基于APMSSGA-LSTM的容器云资源预测

doi:10.11959/j.issn.2096-0271.2019050

Big Data Research ›› 2019, Vol. 5 ›› Issue (6): 62-72.doi: 10.11959/j.issn.2096-0271.2019050

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Container cloud resource prediction based on APMSSGA-LSTM

Xiaolan XIE^1,²,Zhengzheng ZHANG¹(),Qiangqing ZHENG¹,Chaoquan CHEN¹

¹ College of Information Science and Engineering,Guilin University of Technology,Guilin 541004,China
² Guangxi Key Laboratory of Embedded Technology and Intelligent System,Guilin 541004,China

Online:2019-11-15 Published:2020-01-10
Supported by:
The National Natural Science Foundation of China(6176203);Science and Technology Major Project of Guangxi(2018AA32003);The Key Research and Development Program of Guangxi(AB17195029);The Key Research and Development Program of Guangxi(AB18126006);Innovation Project of Guangxi Graduate Education(YCSW2017156);Innovation Project of Guangxi Graduate Education(YCSW2018157);Subsidies for the Project of Promoting the Ability of Young and Middle-Aged Scientific Research in Universities and Colleges of Guangxi(KY2016YB184)

Abstract

Abstract:

With the development and application of container cloud,the demand for high concurrency,high availability,high flexibility,and high flexibility of resources is becoming more and more intense.After investigating the current research status of container cloud resource prediction,a container cloud resource prediction model which using an adaptive probability multiselection strategy genetic algorithm (APMSSGA) to optimize the long short term memory network (LSTM) was proposed.The experimental results show that compared with the simple genetic algorithm (SGA),APMSSGA is more efficient in LSTM parameter optimal solution combination search,and the APMSSGA-LSTM model has higher prediction accuracy.

Key words: container cloud, resource prediction, long short term memory network, genetic algorithm

CLC Number:

TP393

Xiaolan XIE, Zhengzheng ZHANG, Qiangqing ZHENG, Chaoquan CHEN. Container cloud resource prediction based on APMSSGA-LSTM[J]. Big Data Research, 2019, 5(6): 62-72.

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References 18

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参数设置	SGA	APMSSGA
迭代次数/次	100	100
种群规模/个	80	80
交叉率	0.8	0.8
变异率	0.01	0.01
染色体长度	6+6+8	6+6+8

Container cloud resource prediction based on APMSSGA-LSTM

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