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Intelligent Cloud Workflow Management and Scheduling Method for Big Data Applications
Hui Wang
Weifang University
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at Journal of Cloud Computing.
With the application and comprehensive development of big data, the need for effective research on cloud workflow management and scheduling is becoming more and more urgent. However, there are currently suitable methods for effective analysis. In order to find out how to effectively manage and schedule smart cloud workflows, the article studies big data from different aspects and draws the following conclusions: Compared with the original JStorm system, the average response time is shortened by up to 58.26%, and the average is shortened. 23.18%; CPU resource utilization increased by 17.96%, an average increase of 11.39%; memory utilization increased by 88.7%, an average increase of 71.16%. In optimizing the dynamic combination of web services, the overall performance of MOACO algorithm and CCA algorithm is better than GA algorithm, and the average performance of MOACO algorithm is better than CCA algorithm. The paper also proposes a cloud workflow scheduling strategy based on intelligent algorithms and adjusting the perceived cloud service resource combination strategy to realize two-layer scheduling of cloud workflow tasks. We have studied three representative intelligent algorithms (ACO algorithm, PSO algorithm and GA algorithm) and designed and improved them for scheduling optimization. It can be clearly seen that in the same scenario, in different test cases the optimal values of the different algorithms vary greatly. However, the optimal solution curve is substantially consistent with the trend of the mean curve.
Keywords
Big Data, Cloud Workflow, Cloud Service Resource Combination, Scheduling Optimization
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This preprint is under consideration at Journal of Cloud Computing. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Intelligent Cloud Workflow Management and Scheduling Method for Big Data Applications
Hui Wang
Weifang University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
Version 1
Posted 18 Dec, 2019
Published
On 21 Jul, 2020
No community comments so far
Submission checks complete
On 10 Dec, 2019
Editor invited
On 10 Dec, 2019
Editor assigned
On 10 Dec, 2019
First submitted
On 09 Dec, 2019
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at Journal of Cloud Computing.
With the application and comprehensive development of big data, the need for effective research on cloud workflow management and scheduling is becoming more and more urgent. However, there are currently suitable methods for effective analysis. In order to find out how to effectively manage and schedule smart cloud workflows, the article studies big data from different aspects and draws the following conclusions: Compared with the original JStorm system, the average response time is shortened by up to 58.26%, and the average is shortened. 23.18%; CPU resource utilization increased by 17.96%, an average increase of 11.39%; memory utilization increased by 88.7%, an average increase of 71.16%. In optimizing the dynamic combination of web services, the overall performance of MOACO algorithm and CCA algorithm is better than GA algorithm, and the average performance of MOACO algorithm is better than CCA algorithm. The paper also proposes a cloud workflow scheduling strategy based on intelligent algorithms and adjusting the perceived cloud service resource combination strategy to realize two-layer scheduling of cloud workflow tasks. We have studied three representative intelligent algorithms (ACO algorithm, PSO algorithm and GA algorithm) and designed and improved them for scheduling optimization. It can be clearly seen that in the same scenario, in different test cases the optimal values of the different algorithms vary greatly. However, the optimal solution curve is substantially consistent with the trend of the mean curve.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.