Improved Hadoop-based cloud for complex model simulation optimization: Calibration of SWAT as an example Article Swipe
Jinfeng Ma
,
Kaifeng Rao
,
Ruonan Li
,
Yanzheng Yang
,
Weifeng Li
,
Hua Zheng
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.envsoft.2022.105330
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.envsoft.2022.105330
A simulation optimization framework requires a substantial number of model simulations, which are computationally intensive and may be impractical when the model simulations are extremely time-consuming. This paper presents an improved Hadoop-based cloud framework to alleviate the computational burden of optimization. The framework parallelizes conventional sequential-model-based optimization techniques by concurrently orchestrating multiple model computations within Hadoop MapReduce. It guarantees the reliability of simulation optimization tasks by handling node failures without affecting the ongoing simulation. A case study, using Bayesian optimization to calibrate a SWAT model, achieved a speedup of nearly 55–58 when using 100 cores, demonstrating the efficiency of parallelizing the Bayesian optimization algorithm on the Hadoop-based cloud. Experiments in which computing nodes were dynamically increased or decreased demonstrated that the framework can automatically rebalance the workload across the remaining nodes. The framework is readily adaptable to other complex model applications that perform sequential-model-based optimizations or large-scale simulations.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.envsoft.2022.105330
- OA Status
- hybrid
- Cited By
- 11
- References
- 60
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4205137743
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- OpenAlex ID
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https://openalex.org/W4205137743Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.envsoft.2022.105330Digital Object Identifier
- Title
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Improved Hadoop-based cloud for complex model simulation optimization: Calibration of SWAT as an exampleWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-01-17Full publication date if available
- Authors
-
Jinfeng Ma, Kaifeng Rao, Ruonan Li, Yanzheng Yang, Weifeng Li, Hua ZhengList of authors in order
- Landing page
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https://doi.org/10.1016/j.envsoft.2022.105330Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
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https://doi.org/10.1016/j.envsoft.2022.105330Direct OA link when available
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Computer science, Cloud computing, Speedup, Workload, Bayesian optimization, Reliability (semiconductor), Computation, Node (physics), Distributed computing, Parallel computing, Algorithm, Artificial intelligence, Engineering, Operating system, Physics, Quantum mechanics, Structural engineering, Power (physics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
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2025: 2, 2024: 4, 2023: 4, 2022: 1Per-year citation counts (last 5 years)
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60Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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