Publications

Abstract : Predicting performance of CPU intensive applications for a target platform is of significant importance to IT industries. However, the target hardware platform for which we want to predict the performance is often different from the testbed on which the application performance measurements are done, and may not be unavailable for deployment for various practical reasons. This paper presents M3, a Measure-Measure-Model method, which uses a pipeline of three steps to address this problem. The methodology starts with measuring CPU service demands of the application on the testbed. Then, it builds clones that mimic the application code in terms of the type of operations, the number and size of network calls with external servers and API calls made at different layers of the technology stack. The clones are simple and easy to deploy, yet demonstrate the same speedup factor between the source and the target as the original application. The clones are then deployed on the testbed and on the target to estimate the application CPU service demand under light load generation. In the final step, this estimated CPU service demand is fed into specific performance modeling tools that are capable of predicting application performance on the target under arbitrary higher workload. Predictions made using this approach are validated against direct measurements made on five applications in Java and PHP, and on a number of combinations of testbed and target platforms (Intel and AMD servers) and the prediction error is always less than 20%.