A Proactive Self-Adaptation Approach for Software Systems based on Environment-Aware Model Predictive Control

Modern software systems need to maintain their goals in a highly dynamic environment, which requires self-adaptation. Many existing self-adaptive approaches are reactive, they execute the adaptation behavior after the goal violation. However, proactive adaptation can adapt before the goal violation to avoid adverse consequence so it has attracted more and more attention. Model predictive control is a widely used method to implement proactive adaptation. However, these works often ignore uncertainty of environment, which makes the prediction of the system inaccurate and affect the control effectiveness. Therefore, we propose an environment-aware model predictive control method. Its main idea is to add the environment state to the system model, predict the future state of the system according to the predicted environment state and the current state of the system, and solve the optimal control strategy. We use a web application simulation platform to evaluate our method. The results show that our method can achieve better adaptation results and reduce the occurrence of goal violation.