Application Behavior Mapping across Heterogeneous Hardware Platforms

Predicting the application behavior such as its resource utilization in a new hardware machine is becoming an urgent issue as the increasing number of servers with various configurations show up in data centers and clouds. Current two categories of approaches, the test bed evaluation based and the software simulation based methods, both have certain shortcomings. While the test bed evaluation based approaches suffer from the lack of measurement data to build the prediction model, the simulation based methods intrinsically introduce uncertainties and errors in the data. In order to overcome those issues, this paper proposes a new solution that combines the current two separate processes. We develop a generalized regression model with L1 penalty to predict the application behavior from software simulation. Meanwhile we also use evaluations on real hardware instances to improve the model obtained from simulation. Our model improvement is grounded on the Bayesian learning theory, which elegantly embeds outcomes from both simulation and real evaluation stages into the final prediction. Experimental results show the higher prediction accuracy of our method compared with current techniques.