A performance prediction scheme for computation-intensive applications on cloud

As cloud computing services are gaining popularity, many organizations are considering migrating their large-scale computing applications to cloud. Different cloud service providers (CSPs) may have different computing platforms and billing methods. Most cloud customers don´t know which CSP is more suitable for their applications and how much computing resource should be purchased. To address this issue, in this paper, we present a performance prediction scheme that allows a cloud customer to accurately predict computing resource (e.g., running time) for an application. The proposed scheme identifies application´s control flow and scaling blocks, constructs a miniature version program to run in local machines, and then replays it in cloud to get the performance ratio between local and cloud. Our real-network experiments show that the scheme can achieve high prediction accuracy with low overhead.