欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
为了实现在线海量数据的高效存储与访问,在内存云分级存储架构下,提出一种基于数据重要性的迁移模型（MMDS）。首先,通过数据本身的大小、时间重要性、用户访问总量等因素对数据本身的重要性进行计算;其次,采用推荐系统中相似用户和PageRank算法中的重要性排名思想对数据的潜在价值进行评估,数据重要性和潜在价值共同决定了数据的重要程度;然后基于数据的重要性,设计了数据迁移机制。实验结果表明：该模型能够识别出数据的重要程度并分级放置数据,相比最近最少使用（LRU）、最近最不常用（LFU）、基于价值评估的数据迁移（MSDV）等算法,提高了存储系统的数据访问命中率。该模型能够缓解部分存储压力,数据访问性能也有了一定的提高。
英文摘要：
In order to achieve the efficient storage and access to the huge amounts of data online, under the hierarchical storage architecture of memory cloud, a model of Migration Model based on Data Significance（ MMDS） was proposed. Firstly,the importance of data itself was calculated based on factors of the size of the data itself, the importance of time, the total amount of user access, and so on. Secondly, the potential value of the data was evaluated by adopting users＇ similarity and the importance ranking of the PageRank algorithm in the recommendation system. The importance of the data was determined by the importance of data itself and its potential value together. Then, data migration mechanism was designed based on the importance of data, The experimental results show that, the proposed model can identify the importance of the data and place the data in a hierarchical way and improved the data access hit rate from the storage system compared with the algorithms of Least Recently Used（ LRU）, Least Frequently Used（ LFU）, Migration Strategy based on Data Value（ MSDV）. The proposed model can alleviate the part pressure of storage and has improved the data access performance.
同期刊论文项目
同项目期刊论文
期刊信息
位置：