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Journal articles on the topic 'Scalable computing'

Author: Grafiati

Published: 10 March 2023

Last updated: 6 September 2023

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1

Gusev, Marjan. "Scalable Dew Computing." Applied Sciences 12, no. 19 (September 22, 2022): 9510. http://dx.doi.org/10.3390/app12199510.

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Dew computing differs from the classical cloud and edge computing by bringing devices closer to the end-users and adding autonomous processing independent from the Internet, but it is still able to collaborate with other devices to exchange information on the Internet. The difference is expressed also on scalability, since edge and cloud providers can provide (almost endless) resources, and in the case of dew computing the scalability needs to be realized on the level of devices, instead of servers. In this paper, we introduce an approach to provide deviceless and thingless computing and ensure scalable dew computing. The deviceless approach allows functions to be executed on nearby devices found closer to the user, and the thingless approach goes even further, providing scalability on a low-level infrastructure that consists of multiple things, such as IoT devices. These approaches introduce the distribution of computing to other smart devices or things on a lower architectural level. Such an approach enhances the existing dew computing architectural model as a sophisticated platform for future generation IoT systems.

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2

Venkatasubramanian, Nalini, Shakuntala Miriyala, and Gul Agha. "Scalable concurrent computing." Sadhana 17, no. 1 (March 1992): 193–220. http://dx.doi.org/10.1007/bf02811343.

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3

Keidar, Idit, and Assaf Schuster. "Want scalable computing?" ACM SIGACT News 37, no. 3 (September 2006): 59–66. http://dx.doi.org/10.1145/1165555.1165569.

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4

Rouson, Damian W. I. "Complexity in Scalable Computing." Scientific Programming 16, no. 4 (2008): 275–76. http://dx.doi.org/10.1155/2008/693705.

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5

DeBenedictis, E. P., and S. C. Johnson. "Extending Unix for scalable computing." Computer 26, no. 11 (November 1993): 43–53. http://dx.doi.org/10.1109/2.241425.

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6

Banerjee, S., S. Agarwal, K. Kamel, A. Kochut, C. Kommareddy, T. Nadeem, P. Thakkar, et al. "Rover: scalable location-aware computing." Computer 35, no. 10 (October 2002): 46–53. http://dx.doi.org/10.1109/mc.2002.1039517.

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7

Alexandrov, Vassil. "Towards scalable mathematics and scalable algorithms for extreme scale computing." Journal of Computational Science 4, no. 6 (November 2013): iii—v. http://dx.doi.org/10.1016/s1877-7503(13)00120-8.

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8

Liu, Zhi, Cheng Zhan, Ying Cui, Celimuge Wu, and Han Hu. "Robust Edge Computing in UAV Systems via Scalable Computing and Cooperative Computing." IEEE Wireless Communications 28, no. 5 (October 2021): 36–42. http://dx.doi.org/10.1109/mwc.121.2100041.

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9

Barrett, Sean D., Peter P. Rohde, and Thomas M. Stace. "Scalable quantum computing with atomic ensembles." New Journal of Physics 12, no. 9 (September 22, 2010): 093032. http://dx.doi.org/10.1088/1367-2630/12/9/093032.

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10

Jararweh, Yaser, Lo’ai Tawalbeh, Fadi Ababneh, Abdallah Khreishah, and Fahd Dosari. "Scalable Cloudlet-based Mobile Computing Model." Procedia Computer Science 34 (2014): 434–41. http://dx.doi.org/10.1016/j.procs.2014.07.051.

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11

Kaur, Pankaj Deep, and Gitanjali Sharma. "Scalable Database Management in Cloud Computing." Procedia Computer Science 70 (2015): 658–67. http://dx.doi.org/10.1016/j.procs.2015.10.102.

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12

Min, Geyong, Ahmed Al-Dubai, Jia Hu, and James Gao. "Guest editorial: scalable computing and communications." Telecommunication Systems 55, no. 3 (July 20, 2013): 331–32. http://dx.doi.org/10.1007/s11235-013-9790-2.

