@inproceedings{
 title = {A multi-tenant fair share approach to full-text search engine},
 type = {inproceedings},
 year = {2016},
 pages = {45-50},
 publisher = {IEEE Press},
 id = {36b6f627-29ee-3334-8f64-1f408389326a},
 created = {2019-10-01T17:20:58.701Z},
 file_attached = {false},
 profile_id = {42d295c0-0737-38d6-8b43-508cab6ea85d},
 last_modified = {2019-10-01T17:24:00.284Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Peng2016},
 source_type = {CONF},
 folder_uuids = {73f994b4-a3be-4035-a6dd-3802077ce863},
 private_publication = {false},
 abstract = {© 2016 IEEE. Full text search engines underly the search of major content providers, Google, Bing and Yahoo. Open source search engines, such as Solr and ElasticSearch, are highly scalable and widely used in a Software-as-a-Service (SaaS) manner, in which multiple tenants share a single resource for improved resource utilization and lower management cost. Sharing of a full text search engine can exhibit unfairness in the form of performance interference. We propose a multi-tenancy solution that provides fair share of resource usage of a SaaS hosted search engine. It includes a revised deficit round robin technique for admission control, query resource usage estimation and a deadlock breaking mechanism. Experimental results show that our approach works well for both monolithic and distributed search engines.},
 bibtype = {inproceedings},
 author = {Peng, Zong and Plale, Beth},
 doi = {10.1109/DataCloud.2016.010},
 booktitle = {Proceedings of the 7th International Workshop on Data-Intensive Computing in the Cloud}
}

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