Relevant bibliographies by topics / Cloud workflow

Contents

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

Academic literature on the topic 'Cloud workflow'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Cloud workflow"

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Lu, Pingping, Gongxuan Zhang, Zhaomeng Zhu, Xiumin Zhou, Jin Sun, and Junlong Zhou. "A Review of Cost and Makespan-Aware Workflow Scheduling in Clouds." Journal of Circuits, Systems and Computers 28, no. 06 (2019): 1930006. http://dx.doi.org/10.1142/s021812661930006x.

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Scientific workflow is a common model to organize large scientific computations. It borrows the concept of workflow in business activities to manage the complicated processes in scientific computing automatically or semi-automatically. The workflow scheduling, which maps tasks in workflows to parallel computing resources, has been extensively studied over years. In recent years, with the rise of cloud computing as a new large-scale distributed computing model, it is of great significance to study workflow scheduling problem in the cloud. Compared with traditional distributed computing platform
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Dr.S.M., Jagateesan1 V.M.Pavithra2. "A SURVEY ON BIGDATA SCHEDULING ON CLOUD FRAMEWORK." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 7 (2017): 922–27. https://doi.org/10.5281/zenodo.834614.

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Computational science workflows have been successfully run on traditional High Performance Computing (HPC) systems like clusters and Grids for many years. Now a day, users are interested to execute their workflow applications in the Cloud to exploit the economic and technical benefits of this new rising technology. The deployment and management of workflows over the current existing heterogeneous and not yet interoperable Cloud providers, is still a challenging task for the workflow developers. The Pointer Gossip Content Addressable Network Montage Framework allows an automatic selection of th
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Lakhwani, Kamlesh, Gajanand Sharma, Ramandeep Sandhu, et al. "Adaptive and Convex Optimization-Inspired Workflow Scheduling for Cloud Environment." International Journal of Cloud Applications and Computing 13, no. 1 (2023): 1–25. http://dx.doi.org/10.4018/ijcac.324809.

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Scheduling large-scale and resource-intensive workflows in cloud infrastructure is one of the main challenges for cloud service providers (CSPs). Cloud infrastructure is more efficient when virtual machines and other resources work up to their full potential. The main factor that influences the quality of cloud services is the distribution of workflow on virtual machines (VMs). Scheduling tasks to VMs depends on the type of workflow and mechanism of resource allocation. Scientific workflows include large-scale data transfer and consume intensive resources of cloud infrastructures. Therefore, s
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Hanoosh, Zaid. "A Survey on Multiple Workflow Scheduling Algorithms in Cloud Environment." Al-Furat Journal of Innovations in Electronics and Computer Engineering 3, no. 1 (2024): 36–44. http://dx.doi.org/10.46649/fjiece.v3.1.4a.13.4.2024.

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The workflow approach is a standard for displaying processes and their implementation process. With the advent of electrical sciences, more cumbersome workflows were created with more processing requirements. New distributed systems, such as grid systems and computing clouds, allow users to access heterogeneous resources that are geographically located at different points and execute their workflow tasks. Therefore, the simultaneous receipt and execution of several workflows is obvious. As a result of discussing scheduling algorithms, it is necessary to consider arrangements for implementing m
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Malawski, Maciej, Kamil Figiela, Marian Bubak, Ewa Deelman, and Jarek Nabrzyski. "Scheduling Multilevel Deadline-Constrained Scientific Workflows on Clouds Based on Cost Optimization." Scientific Programming 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/680271.

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This paper presents a cost optimization model for scheduling scientific workflows on IaaS clouds such as Amazon EC2 or RackSpace. We assume multiple IaaS clouds with heterogeneous virtual machine instances, with limited number of instances per cloud and hourly billing. Input and output data are stored on a cloud object store such as Amazon S3. Applications are scientific workflows modeled as DAGs as in the Pegasus Workflow Management System. We assume that tasks in the workflows are grouped into levels of identical tasks. Our model is specified using mathematical programming languages (AMPL an
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Shang, Shi Feng, Jing He Huo, and Zeng Zhang. "QBARM: A Queue Theory-Based Adaptive Resource Usage Model." Advanced Materials Research 756-759 (September 2013): 2523–27. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.2523.

