Data-intensive service deployment based on edge computing

Yongmei GAO; Guanjie CHENG

doi:10.11959/j.issn.1000-0801.2019170

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Data-intensive service deployment based on edge computing

research and development | 更新时间：2024-06-05

- Data-intensive service deployment based on edge computing
- Telecommunications Science Vol. 35, Issue 7, Pages: 87-99(2019)
- 作者机构：
  
  1. 杭州职业技术学院信息工程学院，浙江杭州 310018
  2. 浙江大学计算机科学与技术学院，浙江杭州 310027
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2019170
  CLC： TP302
- Published Online：2019-07，
  
  Published：20 July 2019
- 稿件说明：
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Yongmei GAO, Guanjie CHENG. Data-intensive service deployment based on edge computing[J]. Telecommunications science, 2019, 35(7): 87-99.
DOI：

Yongmei GAO, Guanjie CHENG. Data-intensive service deployment based on edge computing[J]. Telecommunications science, 2019, 35(7): 87-99. DOI： 10.11959/j.issn.1000-0801.2019170.

摘要

日益增长的数据量对数据处理的要求越来越高，于是出现了数据密集型服务。在解决复杂问题时，多个数据密集型服务通常会形成一个服务组合。由于服务组件之间存在大量的数据传输，巨大的传输时延会对系统的整体性能造成影响。在边缘计算环境中，基于否定选择算法，为降低服务组合中的数据传输时间提出了一种优化部署策略。首先，给出了此类数据密集型服务组件部署问题的定义，并为该部署问题构建优化模型；然后，设计了一种否定选择算法来获取最佳的部署方案；为了评估该算法的适用性和收敛性，使用遗传算法和模拟退火算法与其对比，结果显示，提出的算法在这种数据密集型服务组件的部署问题中表现得更为出色。

Abstract

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.

关键词

Keywords

references

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