CloudOptimizer: multi-tenancy for I/O-bound OLAP workloads

ABSTRACT

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.

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Index Terms

• Computing methodologies

  • Modeling and simulation

    • Model development and analysis

      • Modeling methodologies

• Information systems

  • Data management systems

    • Database management system engines