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Workflow is becoming a more and more important tool for business operations, scientific research and engineering. Cloud computing provides an elastic, on-demand and high cost-efficient resource allocation model for workflow executions. During workflow execution, the load will change from time to time and therefore, it becomes an interesting topic to optimize resource utilization of workflows in the cloud computing environment. In this paper, a workflow framework is proposed that can adaptively use cloud resources. In detail, after users specify the desired goal to achieve, the proposed workflo
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Wu, Lei, and Yuandou Wang. "Scheduling Multi-Workflows Over Heterogeneous Virtual Machines With a Multi-Stage Dynamic Game-Theoretic Approach." International Journal of Web Services Research 15, no. 4 (2018): 82–96. http://dx.doi.org/10.4018/ijwsr.2018100105.

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Cloud computing, with dependable, consistent, pervasive, and inexpensive access to geographically distributed computational capabilities, is becoming an increasingly popular platform for the execution of scientific applications such as scientific workflows. Scheduling multiple workflows over cloud infrastructures and resources is well recognized to be NP-hard and thus critical to meeting various types of Quality-of-Service (QoS) requirements. In this work, the authors consider a multi-objective scientific workflow scheduling framework based on the dynamic game-theoretic model. It aims at reduc
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Wu, Lei, Ran Ding, Zhaohong Jia, and Xuejun Li. "Cost-Effective Resource Provisioning for Real-Time Workflow in Cloud." Complexity 2020 (March 30, 2020): 1–15. http://dx.doi.org/10.1155/2020/1467274.

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In the era of big data, mining and analysis of the enormous amount of data has been widely used to support decision-making. This complex process including huge-volume data collecting, storage, transmission, and analysis could be modeled as workflow. Meanwhile, cloud environment provides sufficient computing and storage resources for big data management and analytics. Due to the clouds providing the pay-as-you-go pricing scheme, executing a workflow in clouds should pay for the provisioned resources. Thus, cost-effective resource provisioning for workflow in clouds is still a critical challenge
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Kaur, Avinash, Pooja Gupta, Parminder Singh, and Manpreet Singh. "Data Placement Oriented Scheduling Algorithm for Scheduling Scientific Workflow in Cloud: A Budget-Aware Approach." Recent Advances in Computer Science and Communications 13, no. 5 (2020): 871–83. http://dx.doi.org/10.2174/2666255813666190925141324.

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Background: A large number of communities and enterprises deploy numerous scientific workflow applications on cloud service. Aims: The main aim of the cloud service provider is to execute the workflows with a minimal budget and makespan. Most of the existing techniques for budget and makespan are employed for the traditional platform of computing and are not applicable to cloud computing platforms with unique resource management methods and pricing strategies based on service. Methods: In this paper, we studied the joint optimization of cost and makespan of scheduling workflows in IaaS clouds,
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Akter, Sohana. "Harmonizing Compliance: Coordinating Automated Verification Processes within Cloud-based AI/ML Workflows." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 3, no. 1 (2024): 292–302. http://dx.doi.org/10.60087/jaigs.v3i1.121.

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The significance of ensuring security and upholding data privacy within cloud-based workflows is widely recognized in research domains. This importance is particularly evident in contexts such as safeguarding patients' private data managed within cloud-deployed workflows, where maintaining confidentiality is paramount, alongside ensuring secure communication among involved stakeholders. In response to these imperatives, our paper presents an architecture and formal model designed to enforce security measures within cloud workflow orchestration. Central to our proposed architecture is the empha
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Dissertations / Theses on the topic "Cloud workflow"

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Chaudhry, Nauman Riaz. "Workflow framework for cloud-based distributed simulation." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/14778.

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Although distributed simulation (DS) using parallel computing has received considerable research and development in a number of compute-intensive fields, it has still to be significantly adopted by the wider simulation community. According to scientific literature, major reasons for low adoption of cloud-based services for DS execution are the perceived complexities of understanding and managing the underlying architecture and software for deploying DS models, as well as the remaining challenges in performance and interoperability of cloud-based DS. The focus of this study, therefore, has been
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Lopez, Israel Casas. "Scientific Workflow Scheduling for Cloud Computing Environments." Thesis, The University of Sydney, 2016. http://hdl.handle.net/2123/16769.

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The scheduling of workflow applications consists of assigning their tasks to computer resources to fulfill a final goal such as minimizing total workflow execution time. For this reason, workflow scheduling plays a crucial role in efficiently running experiments. Workflows often have many discrete tasks and the number of different task distributions possible and consequent time required to evaluate each configuration quickly becomes prohibitively large. A proper solution to the scheduling problem requires the analysis of tasks and resources, production of an accurate environment model and, m
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Cao, Fei. "Efficient Scientific Workflow Scheduling in Cloud Environment." OpenSIUC, 2014. https://opensiuc.lib.siu.edu/dissertations/802.

