Cost-Effective Resource Configurations for Multi-Tenant Database Systems in Public Clouds

Rizwan Mian, Patrick Martin, Farhana Zulkernine, Jose Luis Vazquez-Poletti
2015 International Journal of Cloud Applications and Computing  
Cloud computing is a promising paradigm for deploying applications due to its large resource offerings on a payas-you-go basis. In this report, we examine the problem of determining the most cost-effective provisioning of a multitenant database system as a service over public clouds. We formulate the problem of resource provisioning, and then define a framework to solve it. Our framework uses heuristic based algorithms to select cost-effective configurations. The algorithms can optionally
more » ... e resource costs against penalties incurred from the violation of Service Level Agreements (SLAs) or opt for non SLA violating configurations. The specific resource demands on the virtual machines for a workload and SLAs are accounted for by our performance and cost models, which are used to predict performance and expected cost respectively. We validate our approach experimentally using workloads based on standard TPC database benchmarks in the Amazon EC2 cloud. Abstract Cloud computing is a promising paradigm for deploying applications due to its large resource offerings on a pay-as-you-go basis. In this report, we examine the problem of determining the most cost-effective provisioning of a multi-tenant database system as a service over public clouds. We formulate the problem of resource provisioning, and then define a framework to solve it. Our framework uses heuristic based algorithms to select cost-effective configurations. The algorithms can optionally balance resource costs against penalties incurred from the violation of Service Level Agreements (SLAs) or opt for non SLA violating configurations. The specific resource demands on the virtual machines for a workload and SLAs are accounted for by our performance and cost models, which are used to predict performance and expected cost respectively. We validate our approach experimentally using workloads based on standard TPC database benchmarks in the Amazon EC2 cloud.
doi:10.4018/ijcac.2015040101 fatcat:wy6w3svybza2he6e3copen7pe4