An Approach and Case Study of Cloud Instance Type Selection for Multi-tier Web Applications

A challenging problem for users of Infrastructure-as-a-Service (IaaS) clouds is selecting cloud providers, regions, and instance types cost-optimally for a given desired service level. Issues such as hardware heterogeneity, contention, and virtual machine (VM) placement can result in considerably differing performance across supposedly equivalent cloud resources. Existing research on cloud benchmarking helps, but often the focus is on providing low-level microbenchmarks (e.g., CPU or network speed), which are hard to map to concrete business metrics of enterprise cloud applications, such as request throughput of a multi-tier Web application. In this paper, we propose Okta, a general approach for fairly and comprehensively benchmarking the performance and cost of a multi-tier Web application hosted in an IaaS cloud. We exemplify our approach for a case study based on the two-tier AcmeAir application, which we evaluate for 11 real-life deployment configurations on Amazon EC2 and Google Compute Engine. Our results show that for this application, choosing compute-optimized instance types in the Web layer and small bursting instances for the database tier leads to the overall most cost-effective deployments. This result held true for both cloud providers. The least cost-effective configuration in our study provides only about 67% of throughput per US dollar spent. Our case study can serve as a blueprint for future industrial or academic application benchmarking projects.