Cloud storage is a hot topic in current research. Different from previous work, we emphasize the importance of metadata cache in the study of cloud storage. Because the efficiency of distributed file system has much effect on cloud storage The metadata operation accounts for more than 50% of the total file operation. So the strategy of efficient metadata management is important. There are three parts in this paper. We start with a brief introduction of cloud storage. Then a metadata caching algorithm for cloud storage is proposed. An additional discussion of its performance is also provided. The prototype which incorporates the proposed metadata caching algorithm is realized on Luster to evaluate its performance. Comparing experimental results from this study conclude that the metadata caching subsystem can improve the performance of cloud storage.
The efficiency of metadata indexing is important to the performance of distributed file system. Time and space spending of current metadata management algorithms are unstable. In this paper, we use B-tree to index the metadata of distributed file system. Lustre is an open source distributed file system in which Hash function is used to manage the metadata. We implement the prototype of metadata indexing sub-system on Lustre and use Iozone to test the I/O performance of Lustre with and without the metadata indexing sub-system respectively. The simulation results show that Lustre with the metadata indexing sub-system has higher adaptability than Lustre with Hash-based metadata management algorithm.
Metadata query plays an important role in mass storage system. Efficient indexing algorithm can reduce the time and space which greatly determine the efficiency of mass storage system. Typically, temporal and spatial consuming is immense and volatile in the existing metadata management algorithms. In this paper, a novel metadata indexing algorithm is presented. Metadata query algorithm is based on two-level indexing strategy. The metadata is classified into two categories, that are active metadata and non-active metadata. The Bloom Filter is used to generate binary string for active metadata, and the B-tree is used to establish index of each active partition. While, the suitable hash function is selected for each non-active metadata partition. The results show that the multi-level metadata indexing algorithm can reduce the temporal and spatial costs of metadata query.
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