期刊文献+

面向容器的云平台数据重分布策略研究 被引量:1

An efficient data distribution strategy for container in the cloud
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摘要 随着Docker等的问世,基于容器的操作系统级虚拟化技术受到云计算厂商的广泛关注。针对云平台上不同应用领域的数据库容器(面向事务型任务的数据库容器与面向分析型任务的数据库容器)在运行时对宿主机资源需求的差异,提出一种面向容器的数据重分布策略,用于优化容器中数据库服务的性能。实验结果表明,该策略达到了预期效果,可以有效提升容器中数据库服务的性能。 With the advent of Docker,container technology has gained much attention by cloud computing vendors. In this paper,we propose a new strategy of data redistribution for database containers,on the different application domains( transaction or analysis) of cloud platform,call for different resource of the host at runtime. Experimental results show that the proposed strategy can improve the performance of database services in the container efficiently.
出处 《微型机与应用》 2016年第5期26-29,共4页 Microcomputer & Its Applications
基金 国家自然科学基金(61462012 61562010) 贵州省高技术发展专项([2013]2069) 贵州省科技计划项目([2013]2099 GY[2014]3018 JZ20142001-05) 贵州大学引进人才项目(700246003301) 贵州大学研究生创新基金(研理工2015016)
关键词 云计算 虚拟化 数据库 容器 数据重分布 cloud computing virtualization database container data redistribution
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参考文献6

  • 1Wei Hao,YEN I L,THURAISINGHA M B.Dynamic service and data migration in the clouds[C].IEEE COMPSAC,2009:134-136.
  • 2石杰.云计算环境下的数据挖掘应用[J].微型机与应用,2015,34(5):13-15. 被引量:9
  • 3吴军,张轶君,白光伟.Xen下虚拟机动态迁移优化策略的研究[J].电子技术应用,2015,41(11):128-131. 被引量:6
  • 4杨保华,戴王剑,曹亚伦.Docker技术入门与实践[M].北京:机械工业出版社,2015.
  • 5QUAMAR A,ASHWIN KUMAR K,DESHPANDE A.SWORD:scalable workload-aware data placement for transactional workloads[M].EDBT,2013.
  • 6Li Yinan,PATEL J M.Wide Table:an accelerator for analytical data processing[C].Proceedings of the VLDB Endowment,2014:907-909.

二级参考文献15

  • 1WEISS A.Computing in clouds[J].ACM Networker,2007,11(4):18-25.
  • 2BUYYA R,VENUGOPAL S.Market-oriented cloud computing:vision,hype,and reality for delivering IT services as computing utilities[C].Proceedings of the 2008 10th IEEE International Conference on High Performance Computing and Communications,2008:5-13.
  • 3BOHM C,BERCHTOLD S,MICHEL U.Multidimensional index structures in relational databases[C].in 1stInternational Conference on Data Warehousing and Knowledge Discovery,1999:51-70.
  • 4DEAN J,GHEMAWAT S,USENIX.Map Reduce:simplified data processing on large clusters[C].6th Symposium on Operating Systems Design and Implementation,2004:137-149.
  • 5Han J,Pei J,Yin Y.Mining frequent patterns without candidate generation[C].Proc.of ACM Int.Conf.on Management of data(SIGMOD),2000:1-12.
  • 6KAWUU W LIN,LUO Y C.Efficient strategies for manytask frequent pattern mining in cloud computing environments[C].Systems Man and Cybernetics(SMC),IEEE International Conference,2010(10):620-623.
  • 7NAIR T R G,MADHURI K L.Data mining using hierarchical virtual k-means approach integrating data fragments in cloud computing environment[C].Cloud Computing and Intelligence Systems(CCIS),IEEE International Conference,2011(1):230-234.
  • 8CLARK C , FRASER K,HAND S,et al.Live migration ofvirtual machines [ C ]. Proceedings of the 2nd Symposium onNetworked Systems Design and Implementation , 2005 : 273-.
  • 9NELSON M,LIM B , HUTCHINES G.Fast transparentmigration for virtual machines [ C ]. Proceedings of theUSENIX Annual Technical Conference , 2005 : 391-394.
  • 10Hai Jin , Li Deng, Song Wu , et al.MECOM ; Live migration ofvirtual machines by adaptively compressing memory pages [J].Future Generation Computer Systems , 2014 ; 23-35.

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