Using Machine Learning for Recommending Service Demand Estimation Approaches - Position Paper

摘要

Service demands are key parameters in service and performance modeling. Hence, a variety of different approaches to service demand estimation exist in the literature. However, given a specific scenario, it is not trivial to select the currently best approach, since deep expertise in statistical estimation techniques is required and the requirements and characteristics of the application scenario might change over time (e.g., by varying load patterns). To tackle this problem, we propose the use of machine learning techniques to automatically recommend the best suitable approach for the target scenario. The approach works in an online fashion and can incorporate new measurement data and changing characteristics on-the-fly. Preliminary results show that executing only the recommended estimation approach achieves 99.6% accuracy compared to executing all approaches available, while speeding up the estimation time by 57%.