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Fault Tolerant Multitenant Database Server Consolidation
Abstract
Server consolidation is important in situations where a sequence of database tenants need
to be allocated (hosted) dynamically on a minimum number of cloud server machines. Given
a tenant’s load defined by the amount of resources that the tenant requires and a service-level-
agreement (SLA) between the tenant customer and the cloud service provider, resource cost
savings can be achieved by consolidating multiple database tenants on server machines. Ad-
ditionally, in realistic settings, server machines might fail causing their tenants to become un-
available. To address this, service providers place multiple replicas of each tenant on different
servers and reserve extra capacity to ensure that tenant failover will not result in overload on
any remaining server. The focus of this thesis is on providing effective strategies for placing
tenants on server machines so that the SLA requirements are met in the presence of failure of
one or more servers. We propose the Cube-Fit (CUBEFIT ) algorithm for multitenant database
server consolidation that saves resource costs by utilizing fewer servers than existing approaches
for analytical workloads. Additionally, unlike existing consolidation algorithms, CUBEFIT can
tolerate multiple server failures while ensuring that no server becomes overloaded. We provide
extensive theoretical analysis and experimental evaluation of CUBEFIT. We show that compared
to existing algorithms, the average case and worst case behavior of CUBEFIT is superior and that
CUBEFIT produces near-optimal tenant allocation when the number of tenants is large. Through
evaluation and deployment on a cluster of up to 73 machines as well as through simulation stud-
ies, we experimentally demonstrate the efficacy of CUBEFIT in practical settings.
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Cite this version of the work
Joseph Mate
(2016).
Fault Tolerant Multitenant Database Server Consolidation. UWSpace.
http://hdl.handle.net/10012/10178
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