Mate, Joseph2016-01-212017-01-212016-01-212016-01-08http://hdl.handle.net/10012/10178Server 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.enMultitenant DatabasesCloud ComputingServer ConsolidationMaster Thesis