基于多属性度量的数据分级访问模型研究

随着云计算的迅速发展,云存储已成为企业关键信息服务的重要途径。但是,受限于存储资源性能以及大数据等影响,用户往往需要忍受较长的访问延时。为了缓解这种情况,人们提出了智能数据管理技术,用于有效管理大量数据以及降低用户的访问延迟,提高云计算的服务质量。提出一种基于多属性分析的存储端数据分级访问模型。模型通过对被管理数据对象的动态、静态属性进行统计分析来提取属性中的关键信息,并依此进行数据管理决策,将冷/热数据迁移至对应层级,以便能够在合理规划存储资源的同时提高存储系统的访问性能。性能测试实验的结果表明,该模型具有较好的整体性能。

Hierarchical Storage Access Model Based on Multi-Attributes Measurement

With the rapid development of cloud computing, cloud storage has become an important way of business-critical information services. However, limited by storage resource performance and impact data, users often need to endure a long delay of access. In order to alleviate the situation, intelligent data management technologies have been proposed, to effectively manage large amounts of data, as well as reduce access latency, improving the quality of cloud computing services. Proposing an Hierarchical Storage Access Model based on Multi-Attributes Measurement. Model managed through the statistical analysis of the static and dynamic properties of the data object, extracting key information from the properties, data management and in accordance with this decision, cold/hot data migration to the corresponding levels so that you can reasonable while planning your storage resources to improve storage system performance. Performance test results of the experiment showed that the model has good overall performance.