Paper Info
Title
Characterizing tenant behavior for placement and crisis mitigation in multitenant DBMSs
Abstract
A multitenant database management system (DBMS) in the cloud must continuously monitor the trade-off between efficient resource sharing among multiple application databases (tenants) and their performance. Considering the scale of \attn{hundreds to} thousands of tenants in such multitenant DBMSs, manual approaches for continuous monitoring are not tenable. A self-managing controller of a multitenant DBMS faces several challenges. 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' desired service level objective (SLO) is not achieved. We present Delphi, a self-managing system controller for a multitenant DBMS, and Pythia, a technique to learn behavior through observation and supervision using DBMS-agnostic database level performance measures. Pythia accurately learns tenant behavior even when multiple tenants share a database process, learns good and bad tenant consolidation plans (or packings), and maintains a pertenant history to detect behavior changes. Delphi detects performance crises, and leverages Pythia to suggests remedial actions using a hill-climbing search algorithm to identify a new tenant placement strategy to mitigate violating SLOs. Our evaluation using a variety of tenant types and workloads shows that Pythia can learn a tenant's behavior with more than 92% accuracy and learn the quality of packings with more than 86% accuracy. During a performance crisis, Delphi is able to reduce 99th percentile latencies by 80%, and can consolidate 45% more tenants than a greedy baseline, which balances tenant load without modeling tenant behavior.
Year
DOI
Venue
2013
10.1145/2463676.2465308
SIGMOD Conference
Keywords
Field
DocType
new tenant placement strategy,multitenant dbms,characterizing tenant behavior,tenant colocation,tenant behavior,crisis mitigation,multiple tenant,bad tenant consolidation plan,performance crisis,tenant type,dbms-agnostic database level performance,tenant load,multitenant dbmss,multitenancy
Data mining,Service level objective,Control theory,Search algorithm,System controller,Computer science,Multitenancy,Delphi,Shared resource,Database,Cloud computing
Conference
Citations 
PageRank 
References 
10
0.64
23
Authors
6
Name
Order
Citations
PageRank
Aaron J. Elmore135234.03
Sudipto Das2109951.98
Alexander Pucher3673.57
Divyakant Agrawal482011674.75
Amr El Abbadi567671569.95
Xifeng Yan66633280.06