基于类加权迁移深度Q网络策略的分层数据存储框架

HIERARCHICAL DATA STORAGE FRAMEWORK BASED ON CLASS WEIGHTED MIGRATION DEEP Q NETWORK STRATEGY

  • 摘要: 为了实现数据管理的高效性和适用能力,提出一种基于类加权迁移深度Q网络策略的分层数据存储框架。为了提升其在不同存储解决方案之间进行在线动态数据传输的能力,以及根据数据访问模式和可用性做出决策的能力,引入类加权迁移深度Q网络策略来解决分层存储系统中的数据迁移问题,同时忽略源异常值,有效激励了正知识的转移,提升域自适应的效果。最后设计了一个仿真软件和一个云框架进行试验测试,结果证明了提出方法的高效性和自适应能力。

     

    Abstract: In order to realize the efficiency and applicability of data management, a hierarchical data storage framework based on class weighted migration deep Q network strategy is proposed. The key of the proposed method was to improve its ability to conduct online dynamic data transmission between different storage solutions, and to make decisions based on data access mode and availability. Therefore, the class weighted migration deep Q network strategy was introduced to solve the data migration problem in the tiered storage system, while ignoring the source outliers, which effectively encouraged the transfer of positive knowledge and improved the effect of domain adaptation. A simulation software and a cloud framework were designed to test. The results show that the proposed method is efficient and adaptive.

     

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