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13

Wu, Xingfu, and Wei Li. "Performance models for scalable cluster computing." Journal of Systems Architecture 44, no. 3-4 (January 1998): 189–205. http://dx.doi.org/10.1016/s1383-7621(97)00036-2.

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14

Rathore, Nitin, and Anand Rajavat. "Scalable Edge Computing Environment Based on the Containerized Microservices and Minikube." International Journal of Software Science and Computational Intelligence 14, no. 1 (January 1, 2022): 1–14. http://dx.doi.org/10.4018/ijssci.312560.

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The growing number of connected IoT devices and their continuous data collection will generate huge amounts of data in the near future. Edge computing has emerged as a new paradigm in recent years for reducing network congestion and offering real-time IoT applications. Processing the large amount of data generated by such IoT devices requires the development of a scalable edge computing environment. Accordingly, applications deployed in an edge computing environment need to be scalable enough to handle the enormous amount of data generated by IoT devices. The performance of MSA and monolithic architecture is analyzed and compared to develop a scalable edge computing environment. An auto-scaling approach is described to handle multiple concurrent requests at runtime. Minikube is used to perform auto-scaling operation of containerized microservices on resource constraint edge node. Considering performance of both the architecture and according to the results and discussions, MSA is a better choice for building scalable edge computing environment.

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15

Karaivanova, Aneta, and Svetozar Margenov. "Preface." Cybernetics and Information Technologies 20, no. 6 (December 1, 2020): 3–4. http://dx.doi.org/10.2478/cait-2020-0055.

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Abstract We are pleased to present the special issue “New developments in scalable computing” of the scientific journal “Cybernetics and Information Technologies”. For this issue (Volume 20, No 6 – December 2020), we have selected 19 papers which have gone through peer review and represent novel results in the field of Scalable Computing using state-of-the-art high-performance computing infrastructures.

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16

Lingkau, Mingalu Pangare, Kuo Ling Haoseng, Mingalu Pangare Lingkau, Mingalu Pangare Lingkau, and Yong Meng Phaotangu. "Healthcare and IoT devices: role of information technology in the healthcare industry." Business & IT XII, no. 1 (2022): 169–76. http://dx.doi.org/10.14311/bit.2022.01.20.

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Today, wearable health products play a crucial role in most locations, such as constant wellness monitoring of people, street traffic management, weather forecasting, along with smart house. These sensor devices constantly generate massive amounts of data and are kept in cloud computing. This particular chapter proposes Internet of Things design to store and system scalable sensor information for healthcare apps. Proposed architecture comprises 2 primary architecture, specifically, MetaFog-Redirection and Choosing and Grouping architecture. Though cloud computing offers scalable data storage, effective computing platforms must process it. There's a requirement for scalable algorithms to process the big sensor information and recognize the helpful patterns. To conquer this problem, this particular chapter proposes a scalable MapReduce based logistic regression to process such massive quantities of sensor information. Apache Mahout includes scalable logistic regression to system BDA in a distributed way. This particular chapter uses Apache Mahout with Hadoop Distributed File System to process the sensor information produced by the wearable health units.

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17

Lim, Jongbeom. "Scalable Fog Computing Orchestration for Reliable Cloud Task Scheduling." Applied Sciences 11, no. 22 (November 19, 2021): 10996. http://dx.doi.org/10.3390/app112210996.

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As Internet of Things (IoT) and Industrial Internet of Things (IIoT) devices are becoming increasingly popular in the era of the Fourth Industrial Revolution, the orchestration and management of numerous fog devices encounter a scalability problem. In fog computing environments, to embrace various types of computation, cloud virtualization technology is widely used. With virtualization technology, IoT and IIoT tasks can be run on virtual machines or containers, which are able to migrate from one machine to another. However, efficient and scalable orchestration of migrations for mobile users and devices in fog computing environments is not an easy task. Naïve or unmanaged migrations may impinge on the reliability of cloud tasks. In this paper, we propose a scalable fog computing orchestration mechanism for reliable cloud task scheduling. The proposed scalable orchestration mechanism considers live migrations of virtual machines and containers for the edge servers to reduce both cloud task failures and suspended time when a device is disconnected due to mobility. The performance evaluation shows that our proposed fog computing orchestration is scalable while preserving the reliability of cloud tasks.

