Effective hybrid load scheduling of online and offline clusters for e-health service

被引：2

作者：

Cui, Kai ^{[1
,2
]}

Wang, Jie ^{[1
,2
]}

Song, Houbing ^{[3
,4
]}

Lin, Chi ^{[1
,2
]}

Zhou, Kuanjiu ^{[1
,2
]}

Li, Mingchu ^{[1
,2
]}

机构：

[1] Dalian Univ Technol, Sch Software, Dalian, Peoples R China

[2] Key Lab Ubiquitous Network & Serv Software Liaoni, Dalian, Peoples R China

[3] West Virginia Univ, Dept Elect & Comp Engn, Morgantown, WV USA

[4] West Virginia Univ, West Virginia Ctr Excellence Cyber Phys Syst WVCE, Morgantown, WV USA

来源：

NEUROCOMPUTING | 2017年 / 220卷

关键词：

Hybrid load; Online e-health service; Compression index; Storm platform; TAIL;

D O I：

10.1016/j.neucom.2016.03.103

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Hybrid load in e-health services is composed of online e-health service applications and offline jobs. Previous methods overlooked the impact of system performance for the fine-grained service components. In this paper, a hybrid load scheduling scheme is proposed in which scheduling is performed not only at the level of the component, but also within components. To improve both execution efficiency and searching accuracy, the proposed algorithm searches the compressing method of the Lucene index and then filters that index. Simulations are conducted on a Storm platform to evaluate the performance of the proposed scheme. Simulation results demonstrate that the proposed scheme can increase the response speed by 67.79% with an accuracy of 95.94%, and the response speed decreases by 11.6-53.2%. (C) 2016 Elsevier B.V. All rights reserved.

引用

页码：60 / 66

页数：7

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