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Cloud computing enables the delivery of remote computing, software and storage services through web browsers following pay-as-you-go model. In addition to successful commercial applications, many research efforts including DOE Magellan Cloud project focus on discovering the opportunities and challenges arising from the computing and data-intensive scientific applications that are not well addressed by the current supercomputers, Linux clusters and Grid technologies. The elastic resource provision, noninterfering resource sharing and flexible customized configuration provided by the Cloud infra
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Gonzalez, Nelson Mimura. "MPSF: cloud scheduling framework for distributed workflow execution." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-03032017-083914/.

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Cloud computing represents a distributed computing paradigm that gained notoriety due to its properties related to on-demand elastic and dynamic resource provisioning. These characteristics are highly desirable for the execution of workflows, in particular scientific workflows that required a great amount of computing resources and that handle large-scale data. One of the main questions in this sense is how to manage resources of one or more cloud infrastructures to execute workflows while optimizing resource utilization and minimizing the total duration of the execution of tasks (makespan). T
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Ahmad, M. K. H. "Scientific workflow execution reproducibility using cloud-aware provenance." Thesis, University of the West of England, Bristol, 2016. http://eprints.uwe.ac.uk/27390/.

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Scientific experiments and projects such as CMS and neuGRIDforYou (N4U) are annually producing data of the order of Peta-Bytes. They adopt scientific workflows to analyse this large amount of data in order to extract meaningful information. These workflows are executed over distributed resources, both compute and storage in nature, provided by the Grid and recently by the Cloud. The Cloud is becoming the playing field for scientists as it provides scalability and on-demand resource provisioning. Reproducing a workflow execution to verify results is vital for scientists and have proven to be a
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Salvucci, Enrico. "MLOps - Standardizing the Machine Learning Workflow." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23645/.

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MLOps is a very recent approach aimed at reducing the time to get a Machine Learning model in production; this methodology inherits its main features from DevOps and applies them to Machine Learning, by adding more features specific for Data Analysis. This thesis, which is the result of the internship at Data Reply, is aimed at studying this new approach and exploring different tools to build an MLOps architecture; another goal is to use these tools to implement an MLOps architecture (by using preferably Open Source software). This study provides a deep analysis of MLOps features, also compa
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Brodard, Zacharie. "Workflow management and scheduling in a cloud computing context." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-249714.

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Public cloud providers have had a tremendous impact on the software engineering world recently by offering on-demand computing infrastructures and scalable managed solutions. The goal of this master degree project is to determine whether these services offered by public cloud providers can improve the design of workflow management and scheduling systems for data-driven workflows. A cloud based architecture for a distributed workflow management system was designed, implemented and experimented on. It allowed to confirm the value of cloud computing solutions to solve workflow scheduling problems
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Sharif, Nabavi Shaghayeh. "Privacy-Aware Workflow Scheduling Algorithms in Hybrid Cloud Environments." Thesis, The University of Sydney, 2017. http://hdl.handle.net/2123/16576.

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Hybrid clouds have gained popularity with many organisations in recent times due to their ability to provide additional computing capacity to private clouds. A hybrid cloud complements private cloud computing resources when there is resource scarcity. However, deploying distributed applications, such as work ows, in hybrid clouds introduces new challenges such as the privacy of tasks and data in scheduling work ows. The key problem is the danger of exposing private data and tasks in a third-party public cloud infrastructure, especially in healthcare applications. In this thesis, we tackle the
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Chen, Ziwei. "Workflow Management Service based on an Event-driven Computational Cloud." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-141696.

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The Event-driven computing paradigm, also known as trigger computing, is widely used in computer technology. Computer systems, such as database systems, introduce trigger mechanism to reduce repetitive human intervention. With the growing complexity of industrial use case requirements, independent and isolated triggers cannot fulfil the demands any more. Fortunately, an independent trigger can be triggered by the result produced by other triggered actions, and that enables the modelling of complex use cases, where the chains or graphs that consist of triggers are called workflows. Therefore, w
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Kiaian, Mousavy Sayyed Ali. "A learning based workflow scheduling approach on volatile cloud resources." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-282528.

