On multi-dimensional packing problems
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
Query execution assurance for outsourced databases
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Dynamic authenticated index structures for outsourced databases
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
The five-minute rule twenty years later, and how flash memory changes the rules
DaMoN '07 Proceedings of the 3rd international workshop on Data management on new hardware
Automatic virtual machine configuration for database workloads
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Multi-tenant databases for software as a service: schema-mapping techniques
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Supporting Database Applications as a Service
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
FilMINT: An Outer Approximation-Based Solver for Convex Mixed-Integer Nonlinear Programs
INFORMS Journal on Computing
Workload-aware database monitoring and consolidation
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Intelligent management of virtualized resources for database systems in cloud environment
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Predicting in-memory database performance for automating cluster management tasks
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
CloudScale: elastic resource scaling for multi-tenant cloud systems
Proceedings of the 2nd ACM Symposium on Cloud Computing
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Consolidation of multiple databases on the same server allows service providers to save significant resources because many production database servers are often under-utilized. Recent research investigates the problem of minimizing the number of servers required to host a set of tenants when the working sets of tenants are kept in main memory (e.g., in-memory OLAP workloads, or OLTP workloads), thus the memory assigned to each tenant, as well as the I/O bandwidth and CPU time, are all dictated by the working set size of the tenant. Other research investigates the reverse problem when the number of servers is fixed, but the amount of resources allocated to different tenants on the same server needs to be configured to optimize a cost function. In this paper we investigate the problem when neither the number of servers nor the amount of resources allocated to each tenant are fixed. This problem arises when consolidating OLAP workloads of tenants whose service-level agreements (SLAs) allow for queries to be answered from disk. We study the trade-off between the amount of memory and the I/O bandwidth assigned to OLAP workloads, and develop a principled approach for allocating resources to tenants in a manner that minimizes the total number of servers required to host all tenants while satisfying the SLA of each tenant. We then explain how we modified InnoDB, the storage engine of MySQL, to be able to change the amount of resources allocated to each tenant at runtime, so as to account for fluctuations in workloads. Finally, we evaluate our approach experimentally using the TPC-H benchmark to demonstrate its effectiveness and accuracy.