融合Stacking框架的BiGRU-LGB云负载预测模型

doi:10.19665/j.issn1001-2400.2023.03.008

Abstract

Abstract:

With the rapid development of cloud computing technology,more and more users deploy applications on cloud platforms.The scheduling of cluster resources within the platform can improve the actual utilization of the cloud platform data center,and efficient cloud platform load prediction is a key technology for solving the cluster resource scheduling problem,so this paper establishes a cloud load prediction model that incorporates the Stacking framework,multilayer bidirectional gated recurrent unit (BiGRU) network and light gradient boosting machine (LightGBM) algorithm.The structure of the model consists mainly of two kinds of learners:one is the primary learner,which uses a temporal encoding layer to process the original load sequence and reduces the training time and the number of hidden layers by taking advantage of the BiGRU network with fewer parameters and complete information learning,to learn the temporal dimension information in the load sequence with the original load sequence processed by feature engineering used to efficiently train the LightGBM algorithm to learn the load feature dimension information in the sequence.Then comes the other learner,which integrates the load information in temporal and feature dimensions using the GRU network to complete the training of the whole load prediction model.The prediction accuracy of the overall load prediction model is improved by joint learning of the two layers of learners.Experiments are conducted on Huawei Cloud Cluster dataset with the results showing that the prediction accuracy of the BiGRU-LGB model incorporating the Stacking is improved by about 13% and the training time overhead is reduced to some extent compared with the traditional single prediction models,such as BiGRU and LightGBM,and the existing combined prediction model GRU-LSTM.

Key words: cloud platform, load prediction, bidirectional gated cyclic unit, LightGBM, stacking integration framework

CLC Number:

TP183

LIU Hui,DONG Xiyao,YANG Zhihan. BiGRU-LGB cloud load prediction model incorporating stacking framework[J].Journal of Xidian University, 2023, 50(3): 83-94.

Figures/Tables 10

References 30

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字段	类型	说明
QUEUE_ID	int	队列标识,每个ID代表一个惟一的队列
CU	int	队列规格,不同规格的资源大小不一样。1CU为1核4 GB
STATUS	string	队列状态,当前队列的状态是否可用
QUEUE_TYPE	string	队列类型,不同类型适用于不同的任务,常见的有通用队列(general)和SQL队列
PLATFORM	string	队列平台,创建队列的机器平台
CPU_USAGE	int	CPU使用率,集群中各机器节点的CPU平均使用率
MEM_USAGE	int	内存使用率,集群中各机器节点的内存平均使用率
LAUNCHING_JOB_NUMS	int	提交中的作业数,即正在等待执行的作业
RUNNING_JOB_NUMS	int	运行中的作业数
SUCCEED_JOB_NUMS	int	已完成的作业数
CANCELLED_JOB_NUMS	int	已取消的作业数
FAILED_JOB_NUMS	int	已失败的作业数
DOTTING_TIME	bignt	采集时间,每5 min进行一次采集
RESOURCE_TYPE	string	资源类型,创建队列的机器类型
DISK_USAGE	int	磁盘使用

分组	KFOLD折数	BiGRU网络隐藏层数	数据映射维度	随机失活率	模型级联层数	M_MSE	M_MAE
Base	5	2	512	0.2	1	0.463	0.384
A	0					0.487	0.396
	10					0.503	0.402
B		1		0.4		0.472	0.391
		4		0.5		0.481	0.394
C			128			0.497	0.398
			1 024			0.523	0.412
D		1		0.0		0.475	0.392
		3		0.1		0.507	0.403
E					2	0.537	0.422
					3	0.586	0.437

预测模型	M_MSE	M_MAE	训练时间/s
BiGRU-LGB	0.463	0.384	53.526
GRU	1.108	0.832	23.241
LightGBM	0.615	0.505	31.241
BiGRU	0.702	0.596	46.874
Transformer	0.515	0.408	58.952
GRU-LSTM	0.583	0.436	65.427

BiGRU-LGB cloud load prediction model incorporating stacking framework

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References 30

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