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Characterizing tenant behavior for placement and crisis mitigation in multitenant DBMSs

Aaron J. Elmore, Sudipto Das, Alexander Pucher, Divyakant Agrawal, Amr El Abbadi, Xifeng Yan
2013 Proceedings of the 2013 international conference on Management of data - SIGMOD '13  
For instance, how to characterize a tenant given its variety of workloads, how to reduce the impact of tenant colocation, and how to detect and mitigate a performance crisis where one or more tenants'  ...  Considering the scale of hundreds to thousands of tenants in such multitenant DBMSs, manual approaches for continuous monitoring are not tenable.  ...  Acknowledgments The authors thank Neil Conway, Ken Salem, Jon Walker, Zhengkui Wang, Jerry Zheng, and the anonymous reviewers for providing useful feedback.  ... 
doi:10.1145/2463676.2465308 dblp:conf/sigmod/ElmoreDPAAY13 fatcat:yz3rho6g5ffidfoh44zm4n6qfq

Flexible Operator Embeddings via Deep Learning [article]

Ryan Marcus, Olga Papaemmanouil
2019 arXiv   pre-print
In addition to being labor intensive, the process of hand-engineering features must generally be repeated for each data management task, and may make assumptions about the underlying database that are  ...  We introduce flexible operator embeddings, a deep learning technique for automatically transforming query operators into feature vectors that are useful for a multiple data management tasks and is custom-tailored  ...  Efficient Estimation of Word Placement and Crisis Mitigation in Multitenant Representations in Vector Space. arXiv ’13. DBMSs. In SIGMOD ’13.  ... 
arXiv:1901.09090v2 fatcat:qdkutgv4gfegthn5hwlrx7foim