摘要
由于多媒体技术不断发展,学习资源呈爆炸式增长,给资源存储提出了新的挑战。Hadoop平台对小文件的存储和访问存在内存消耗高、存储空间浪费等问题。针对这种情况,分析学习资源的特点,设计两级Hadoop模式,优化学习资源存储策略,提出基于多层次Hadoop的学习资源云存储模型。并在实验室环境下搭建存储模型,对多类型的学习资源文件进行存储测试分析。分析结果表明,模型在存储空间、内存消耗和存储效率上较传统Hadoop模型有着明显的改善,适合海量学习资源的存储需求。
Because of the continuous development of multimedia technology, learning resource shows explosive growth, which raises new challenges to resources storage. There are some problems in Hadoop platform in regard to storing and accessing small files, such as high memory consumption, storage space waste and so on. In view of this, we analysed the characteristics of learning resources, designed the two-step Hadoop mode and optimised the storage policy of learning resources. Besides,we put forward the multilevel Hadoop-based cloud storage model of learning resource. Furthermore, we built the storage model in laboratory environment, and carried out storage test and analysis on multi-type learning resource files. It is demonstrated by analysis result that compared with traditional Hadoop model, the proposed model has a significant improvement in storage space, memory consumption and storage efficiency. It is suitable for the storage needs of mass learning resources.
出处
《计算机应用与软件》
CSCD
2016年第12期23-25,44,共4页
Computer Applications and Software
基金
辽宁省自然科学基金项目(20120297)
关键词
海量学习资源
内存开销
访问效率
小文件
云存储
Mass learning resources
Memory overhead
Access efficiency
Small file
Cloud storage
