基于3-gram模型和数据挖掘技术的元数据预取 

在大规模的文件存储系统中,针对大多数算法的设计没有考虑到元数据访问的特征与元数据本身较小的特点,提出了一种利用存储系统中的元数据操作日志文件,运用3元(3-gram)预测模型和数据挖掘的方法对用户未来可能要操作的元数据进行组预取。实验证明,对于从日志文件中提取出的文件元数据访问序列,新预取模式的缓存(Cache)命中率与基于权重图的预取算法(NEXUS)相比平均提高了3.9%,与最近最少使用算法(least recently used,LRU)比较平均提高了16%。

Metadata prefetching based on 3-gram model and datamining technology

An efficient and accurate metadata-oriented prefetching scheme is critical for achieving the best metadata service performance in large distributed storage systems.Most previously developed algorithms,however,do not consider the characteristics of metadata,such as small size.In light of this issue,we presented a 3-gram based model and datamining technique to use the traces of metadata accessing to prefetch groups of users' future metadata operations.By using large trace-driven simulations in which our new prefetching scheme was adopted,it is shown that the hit rate for metadata access extracted from the traces can be increased by up to 3.9% and 16 % compared with NEXUS and least recently used(LRU),respectively.