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

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• 作者：Kai Cui^a ; ^bAuthor Vitae ; Jie Wang^a ; ^bAuthor Vitae ; Houbing Song^cAuthor Vitae ; Chi Lin^a ; ^b ; ^{c.lin@dlut.edu.cn}Author Vitae ; Kuanjiu Zhou^a ; ^bAuthor Vitae ; Mingchu Li^a ; ^bAuthor Vitae
• 关键词：00-01 ; 99-00
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：12 January 2017
• 年：2017
• 卷：220
• 期：Complete
• 页码：60-66
• 全文大小：1523 K
• 卷排序：220

文摘

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%.