Distributed machine learning load balancing strategy in cloud computing services

doi:10.1007/s11276-019-02042-2

	Distributed machine learning load balancing strategy in cloud computing services
	Li, Mingwei 1,2; Zhang, Jilin 1,2,3; Wan, Jian 1,2,4; Ren, Yongjian 1,2; Zhou, Li 1,2; Wu, Baofu 1,2; Yang, Rui 1,2; Wang, Jue 5
	2020-11-01
发表期刊	WIRELESS NETWORKS
ISSN	1022-0038
卷号	26 期号:8 页码:5517-5533
摘要	Mobile service computing is a new cloud computing model that provides various cloud services for mobile intelligent terminal users through mobile internet access. The quality of service is an essential problem faced by mobile service computing. In this paper, we demonstrate a series of research studies on how to accelerate the training of a distributed machine learning (ML) model based on cloud service. Distributed ML has become the mainstream way of today's ML models training. In traditional distributed ML based on bulk synchronous parallel, the temporary slowdown of any node in the cluster will delay the calculation of other nodes because of the frequent occurrence of synchronous barriers, resulting in overall performance degradation. Our paper proposes a load balancing strategy named adaptive fast reassignment (AdaptFR). Based on this, we built a distributed parallel computing model called adaptive-dynamic synchronous parallel (A-DSP). A-DSP uses a more relaxed synchronization model to reduce the performance consumption caused by synchronous operations while ensuring the consistency of the model. At the same time, A-DSP also implements the AdaptFR load balancing strategy, which addresses the straggler problem caused by the performance difference between nodes under the premise of ensuring the accuracy of the model. The experiments show that A-DSP can effectively improve the training speed while ensuring the accuracy of the model in the distributed ML model training.
关键词	Mobile service computing Cloud service Distributed machine learning Load balancing Adaptive fast reassignment
DOI	10.1007/s11276-019-02042-2
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000574649300003
出版者	SPRINGER
引用统计	被引频次：8[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/15616
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Wan, Jian
作者单位	1.Hangzhou Dianzi Univ, Sch Comp Sci & Technol, Hangzhou 310018, Peoples R China 2.Minist Educ, Key Lab Complex Syst Modeling & Simulat, Hangzhou 310018, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 4.Zhejiang Univ Sci & Technol, Sch Informat & Elect Engn, Hangzhou, Zhejiang, Peoples R China 5.Chinese Acad Sci, Supercomp Ctr, Comp Network Informat Ctr, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Li, Mingwei,Zhang, Jilin,Wan, Jian,et al. Distributed machine learning load balancing strategy in cloud computing services[J]. WIRELESS NETWORKS,2020,26(8):5517-5533.
APA	Li, Mingwei.,Zhang, Jilin.,Wan, Jian.,Ren, Yongjian.,Zhou, Li.,...&Wang, Jue.(2020).Distributed machine learning load balancing strategy in cloud computing services.WIRELESS NETWORKS,26(8),5517-5533.
MLA	Li, Mingwei,et al."Distributed machine learning load balancing strategy in cloud computing services".WIRELESS NETWORKS 26.8(2020):5517-5533.