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Workflows, originally from the business world, provide a systematic organization to an otherwise chaotic complex process. Therefore, they have become dominant and popular in scientific computation, where complex and broadscale data analysis and scientific automation are required. In recent years, demand for reliable algorithms for workflow optimization problems, mainly scheduling and resource provisioning have grown considerably. There are various algorithms and proposals to optimize these problems. However, most of these provisioning and algorithms do not account for reliability and robustnes
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Books on the topic "Cloud workflow"

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Liu, Xiao, Dong Yuan, Gaofeng Zhang, et al. The Design of Cloud Workflow Systems. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-1933-4.

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Dong, Yuan, Zhang Gaofeng, Li Wenhao, et al., eds. The Design of Cloud Workflow Systems. The Author(s), 2012.

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Youn, Chan-Hyun, Min Chen, and Patrizio Dazzi. Cloud Broker and Cloudlet for Workflow Scheduling. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5071-8.

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Liu, Xiao. Temporal QoS management in scientific cloud workflow systems. Elsevier, 2012.

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Higgins, John H. 10 steps to a digital practice in the cloud: New levels of CPA firm workflow efficiency. American Institute of Certified Public Accountants, 2012.

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Kousalya, G., P. Balakrishnan, and C. Pethuru Raj. Automated Workflow Scheduling in Self-Adaptive Clouds. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56982-6.

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Kaufman, Stephen. Pro Windows Server AppFabric. Apress, 2010.

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BPM 2010 (2010 Hoboken, N. J.). Business process management: 8th international conference, BPM 2010, Hoboken, NJ, USA, September 13-16, 2010 : proceedings. Springer, 2010.

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Khan, Rohit, and Rajiv Pandey. Introducing SAP Cloud Platform Workflow. Rheinwerk Publishing Inc., 2017.

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Liu, Xiao, Dong Yuan, and Gaofeng Zhang. The Design of Cloud Workflow Systems. Springer, 2011.

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Book chapters on the topic "Cloud workflow"

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Pandey, Suraj, Dileban Karunamoorthy, and Rajkumar Buyya. "Workflow Engine for Clouds." In Cloud Computing. John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9780470940105.ch12.

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Liu, Xiao, Dong Yuan, Gaofeng Zhang, et al. "Cloud Workflow System Architecture." In The Design of Cloud Workflow Systems. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1933-4_2.

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Liu, Xiao, Dong Yuan, Gaofeng Zhang, et al. "Cloud Workflow System Functionality." In The Design of Cloud Workflow Systems. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1933-4_3.

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Amini Salehi, Mohsen, and Xiangbo Li. "Demystifying Multimedia Streaming Workflow." In Multimedia Cloud Computing Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88451-2_2.

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Jiang, Yusheng, Jian Cao, and Yan Yao. "CIT-Workflow: An Elastic Cloud Workflow Service." In Communications in Computer and Information Science. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1019-4_9.

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Kanemitsu, Hidehiro, Masaki Hanada, and Hidenori Nakazato. "Multiple Workflow Scheduling with Offloading Tasks to Edge Cloud." In Cloud Computing – CLOUD 2019. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23502-4_4.

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Liu, Xiao, Dong Yuan, Gaofeng Zhang, et al. "Workflow Systems in the Cloud." In The Design of Cloud Workflow Systems. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1933-4_1.

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de Oliveira, Daniel C. M., Ji Liu, and Esther Pacitti. "Workflow Execution in a Multi-Site Cloud." In Data-Intensive Workflow Management. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-031-01872-5_4.

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Weder, Benjamin, Uwe Breitenbücher, Kálmán Képes, Frank Leymann, and Michael Zimmermann. "Deployable Self-contained Workflow Models." In Service-Oriented and Cloud Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44769-4_7.

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Liu, Xiao, Dong Yuan, Gaofeng Zhang, et al. "Cloud Workflow System Quality of Service." In The Design of Cloud Workflow Systems. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1933-4_4.

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Conference papers on the topic "Cloud workflow"

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Kilic, Ozgur O., Tianle Wang, Matteo Turilli, et al. "Workflow Mini-Apps: Portable, Scalable, Tunable & Faithful Representations of Scientific Workflows." In 2024 IEEE 24th International Symposium on Cluster, Cloud and Internet Computing (CCGrid). IEEE, 2024. http://dx.doi.org/10.1109/ccgrid59990.2024.00059.

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Lehmann, Fabian, Jonathan Bader, Friedrich Tschirpke, et al. "WOW: Workflow-Aware Data Movement and Task Scheduling for Dynamic Scientific Workflows." In 2025 IEEE 25th International Symposium on Cluster, Cloud and Internet Computing (CCGrid). IEEE, 2025. https://doi.org/10.1109/ccgrid64434.2025.00021.

