Application-specific Cloud Provisioning Model Using Job Profiles Analysis

The rapid advent of computing technology enables scientists to expand their research infrastructure over supercomputer-on-demand using a resource leasing service on pay-per-use basis. This infrastructure service is called as Science cloud which provides uniform user interface to scientific experiments over large-scale heterogeneous resources. In spite of the strength of conveniences, it is difficult to manage the experiments to guarantee optimal performance of jobs since the execution environment is based on virtualization technology which has additional performance overheads. This paper proposes a cloud resource provisioning model using statistical analysis of job profiles for computational science. In this model, we utilize job profiles which are generated from application executions and identify features of application by applying statistical analysis. The analysis is performed using PCA and the most effective factors are inferred from profiles. The effective factors are used for picking a job profile being referred and then VM type is determined using the most effective job's property. An application is executed on VM of the chosen cluster and its performance result is incorporated into job profile set with purpose of evaluating profile's credit. Our simulation results show this model enables scientists to take advantages of cloud computing without performance degradation.