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. 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 1,000 to 10,000 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 43.9 when the system is configured with 16 metadata servers.

TABLE OF CONTENT
TITLE PAGE
CERTIFICATION
APPROVAL
DEDICATION
ACKNOWLEDGEMENT
ABSTRACT
TABLE OF CONTENT

CHAPTER ONE
1.0INTRODUCTION
1.1STATEMENT OF PROBLEM
1.2PURPOSE OF STUDY
1.3AIMS AND OBJECTIVES
1.4SCOPE/DELIMITATIONS
1.5LIMITATIONS/CONSTRAINTS
1.6DEFINITION OF TERMS

CHAPTER TWO
2.0LITERATURE REVIEW

CHAPTER THREE
3.0METHODS FOR FACT FINDING AND DETAILED DISCUSSIONS OF THE SYSTEM
3.1 METHODOLOGIES FOR FACT-FINDING 
3.2DISCUSSIONS

CHAPTER FOUR
4.0FUTURES, IMPLICATIONS AND CHALLENGES OF THE SYSTEM 
4.1FUTURES 
4.2IMPLICATIONS
4.3CHALLENGES

CHAPTER FIVE
5.0RECOMMENDATIONS, SUMMARY AND CONCLUSION
5.1RECOMMENDATION
5.2SUMMARY
5.3CONCLUSION
5.4REFERENCES
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APA

Possibility, A. (2018). DISTRIBUTED METADATA MANAGEMENT FOR LARGE CLUSTER-BASED STORAGE SYSTEMS. Afribary. Retrieved from https://afribary.com/works/distributed-metadata-management-for-large-cluster-based-storage-systems-4954

MLA 8th

Possibility, Aka "DISTRIBUTED METADATA MANAGEMENT FOR LARGE CLUSTER-BASED STORAGE SYSTEMS" Afribary. Afribary, 29 Jan. 2018, https://afribary.com/works/distributed-metadata-management-for-large-cluster-based-storage-systems-4954. Accessed 27 May. 2024.

MLA7

Possibility, Aka . "DISTRIBUTED METADATA MANAGEMENT FOR LARGE CLUSTER-BASED STORAGE SYSTEMS". Afribary, Afribary, 29 Jan. 2018. Web. 27 May. 2024. < https://afribary.com/works/distributed-metadata-management-for-large-cluster-based-storage-systems-4954 >.

Chicago

Possibility, Aka . "DISTRIBUTED METADATA MANAGEMENT FOR LARGE CLUSTER-BASED STORAGE SYSTEMS" Afribary (2018). Accessed May 27, 2024. https://afribary.com/works/distributed-metadata-management-for-large-cluster-based-storage-systems-4954

Document Details
Field: Computer Science Type: Project 35 PAGES (4262 WORDS) (rtf)