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Pedratscher, Stefan, Thomas Fahringer, and Juan Aznar-Poveda. "Dynamic Workflow Scheduling in the Edge-Cloud Continuum: Optimizing Runtimes Under Budget Constraints." In 2024 IEEE 17th International Conference on Cloud Computing (CLOUD). IEEE, 2024. http://dx.doi.org/10.1109/cloud62652.2024.00018.

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Saeed, Amer, Gang Chen, Hui Ma, and Qiang Fu. "Genetic Algorithm with Repair Method for Deadline-Constrained IoT Workflow Scheduling in Fog-Cloud Computing." In 2024 IEEE 17th International Conference on Cloud Computing (CLOUD). IEEE, 2024. http://dx.doi.org/10.1109/cloud62652.2024.00035.

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Agarkhed, Jayashree, and R. Ashalatha. "Optimal workflow scheduling for scientific workflows in cloud computing." In 2016 International Conference on Emerging Technological Trends (ICETT). IEEE, 2016. http://dx.doi.org/10.1109/icett.2016.7873739.

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Kübler, Eric, and Mirjam Minor. "WFCF - A Workflow Cloud Framework." In 7th International Conference on Cloud Computing and Services Science. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006346305460551.

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Wu-Lee, Chi, and Gwan-Hwan Hwang. "Workflow Definition by Cloud Collaboration." In 9th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing. ICST, 2013. http://dx.doi.org/10.4108/icst.collaboratecom.2013.254050.

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Pan, Hailan, and Cuihong Wu. "Analysis of Cloud Workflow System." In 2016 International Conference on Modeling, Simulation and Optimization Technologies and Applications (MSOTA2016). Atlantis Press, 2016. http://dx.doi.org/10.2991/msota-16.2016.68.

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Huang, Hua, Yi-Lai Zhang, and Min Zhang. "A Survey of Cloud Workflow." In 2nd International Conference On Systems Engineering and Modeling. Atlantis Press, 2013. http://dx.doi.org/10.2991/icsem.2013.45.

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Hajjej, Fahima, Yousra Bendaly Hlaoui, and Leila Jemni Ben Ayed. "Cloud Adapted Workflow e-Assessment System: Cloud-AWAS." In 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC). IEEE, 2017. http://dx.doi.org/10.1109/compsac.2017.86.

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Reports on the topic "Cloud workflow"

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Sylvester, Charlene, and Michael Hartman. Cloud-based workflow to process regional topobathymetric lidar datasets for integrated USACE shoaling analyses. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49583.

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This Coastal and Hydraulics Engineering Technical Note (CHETN) details a methodology to process and format regional topobathymetric datasets for use in the US Army Corps of Engineers (USACE) Corps Shoaling Analysis Tool (CSAT).
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Stockley, Joshua, and Laura Lowes. Evaluation and Calibration of an OpenSees Layered Shell Element Model for Simulating the Earthquake Response of Flexure-Controlled Reinforced Concrete Walls. Pacific Earthquake Engineering Research Center, 2024. https://doi.org/10.55461/amqf4907.

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Reinforced concrete walls are used commonly to resist lateral loads; often, concrete walls have three-dimensional configurations to maximize stiffness and strength. Seismic design, evaluation, and retrofit of concrete wall structures often require nonlinear analysis to predict performance; assessment of earthquake risk for regions and for individual structures often require nonlinear analysis to predict damage, loss of functionality, and repair time and cost. This report presents the results of a research study in which high-performance cloud-computing resources were used to calibrate an OpenS
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Garzoglio, Gabriele. Enabling On-Demand Scientific Workflows on a Federated Cloud. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1333133.

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Garzoglio, Gabriele. Integration and Commissioning of a Prototype Federated Cloud for Scientific Workflows. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1333134.

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Garzoglio, Gabriele. Data Intensive Scientific Workflows on a Federated Cloud: CRADA Final Report. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1333843.

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Randles, Timothy. STNS01-22 BEE - FY21 P6-2: Execute BEE workflows on private cloud infrastructure. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1822695.

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Randles, Timothy. BEE-FY21 P6-2: Execute BEE workflows on private cloud infrastructure 2.3.6.01 - LANL ATDM ST / STNS01-22 Milestone Completion Documentation. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1822696.

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