Data-intensive service deployment based on edge computing
The demand is getting higher and higher for data processing due to big data volume, thus, data-intensive service shave emerged. When solving complex problems, multiple data-intensive services are often united as a service portfolio. Due to the huge data transmission between service components, a gre...
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| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
| Published: |
Beijing Xintong Media Co., Ltd
2019-07-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2019170/ |
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| Summary: | The demand is getting higher and higher for data processing due to big data volume, thus, data-intensive service shave emerged. When solving complex problems, multiple data-intensive services are often united as a service portfolio. Due to the huge data transmission between service components, a great transmission delay will affect the overall performance of the system. In the edge computing environment, an optimized deployment strategy based on the negative selection algorithm was proposed to reduce the data transmission time in the service composition. Firstly, the definition of such a data-intensive service component deployment problem was given, and the deployment problem was modeled as an optimization model; then, a negative selection algorithm was designed to obtain the best deployment solution. In order to evaluate the applicability and convergence of the algorithm, it was compared with genetic algorithm and simulated annealing algorithm. The results show that proposed algorithm outperforms other schemes in this data-intensive service deployment problem. |
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| ISSN: | 1000-0801 |