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18

REN Li, DAN Yang, HAIBO Hu, JIANHUA Luo, and JUAN Xie. "Scalable OWL Ontology Reasoning using Cloud Computing." International Journal of Advancements in Computing Technology 4, no. 21 (November 30, 2012): 623–31. http://dx.doi.org/10.4156/ijact.vol4.issue21.74.

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19

Li, Songze, Qian Yu, Mohammad Ali Maddah-Ali, and A. Salman Avestimehr. "A Scalable Framework for Wireless Distributed Computing." IEEE/ACM Transactions on Networking 25, no. 5 (October 2017): 2643–54. http://dx.doi.org/10.1109/tnet.2017.2702605.

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20

White, Ian, Eng Tin Aw, Kevin Williams, Haibo Wang, Adrian Wonfor, and Richard Penty. "Scalable optical switches for computing applications [Invited]." Journal of Optical Networking 8, no. 2 (January 28, 2009): 215. http://dx.doi.org/10.1364/jon.8.000215.

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21

Holmes, Lewis M. "Supercomputing Conference Emphasizes Scalable Computing and Networks." Computers in Physics 8, no. 1 (1994): 8. http://dx.doi.org/10.1063/1.4823268.

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22

Sun, Xian-He, Thomas Fahringer, and Mario Pantano. "Scala: A Performance System for Scalable Computing." International Journal of High Performance Computing Applications 16, no. 4 (November 2002): 357–70. http://dx.doi.org/10.1177/109434200201600401.

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Summary Lack of effective performance-evaluation environments is a major barrier to the broader use of high performance computing. Conventional performance environments are based on profiling and event instrumentation. It becomes problematic as parallel systems scale to hundreds of nodes and beyond. A framework of developing an integrated performance modeling and prediction system, SCALability Analyzer (SCALA), is presented in this study. In contrast to existing performance tools, the program performance model generated by SCALA is based on scalability analysis. SCALA assumes the availability of modern compiler technology, adopts statistical and symbolic methodologies, and has the support of browser interface. These technologies, together with anew approach of scalability analysis, enable SCALA to provide the user with a more intuitive level of performance analysis for scalable computing. A prototype SCALA system has been implemented. Initial experimental results show that SCALA is unique in its ability of revealing the scaling properties of a computing system.

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23

Spreitzer, Michael, and Marvin Theimer. "Scalable, secure, mobile computing with location information." Communications of the ACM 36, no. 7 (July 1993): 27. http://dx.doi.org/10.1145/159544.159558.

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24

Stpiczynski, P. "Valuable Resources on Aspects of Scalable Computing." IEEE Distributed Systems Online 5, no. 12 (December 2004): 4. http://dx.doi.org/10.1109/mdso.2004.38.

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25

Bryant, Randal E. "Data-Intensive Scalable Computing for Scientific Applications." Computing in Science & Engineering 13, no. 6 (November 2011): 25–33. http://dx.doi.org/10.1109/mcse.2011.73.

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26

Van Meter, Rodney, and Simon J. Devitt. "The Path to Scalable Distributed Quantum Computing." Computer 49, no. 9 (September 2016): 31–42. http://dx.doi.org/10.1109/mc.2016.291.

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27

Barak, Amnon, and Avner Braverman. "Memory ushering in a scalable computing cluster." Microprocessors and Microsystems 22, no. 3-4 (August 1998): 175–82. http://dx.doi.org/10.1016/s0141-9331(98)00077-5.

