A Scalable and Self-Adjust Multi-Tenant Data Storage Strategy Under Different SLAs
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摘要: 多租户是云应用的主要特征，在共享数据存储模式下，如何根据不同租户对数据请求的性能需求，实现多节点的数据动态伸缩存储是云数据管理的关键.提出一种可伸缩的多租户数据自适应存储方法，主要包括一个分段多维性能边界模型，用于判定数据节点能否满足不同租户的性能需求；一个基于贪婪的数据存储布局调整策略生成算法，制定对过载节点数据的移动和对未过载节点数据合并的策略.通过实验系统分析，该方法能够准确预测和判断系统是否过载，通过控制较少的数据移动，减少对系统性能的影响，使得云中共享数据节点能够满足不同租户的性能需求.Abstract: Multi-tenancy is one of the key characteristics of cloud application. In shared data storage model, how to achieve dynamically scalable data storage of multiple nodes according to different tenants' performance requirements is the key of cloud data management. It faces many challenges, such as how to forecast performance requirements of different tenants under shared data storage, how to place and control data layout in response to bursty in workload, i.e. data items automatic partitioned, coalesced or copied among the nodes of the system according to variable tenants' performance requirements. In this paper, we propose a scalable and self-adjust multi-tenant data storage strategy to address these challenges. It mainly consists of piecewise multiple performance boundary model，which is used to predict whether the data node can meet the performance requirements of different tenants, and data storage layout adjustment strategy based on greedy, which is used to make decisions about data movement on overloaded node and data coalescence on underloaded node. Through the analysis of experiment system, this method can accurately predict whether the system is overloaded or not, reduce the influence on system performance by less data movement, and make the shared cloud data node satisfy performance requirements of different tenants.
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Keywords:
- multi-tenant /
- scalable /
- self-adjust /
- performance prediction /
- data storage
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期刊类型引用(9)
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