main features of Internet of Things (IoT). It is a phenomenon that has been proceeding quietly in the background, and it is coming into the surface, where explosion of usage scenarios in businesses will happen. Sensors, actuators, tags, vehicles, and intelligent things all have the ability to communicate. The number of M2M connections is continuously increasing, and it has been predicted to see billions of machines interconnected in a near future. M2M applications provide advantages in various domains from smart cities, factories of the future, connected cars, home automation, e-health to precision agriculture. This fast-growing ecosystem is leading M2M towards a promising future. However, M2M market expansion opportunities are not straightforward. A set of challenges should be overcome to enable M2M mass-scale deployment across various industries including interoperability, complexity, and scalability issues. Currently, the M2M market is suffering from a high vertical fragmentation affecting the majority of business sectors. In fact, various vendor-specific M2M solutions have been designed independently for specific applications, which led to serious interoperability issues. To address this challenge, we designed, implemented, and experimented with the OM2M platform offering a flexible and extensible operational architecture for M2M interoperability compliant with the SmartM2M standard. To support constrained environments, we proposed an efficient naming convention relying on a non-hierarchical resource structure to reduce the payload size. To reduce the semantic gap between applications and machines, we proposed the IoT-O ontology for an effective semantic interoperability. IoT-O consists of five main parts, which are sensor, actuator, observation, actuation and service models and aims to quickly converge to a common IoT vocabulary. An interoperable M2M service platform enables one to interconnect heterogeneous devices that are widely distributed and frequently evolving according to their environment changes. Keeping M2M systems alive is costly in terms of time and money. To address this challenge, we designed, implemented, and integrated the FRAMESELF framework to retrofit self-management capabilities in M2M systems based on the autonomic computing paradigm. Extending the MAPE-K reference architecture model, FRAMESELF enables one to dynamically adapt the OM2M system behavior according to high level policies how the environment changes. We defined a set of semantic rules for reasoning about the IoT-O ontology as a knowledge model. Our goal is to enable automatic discovery of machines and applications through dynamic reconfiguration of resource architectures. Interoperability and self-management pave the way to mass-scale deployment of M2M devices. However, current M2M systems rely on current internet infrastructure, which was never designed to address such requirements, thus raising new requirements in term of scalability. To address this challenge, we designed, simulated and validated the OSCL overlay approach, a new M2M meshed network topology as an alternative to the current centralized approach. OSCL relies on the Named Data Networking (NDN) technique and supports multi-hop communication and distributed caching 5 to optimize networking and enhance data dissemination. We developed the OSCLsim simulator to validate the proposed approach. Finally, a theoretical model based on random graphs is formulated to describe the evolution and robustness of the proposed system
Jenefeldt, Andreas, and Jakobsson Erik Foogel. "Scalability in Startups : A Case Study of How Technological Startup Companies Can Enhance Scalability." Thesis, Linköpings universitet, Institutionen för ekonomisk och industriell utveckling, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-168150.
Full textKrishna, Chaitanya Konduru. "Scalability Drivers in Requirements Engineering." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13480.
Full textMir, Taheri Seyed M. "Scalability of communicators in MPI." Thesis, University of British Columbia, 2011. http://hdl.handle.net/2429/33128.
Full textHao, Fang. "Scalability techniques in QoS networks." Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/9175.
Full textWen, Yang Ph D. Massachusetts Institute of Technology. "Scalability of dynamic traffic assignment." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/47739.
Full textIncludes bibliographical references (p. 163-174).
This research develops a systematic approach to analyze the computational performance of Dynamic Traffic Assignment (DTA) models and provides solution techniques to improve their scalability for on-line applications for large-scale networks. DTA models for real-time use provide short-term predictions of network status and generate route guidance for travelers. The computational performance of such systems is a critical concern. Existing methodologies, which have limited capabilities for online large-scale applications, use single-processor configurations that are less scalable, and rely primarily on trade-offs that sacrifice accuracy for improved computational efficiency. In the proposed scalable methodology, algorithmic analyses are first used to identify the system bottlenecks for large-scale problems. Our analyses show that the computation time of DTA systems for a given time interval depends largely on a small set of parameters. Important parameters include the number of origin-destination (OD) pairs, the number of sensors, the number of vehicles, the size of the network, and the number of time-steps used by the simulator. Then scalable approaches are developed to solve the bottlenecks. A constraint generalized least-squares solution enabling efficient use of the sparse-matrix property is applied to the dynamic OD estimation, replacing the Kalman-Filter solution or other full-matrix algorithms. Parallel simulation with an adaptive network decomposition framework is proposed to achieve better load-balancing and improved efficiency. A synchronization-feedback mechanism is designed to ensure the consistency of traffic dynamics across processors while keeping communication overheads minimal. The proposed methodology is implemented in DynaMIT, a state-of-the-art DTA system. Profiling studies are used to validate the algorithmic analysis of the system bottlenecks.