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28

Clement, Mark J., and Michael J. Quinn. "Automated performance prediction for scalable parallel computing." Parallel Computing 23, no. 10 (October 1997): 1405–20. http://dx.doi.org/10.1016/s0167-8191(97)00066-5.

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29

Desell, Travis, Kaoutar El Maghraoui, and Carlos A. Varela. "Malleable applications for scalable high performance computing." Cluster Computing 10, no. 3 (June 28, 2007): 323–37. http://dx.doi.org/10.1007/s10586-007-0032-9.

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30

Pradhan, Padma Lochan. "Impact of Cryptographic Key on Scalable Computing." International Journal of Security and Privacy in Pervasive Computing 14, no. 1 (January 1, 2022): 1–17. http://dx.doi.org/10.4018/ijsppc.313046.

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The research has contributed to the development of the cryptographic control on the proposed RTS that aims to determine the high-performance computing at optimal cost and time to be invested into dynamic cryptographic control that decides on the major components of real-time operating system resources. Furthermore, the mechanism optimizes the cost, and resources are supposed to optimize the operating system risks. We have to optimize the technology and resource cost and maximizes the productivity and business (throughput) while improving the high performance of the operating system as per business requirement for the multiple locations. This proposed cryptographic control on the real-time system provides high computational services around the clock. The objective should be defined in such a way that the processor, memory, and encryption key are always utilized at minimal cost with high availability of data and services as per business and resource management.

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31

Qu, Wenyu, Zhaobin Liu, and Kai Lin. "Special issue: advanced topics on scalable computing." Concurrency and Computation: Practice and Experience 22, no. 13 (August 20, 2010): 1849–51. http://dx.doi.org/10.1002/cpe.1653.

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32

Carlin, Sean, and Kevin Curran. "Cloud Computing Security." International Journal of Ambient Computing and Intelligence 3, no. 1 (January 2011): 14–19. http://dx.doi.org/10.4018/jaci.2011010102.

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In this paper, the authors focus on Cloud Computing, which is a distributed architecture that centralizes server resources on quite a scalable platform so as to provide on demand’ computing resources and services The authors outline what cloud computing is, the various cloud deployment models and the main security risks and issues that are currently present within the cloud computing industry.

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33

Jayswal, Anant Kumar. "Hybrid Load-Balanced Scheduling in Scalable Cloud Environment." International Journal of Information System Modeling and Design 11, no. 3 (July 2020): 62–78. http://dx.doi.org/10.4018/ijismd.2020070104.

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Cloud computing is a high computational distributed environment with high reliability and quality of service. It is playing an important role in the next generation of computing with pay per use model and high elasticity. With increased requirement for cloud resources, load over the cloud servers has increased, which makes cloud use a more efficient algorithm to maintain its performance and quality of service to users. The performance metrics that define the performance of task scheduling include execution time, finish time, scheduling time, task completion cost, and load balancing on each computing resources. So, to overcome existing solutions and provide better QoS performance, a neural-network-based GA-ANN scheduling algorithm is proposed in this paper, which outperforms the existing solutions. To simulate the proposed GA-ANN model, cloudsim3.0 toolkit is used, and the performance is evaluated by comparing simulation time, average start time, average finish time, execution time, and utilization percentage of computing resources (VMs).

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34

Balamurugan, S., K. Deepika, R. S. Venkatesh, R. Poornima, Gokul Kruba Shanker, and V. S. Duruvak Kumar. "SUN Computing: Scalable Ubiquitous Nestle (SUN) Computing for Healing on the IoT." Asian Journal of Research in Social Sciences and Humanities 6, no. 8 (2016): 650. http://dx.doi.org/10.5958/2249-7315.2016.00640.7.

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35

Briscoe, Gerard, and Philippe De Wilde. "The Computing of Digital Ecosystems." International Journal of Organizational and Collective Intelligence 1, no. 4 (October 2010): 1–17. http://dx.doi.org/10.4018/joci.2010100101.

