Predictive modeling for management of database resources in the cloud
Name
927409325-MIT.pdf
Description
Full printable version
Size
5.07 MB
Format
Adobe PDF
Checksum (MD5)
ccbf098087f3b5a03e240c950eebe5e1
Author(s)
Taft, Rebecca (Rebecca Yale)
Advisor(s)
Michael Stonebraker and Frans Kaashoek.
Date Issued
2015
Publisher
Massachusetts Institute of Technology
Abstract
Public cloud providers who support a Database-as-a-Service offering must efficiently allocate computing resources to each of their customers in order to reduce the total number of servers needed without incurring SLA violations. For example, Microsoft serves more than one million database customers on its Azure SQL Database platform. In order to avoid unnecessary expense and stay competitive in the cloud market, Microsoft must pack database tenants onto servers as efficiently as possible. This thesis examines a dataset which contains anonymized customer resource usage statistics from Microsoft's Azure SQL Database service over a three-month period in late 2014. Using this data, this thesis contributes several new algorithms to efficiently pack database tenants onto servers by collocating tenants with compatible usage patterns. An experimental evaluation shows that the placement algorithms, specifically the Scalar Static algorithm and the Dynamic algorithm, are able to pack databases onto half of the machines used in production while incurring fewer SLA violations. The evaluation also shows that with two different cost models these algorithms can save 80% of operational costs compared to the algorithms used in production in late 2014.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages [68]-[70]).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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