@inproceedings{
 title = {Towards building a lightweight key-value store on parallel file system},
 type = {inproceedings},
 year = {2015},
 volume = {2015-Octob},
 id = {c3d7c6b0-c6bf-3d01-9e3c-46567ffbb1ad},
 created = {2019-10-01T17:20:47.601Z},
 file_attached = {false},
 profile_id = {42d295c0-0737-38d6-8b43-508cab6ea85d},
 last_modified = {2019-10-01T17:23:38.372Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Zeng2015a},
 folder_uuids = {73f994b4-a3be-4035-a6dd-3802077ce863},
 private_publication = {false},
 abstract = {© 2015 IEEE. As data grows in number and size, big data applications begin to revolutionize the underlying storage system. On one hand, key-value store has prevailed as the back-end storage for big data applications owning to its schema-less data model, high scalability, and etc. On the other hand, parallel file system shared by multiple nodes offers large-capacity, high-throughput, as well as high-bandwidth access and is used widely in high performance computing (HPC) and cloud computing environments. In this paper, we explore the opportunity of building a lightweight key-value store that supports concurrent access over a parallel file system. The key-value store proposed relies on the sharing nature of parallel file system to provide distributed access. Instead of organizing a cluster of nodes with long running services to delegate the access, our key-value store simply embeds itself into applications and requires no long running services neither communication between nodes. Such a design not only simplifies the structure of a distributed key-value store but also avoids overhead introduced by having running services around the file system. We implemented a prototype of this system and compared it against Cassandra, a state-of-art key-value store. Preliminary results are promising.},
 bibtype = {inproceedings},
 author = {Zeng, J. and Plale, B.},
 doi = {10.1109/CLUSTER.2015.100},
 booktitle = {Proceedings - IEEE International Conference on Cluster Computing, ICCC}
}

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