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A primary motivation this research in digital ecosystems is the desire to exploit the self-organising properties of biological ecosystems. Ecosystems are thought to be robust, scalable architectures that can automatically solve complex and dynamic problems. However, the computing technologies that contribute to these properties have not been made explicit in digital ecosystems research. In this paper, the authors discuss how different computing technologies can contribute to providing the necessary self-organising features, including Multi-Agent Systems (MASs), Service-Oriented Architectures (SOAs), and distributed evolutionary computing (DEC). The potential for exploiting these properties in digital ecosystems is considered, suggesting how several key features of biological ecosystems can be exploited in Digital Ecosystems, and discussing how mimicking these features may assist in developing robust, scalable self-organising architectures. An example architecture, the Digital Ecosystem, is considered in detail. The Digital Ecosystem is then measured experimentally through simulations, which consider the self-organised diversity of its evolving agent populations relative to the user request behaviour.

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36

Ravi, Vijayalakshmi. "Cloud Computing Paradigm for Indian Education Sector." International Journal of Cloud Applications and Computing 2, no. 2 (April 2012): 41–47. http://dx.doi.org/10.4018/ijcac.2012040104.

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The Indian education sector is constrained by cost but demand has been rising for cost-effective, robust software applications to deliver services for learning and administration. Existing systems are not scalable and require huge capex (Capital Expenditure) and IT staff to maintain the system, which has shifted the focus from the core education business to managing the overheads of IT operations. The Cloud Computing paradigm has emerged as the optimal solution to meet the requirements of cost effective, scalable, and secure systems. In this paper, the author examines how the deployment of cloud computing in the education sector in India can meet these challenges.

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37

Srivastava, Divya. "Serverless and IaC." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 3271–79. http://dx.doi.org/10.22214/ijraset.2023.51356.

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Abstract: The objective of this research paper is to investigate the potential advantages of integrating Terraform and Serverless Computing to construct a scalable and efficient cloud infrastructure. Terraform, an infrastructure-as-code open-source tool, and Serverless Computing, a new paradigm that enables developers to run code without worrying about the underlying infrastructure, are briefly described along with their benefits. The paper then explores how these two can be combined to build a dynamic and robust infrastructure while also addressing the difficulties that arise when constructing a Serverless Computing infrastructure with Terraform, proposing solutions to overcome them. Finally, the study concludes that integrating Terraform and Serverless Computing can help organizations create an efficient, adaptable, and scalable infrastructure while decreasing expenses and enhancing developer productivity.

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38

Shi, Lizhen, and Zhong Wang. "Computational Strategies for Scalable Genomics Analysis." Genes 10, no. 12 (December 6, 2019): 1017. http://dx.doi.org/10.3390/genes10121017.

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The revolution in next-generation DNA sequencing technologies is leading to explosive data growth in genomics, posing a significant challenge to the computing infrastructure and software algorithms for genomics analysis. Various big data technologies have been explored to scale up/out current bioinformatics solutions to mine the big genomics data. In this review, we survey some of these exciting developments in the applications of parallel distributed computing and special hardware to genomics. We comment on the pros and cons of each strategy in the context of ease of development, robustness, scalability, and efficiency. Although this review is written for an audience from the genomics and bioinformatics fields, it may also be informative for the audience of computer science with interests in genomics applications.

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39

Geyer, Simon, Leon C. Camenzind, Lukas Czornomaz, Veeresh Deshpande, Andreas Fuhrer, Richard J. Warburton, Dominik M. Zumbühl, and Andreas V. Kuhlmann. "Self-aligned gates for scalable silicon quantum computing." Applied Physics Letters 118, no. 10 (March 8, 2021): 104004. http://dx.doi.org/10.1063/5.0036520.

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40

El-Sayed, Gamal, and Aref Abdullah. "Fault-tolerant scalable hierarchical scheduling in grid computing." International Conference on Electrical Engineering 8, no. 8 (May 1, 2012): 1–27. http://dx.doi.org/10.21608/iceeng.2012.32716.

