CAP: A Cloud Auto-Provisioning Framework for Parallel Processing Using On-demand and Spot Instances

Cloud computing has drawn increasing attention from the scientific computing community due to its ease of use, elasticity, and relatively low cost. Because a high-performance computing (HPC) application is usually resource demanding, without careful planning, it can incur a high monetary expense even in Cloud. We design a tool called CAP (Cloud AutoProvisioning framework for Parallel Processing) to help a user minimize the expense of running an HPC application in Cloud, while meeting the user-specified job deadline. Given an HPC application, CAP automatically profiles the application, builds a model to predict its performance, and infers a proper cluster size that can finish the job within its deadline while minimizing the total cost. To further reduce the cost, CAP intelligently chooses the Cloud’s reliable on-demand instances or low-cost spot instances, depending on whether the remaining time is tight in meeting the application’s deadline. Experiments on Amazon EC2 show that the execution strategy given by CAP is costeffective, by choosing a proper cluster size and a proper instance type (on-demand or spot).