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dc.contributor.authorCurino, Carlo
dc.contributor.authorJones, Evan Philip Charles
dc.contributor.authorMadden, Samuel R.
dc.contributor.authorBalakrishnan, Hari
dc.date.accessioned2012-10-24T12:16:01Z
dc.date.available2012-10-24T12:16:01Z
dc.date.issued2011-06
dc.identifier.isbn978-1-4503-0661-4
dc.identifier.urihttp://hdl.handle.net/1721.1/74218
dc.description.abstractIn most enterprises, databases are deployed on dedicated database servers. Often, these servers are underutilized much of the time. For example, in traces from almost 200 production servers from different organizations, we see an average CPU utilization of less than 4%. This unused capacity can be potentially harnessed to consolidate multiple databases on fewer machines, reducing hardware and operational costs. Virtual machine (VM) technology is one popular way to approach this problem. However, as we demonstrate in this paper, VMs fail to adequately support database consolidation, because databases place a unique and challenging set of demands on hardware resources, which are not well-suited to the assumptions made by VM-based consolidation. Instead, our system for database consolidation, named Kairos, uses novel techniques to measure the hardware requirements of database workloads, as well as models to predict the combined resource utilization of those workloads. We formalize the consolidation problem as a non-linear optimization program, aiming to minimize the number of servers and balance load, while achieving near-zero performance degradation. We compare Kairos against virtual machines, showing up to a factor of 12× higher throughput on a TPC-C-like benchmark. We also tested the effectiveness of our approach on real-world data collected from production servers at Wikia.com, Wikipedia, Second Life, and MIT CSAIL, showing absolute consolidation ratios ranging between 5.5:1 and 17:1.en_US
dc.language.isoen_US
dc.publisherAssociation for Computing Machinery (ACM)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1145/1989323.1989357en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alike 3.0en_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/en_US
dc.sourceMIT web domainen_US
dc.titleWorkload-Aware Database Monitoring and Consolidationen_US
dc.typeArticleen_US
dc.identifier.citationCarlo Curino, Evan P.C. Jones, Samuel Madden, and Hari Balakrishnan. 2011. Workload-aware database monitoring and consolidation. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of data (SIGMOD '11). ACM, New York, NY, USA, 313-324. DOI=10.1145/1989323.1989357 http://doi.acm.org/10.1145/1989323.1989357 © 2011 ACMen_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.mitauthorCurino, Carlo
dc.contributor.mitauthorJones, Evan Philip Charles
dc.contributor.mitauthorMadden, Samuel R.
dc.contributor.mitauthorBalakrishnan, Hari
dc.relation.journalProceedings of the 2011 ACM SIGMOD International Conference on Management of data (SIGMOD '11)en_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
dspace.orderedauthorsCurino, Carlo; Jones, Evan P.C.; Madden, Samuel; Balakrishnan, Harien
dc.identifier.orcidhttps://orcid.org/0000-0002-7470-3265
dc.identifier.orcidhttps://orcid.org/0000-0002-1455-9652
mit.licenseOPEN_ACCESS_POLICYen_US


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