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41

Dai Senior, Hong-Ning, Zibin Zheng, Yan Zhang, Michael Rung Tsong Lyu, and Alberto Nannarelli. "Special Section on Scalable Computing for Blockchain Systems." IEEE Transactions on Emerging Topics in Computing 9, no. 3 (July 1, 2021): 1372. http://dx.doi.org/10.1109/tetc.2021.3106180.

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42

Gao, Yunlong, Ying Cui, Xinyun Wang, and Zhi Liu. "Optimal Resource Allocation for Scalable Mobile Edge Computing." IEEE Communications Letters 23, no. 7 (July 2019): 1211–14. http://dx.doi.org/10.1109/lcomm.2019.2916075.

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43

Manaa, Mehdi Ebady, and Zuhair Gheni Hadi. "Scalable and robust cryptography approach using cloud computing." Journal of Discrete Mathematical Sciences and Cryptography 23, no. 7 (June 4, 2020): 1439–45. http://dx.doi.org/10.1080/09720529.2020.1727609.

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44

Cappello, Peter, and Dimitrios Mourloukos. "CX: A Scalable, Robust Network for Parallel Computing." Scientific Programming 10, no. 2 (2002): 159–71. http://dx.doi.org/10.1155/2002/598245.

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CX, a network-based computational exchange, is presented. The system's design integrates variations of ideas from other researchers, such as work stealing, non-blocking tasks, eager scheduling, and space-based coordination. The object-oriented API is simple, compact, and cleanly separates application logic from the logic that supports interprocess communication and fault tolerance. Computations, of course, run to completion in the presence of computational hosts that join and leave the ongoing computation. Such hosts, or producers, use task caching and prefetching to overlap computation with interprocessor communication. To break a potential task server bottleneck, a network of task servers is presented. Even though task servers are envisioned as reliable, the self-organizing, scalable network ofn- servers, described as asibling-connected height-balanced fat tree, tolerates a sequence ofn-1 server failures. Tasks are distributed throughout the server network via a simple "diffusion" process. CX is intended as a test bed for research on automated silent auctions, reputation services, authentication services, and bonding services. CX also provides a test bed for algorithm research into network-based parallel computation.

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45

Copsey, D., M. Oskin, F. Impens, T. Metodiev, A. Cross, F. T. Chong, I. L. Chuang, and J. Kubiatowicz. "Toward a scalable, silicon-based quantum computing architecture." IEEE Journal of Selected Topics in Quantum Electronics 9, no. 6 (November 2003): 1552–69. http://dx.doi.org/10.1109/jstqe.2003.820922.

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46

May, Dave A., and Matthew G. Knepley. "Optimal, scalable forward models for computing gravity anomalies." Geophysical Journal International 187, no. 1 (August 24, 2011): 161–77. http://dx.doi.org/10.1111/j.1365-246x.2011.05167.x.

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47

Roth, Philip C., and R. Shane Canon. "Special Issue on Data-Intensive Scalable Computing Systems." Parallel Computing 61 (January 2017): 1–2. http://dx.doi.org/10.1016/j.parco.2017.01.001.

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48

Stößer, Jochen, and Dirk Neumann. "GreedEx—a scalable clearing mechanism for utility computing." Electronic Commerce Research 8, no. 4 (October 17, 2008): 235–53. http://dx.doi.org/10.1007/s10660-008-9023-z.

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49

You, J. Q., J. S. Tsai, and Franco Nori. "Experimentally realizable scalable quantum computing using superconducting qubits." Physica E: Low-dimensional Systems and Nanostructures 18, no. 1-3 (May 2003): 35–36. http://dx.doi.org/10.1016/s1386-9477(02)00946-3.

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50

Katsinis, Constantine, and Bahram Nabet. "A Scalable Interconnection Network Architecture for Petaflops Computing." Journal of Supercomputing 27, no. 2 (February 2004): 103–28. http://dx.doi.org/10.1023/b:supe.0000009318.91562.b0.

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