(cont.) The new system is evaluated on two real-world networks under various scenarios. Empirical results of the case studies show that the proposed OD estimation algorithm is insensitive to an increase in the number of OD pairs or sensors, and the computation time is reduced from minutes to a few seconds. The parallel simulation is found to maintain accurate output as compared to the sequential simulation, and with adaptive load-balancing, it considerably speeds up the network models even under non-recurrent incident scenarios. The results demonstrate the practical nature of the methodology and its scalability to large-scale real-world problems.
by Yang Wen.
Ph.D.
Persson, Jonna. "SCALABILITY OF JAVASCRIPTLIBRARIES FOR DATAVISUALIZATION." Thesis, Högskolan i Skövde, Institutionen för ingenjörsvetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-19994.
Full textBooks on the topic "Scalability"
Dhall, Chander. Scalability Patterns. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-1073-4.
Full textZheng, Zibin, Wuhui Chen, and Huawei Huang, eds. Blockchain Scalability. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1059-5.
Full textBulka, Dov. Java performance and scalability. Reading, Mass: Addison-Wesley, 2000.
Find full textLiu, Henry H. Software Performance and Scalability. New York: John Wiley & Sons, Ltd., 2009.
Find full textLanning, Kevin. Consistency, Scalability, and Personality Measurement. New York, NY: Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4612-3072-4.
Full textSadre, Ramin, and Aiko Pras, eds. Scalability of Networks and Services. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02627-0.
Full textAcetozi, Jorge. Pro Java Clustering and Scalability. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2985-9.
Full textPaliouras, Georgios. Scalability of machine learning algorithms. Manchester: University of Manchester, 1993.
Find full textLanning, Kevin Dorsey. Consistency, scalability, and personality measurement. New York: Springer-Verlag, 1991.
Find full textLanning, Kevin Dorsey. Consistency, scalability, and personality measurement. New York: Springer-Verlag, 1991.
Find full textBook chapters on the topic "Scalability"
Palladino, Santiago. "Scalability." In Ethereum for Web Developers, 275–319. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5278-9_8.
Full textWu, Xingfu. "Scalability." In Performance Evaluation, Prediction and Visualization of Parallel Systems, 65–101. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5147-8_3.
Full textStrauss, Rebecca, Austin Volz, and William Lidwell. "Scalability." In The Elements of Education for Curriculum Designers, 78–79. New York: Routledge, 2022. http://dx.doi.org/10.4324/9780429321283-39.
Full textWeik, Martin H. "scalability." In Computer Science and Communications Dictionary, 1517. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_16622.
Full textDongarra, Jack, Piotr Luszczek, Felix Wolf, Jesper Larsson Träff, Patrice Quinton, Hermann Hellwagner, Martin Fränzle, et al. "Scalability." In Encyclopedia of Parallel Computing, 1773. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-09766-4_2046.
Full textTaylor, Ian J., and Andrew B. Harrison. "Scalability." In From P2P and Grids to Services on the Web, 197–211. London: Springer London, 2009. http://dx.doi.org/10.1007/978-1-84800-123-7_11.
Full textStephens, Matt, and Doug Rosenberg. "Scalability." In Extreme Programming Refactored: The Case Against XP, 313–35. Berkeley, CA: Apress, 2003. http://dx.doi.org/10.1007/978-1-4302-0810-5_14.
Full textKale, Vivek. "Scalability." In Digital Transformation of Enterprise Architecture, 169–87. Boca Raton, Florida : CRC Press, [2020]: CRC Press, 2019. http://dx.doi.org/10.1201/9781351029148-10.
Full textWilliams, Michael J. "Scalability." In Preventing and Countering Violent Extremism, 81–84. Abingdon, Oxon; New York, NY: Routledge, 2021. | Series: Political violence: Routledge, 2020. http://dx.doi.org/10.4324/9780429441738-14.
Full textSlama, Dirk. "Scalability." In The Digital Playbook, 103–15. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-88221-1_11.
