Cloud Capability Estimation and Recommendation in Black-Box Environments Using Benchmark-Based Approximation

As cloud computing has become popular and the number of cloud providers has proliferated over time, the first barrier to cloud users will be how to accurately estimate performance capabilities of many different clouds and then, select a right one for given complex workload based on estimates. Such cloud capability estimation and selection can be a big challenge since most clouds can be considered as black-boxes to cloud users by abstracting underlying infrastructures and technologies. This paper describes a cloud recommender system to recommend an optimal cloud configuration to users based on accurate estimates. To achieve this, our system generates the capability vector that consists of relative performance scores of resource types (e.g., CPU, memory, and disk) estimated for given user workload using benchmarks. Then, a search algorithm has been developed to identify an optimal cloud configuration based on these collected capability vectors. Experiments show our approach accurately estimate the performance capability (less than 10% error) while scalable in large search space.