基于边缘计算的数据密集型服务部署

doi:10.11959/j.issn.1000-0801.2019170

摘要/Abstract

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。

关键词: 服务组合, 服务部署, 边缘计算, 云计算

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.

Key words: service composition, service deployment, edge computing, cloud computing

中图分类号:

TP302

高永梅,程冠杰. 基于边缘计算的数据密集型服务部署[J]. 电信科学, 2019, 35(7): 87-99.

Yongmei GAO,Guanjie CHENG. Data-intensive service deployment based on edge computing[J]. Telecommunications Science, 2019, 35(7): 87-99.

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参考文献 12

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参数	α	β	ρ
实验1	0.1，0.3，0.5	1	1
实验2	0.1	1，5，10	1
实验3	0.1	1	0.5，0.9，1

算法参数	设定值
种群规模	50
演化代数	1 000
交叉概率	0.8
变异概率	0.05

算法参数	设定值
初始温度	100
迭代次数	1 000
温度下界	1.00×10^-8
温度下降率	0.98

迭代次数	fitness
40	60.942 02
41	65.152 41
42	76.162 43
43	76.162 43
44	76.162 43
45	87.777 8
46	87.777 8
47	87.777 8
48	87.777 8

迭代次数	fitness
840	43.649 03
841	43.649 03
842	43.649 03
843	45.703 84
844	45.703 84
845	45.703 84
846	45.703 84
847	45.703 84
848	45.703 84