Full textConference papers on the topic "Scalability"
Timmerman, Benoit, Peter Amon, Andreas Hutter, and Francois-Xavier Coudoux. "Motion information scalability for SNR scalability." In Visual Communications and Image Processing 2005. SPIE, 2005. http://dx.doi.org/10.1117/12.631417.
Full textLeesatapornwongsa, Tanakorn, Cesar A. Stuardo, Riza O. Suminto, Huan Ke, Jeffrey F. Lukman, and Haryadi S. Gunawi. "Scalability Bugs." In HotOS '17: Workshop on Hot Topics in Operating Systems. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3102980.3102985.
Full textHeyman, Thomas, Davy Preuveneers, and Wouter Joosen. "Scalar: Systematic Scalability Analysis with the Universal Scalability Law." In 2014 2nd International Conference on Future Internet of Things and Cloud (FiCloud). IEEE, 2014. http://dx.doi.org/10.1109/ficloud.2014.88.
Full textChauhan, Anamika, Om Prakash Malviya, Madhav Verma, and Tejinder Singh Mor. "Blockchain and Scalability." In 2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C). IEEE, 2018. http://dx.doi.org/10.1109/qrs-c.2018.00034.
Full textRosenblum, David S. "Software system scalability." In Proceeding of the 2nd annual conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1506216.1506217.
Full textDumitru, Alex Mircea, Vlad Merticariu, and Peter Baumann. "Array Database Scalability." In SSDBM '16: Conference on Scientific and Statistical Database Management. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2949689.2949717.
Full textCaprarescu, Bogdan Alexandru. "Robustness and scalability." In the Fourth European Conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1842752.1842759.
Full textBrataas, Gunnar, and Peter Hughes. "Exploring architectural scalability." In the fourth international workshop. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/974044.974064.
Full textYe, Fangdan, Da Yu, Ennan Zhai, Hongqiang Harry Liu, Bingchuan Tian, Qiaobo Ye, Chunsheng Wang, et al. "Accuracy, Scalability, Coverage." In SIGCOMM '20: Annual conference of the ACM Special Interest Group on Data Communication on the applications, technologies, architectures, and protocols for computer communication. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3387514.3406217.
Full textHasselbring, Wilhelm. "Microservices for Scalability." In ICPE'16: ACM/SPEC International Conference on Performance Engineering. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2851553.2858659.
Full textReports on the topic "Scalability"
Pressel, Daniel M. Scalability vs. Performance. Fort Belvoir, VA: Defense Technical Information Center, September 2001. http://dx.doi.org/10.21236/ada396665.
Full textWeinstock, Charles B., and John B. Goodenough. On System Scalability. Fort Belvoir, VA: Defense Technical Information Center, March 2006. http://dx.doi.org/10.21236/ada457003.
Full textClay, Robert L., and Max S. Shneider. iSIGHT-FD scalability test report. Office of Scientific and Technical Information (OSTI), July 2008. http://dx.doi.org/10.2172/973656.
Full textPressel, Daniel M. The Scalability of Loop-Level Parallelism. Fort Belvoir, VA: Defense Technical Information Center, August 2001. http://dx.doi.org/10.21236/ada395393.
Full textPascucci, V. ViSUS: Visualization Streams for Ultimate Scalability. Office of Scientific and Technical Information (OSTI), February 2005. http://dx.doi.org/10.2172/15014677.
Full textPascucci, V. ViSUS: Visualization Streams for Ultimate Scalability. Office of Scientific and Technical Information (OSTI), February 2005. http://dx.doi.org/10.2172/918411.
Full textLipari, D., and M. Jette. Purple Milestone Report System Software and Scalability. Office of Scientific and Technical Information (OSTI), December 2006. http://dx.doi.org/10.2172/896604.
Full textTkac, Peter, David Rotsch, Kevin Quigley, and George Vandegrift. SCALABILITY OF THE LEU-MODIFIED CINTICHEM PROCESS. Office of Scientific and Technical Information (OSTI), October 2014. http://dx.doi.org/10.2172/1165454.
Full textNookala, Munichandraiah. Lithium-Air Battery: Study of Rechargeability and Scalability. Fort Belvoir, VA: Defense Technical Information Center, July 2012. http://dx.doi.org/10.21236/ada564754.
Full textSweeney, John D., Huan Li, Roderic A. Grupen, and Krithi Ramamritham. Scalability and Schedulability in Large, Coordinated, Distributed Robot Systems. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada438795.
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