A Cooperative Sampling Approach to Discovering Optimal Configurations in Large Scale Computing Systems

With the growing scale of current computing systems, traditional configuration tuning methods become less effective because they usually assume a small number of parameters in the system. In order to handle the scalability issue of configuration tuning, this paper proposes a cooperative optimization framework, which mimics the behavior of team playing to discover the optimal configuration setting in computing systems. We follow a `best of the best´ rule to decompose the tuning task into a number of small subtasks with manageable size and complexity. While each decomposed module is responsible for the optimization of its own configuration parameters, all the modules share the performance evaluations of new samples as common feedbacks to enhance their optimization objectives. As a result, the qualities of generated samples become improved during the search, and the cooperative sampling will eventually discover the optimal configurations in the system. Experimental results demonstrate that our proposed cooperative optimization can identify better solutions within limited time periods compared with other state of the art configuration search methods. Such advantage becomes more significant when the number of configuration parameters increases.