MTD-DS: An SLA-Aware Decision Support Benchmark for Multi-Tenant Parallel DBMSs

Multi-tenant DBMSs are used by cloud providers for their Database-as-a-Service products. They could be single-node DBMSs installed in virtual machines, SQL-on-Hadoop systems or classic parallel relational DBMSs running on top of a shared-nothing or shared-disk architecture. For a cloud provider, it is interesting to measure these systems’ capability of dealing with multi-tenant workloads, i.e., taking advantage of the statistical multiplexing to obtain economic gain while being attractive by providing a good quality of service and a low bill to the tenants. In this paper, we present MTD-DS benchmark (with MTD for Multi-Tenant parallel DBMSs and DS for Decision Support). MTD-DS extends TPC-DS by adding a multi-tenant query workload generator, a performance Service Level Objectives generator, configurable Database-as-a-Service pricing models, and new metrics to measure the potential capability of a multi-tenant parallel DBMS in obtaining the best trade-off between the provider's benefit and the tenants’ satisfaction. Example experimental results have been produced to show the relevance and the feasibility of the MTD-DS benchmark.