HBA DISTRIBUTED METADATA MANAGEMENT FOR LARGE CLUSTER-BASED STORAGE SYSTEMS PDF

Resources and Help HBA: Distributed Metadata Management for Large Cluster-Based Storage Systems Abstract: An efficient and distributed scheme for file mapping or file lookup is critical in decentralizing metadata management within a group of metadata servers. This paper presents a novel technique called Hierarchical Bloom Filter Arrays HBA to map filenames to the metadata servers holding their metadata. Two levels of probabilistic arrays, namely, the Bloom filter arrays with different levels of accuracies, are used on each metadata server. One array, with lower accuracy and representing the distribution of the entire metadata, trades accuracy for significantly reduced memory overhead, whereas the other array, with higher accuracy, caches partial distribution information and exploits the temporal locality of file access patterns.

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The Bloom channel exhibits with various levels of exactnesses are utilized on every metadata server. The first with low exactness and used to catch the goal metadata server data of every now and again got to documents. The other exhibit is utilized to keep up the goal metadata data of all documents. Recreation comes about demonstrate our HBA configuration to be exceptionally viable and effective in enhancing the execution and versatility of record frameworks in groups with 1, to 10, hubs or superclusters and with the measure of information in the petabyte scale or higher.

HBA is decreasing metadata task by utilizing the single metadata engineering rather than 16 metadata server. Following methodologies are utilized as a part of the Existing framework.

Table-Based Mapping: It neglects to adjust the heap. Hashing-Based Mapping: It has moderate registry tasks, such as posting the registry substance And renaming registries. Static Tree Partitioning: Cannot adjust the heap and has a medium query time. Dynamic Tree Partitioning: Small memory overhead, causes a vast Relocation overhead. There are two clusters utilized here.

To start with the cluster is utilized to lessen memory overhead since it catches just the goal metadata server data habitually got to records to keep high administration productivity. Furthermore, the second one is utilized to keep up the goal metadata data of all records. Both the exhibits are for the most part utilized for quick neighborhood query.

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Abstract Abstract—An efficient and distributed scheme for file mapping or file lookup is critical in decentralizing metadata management within a group of metadata servers. This paper presents a novel technique called Hierarchical Bloom Filter Arrays HBA to map filenames to the metadata servers holding their metadata. Two levels of probabilistic arrays, namely, the Bloom filter arrays with different levels of accuracies, are used on each metadata server. One array, with lower accuracy and representing the distribution of the entire metadata, trades accuracy for significantly reduced memory overhead, whereas the other array, with higher accuracy, caches partial distribution information and exploits the temporal locality of file access patterns. Both arrays are replicated to all metadata servers to support fast local lookups.

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HBA: Distributed Metadata Management for Large Cluster-Based Storage Systems

This paper presents a novel technique called Hierarchical Bloom Filter Arrays HBA to map filenames to the metadata servers holding their metadata. Two levels of probabilistic arrays, namely, the Bloom filter arrays with different levels of accuracies, are used on each metadata server. One array, with lower accuracy and representing the distribution of the entire metadata, trades accuracy for significantly reduced memory overhead, whereas the other array, with higher accuracy, caches partial distribution information and exploits the temporal locality of file access patterns. Both arrays are replicated to all metadata servers to support fast local lookups. We evaluate HBA through extensive trace-driven simulations and implementation in Linux. Simulation results show our HBA design to be highly effective and efficient in improving the performance and scalability of file systems in clusters with 1, to 10, nodes or superclusters and with the amount of data in the petabyte scale or higher. Our implementation indicates that HBA can reduce the metadata operation time of a single-metadata-server architecture by a factor of up to

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