In self-adaptive software systems, the role of context is paramount, especially for proactive self-adaptation. Current research, however, does not fully explore context's impact, for example on priorities of the requirements. To address this gap, we introduce a novel contextual goal model to capture these factors and their influence on the system. Using this, we propose a two-layer control mechanism with a context-aware model predictive control to achieve proactive adaptation for the software system and adaptation for the controller itself. By contextual prediction and a more accurate system model, our approach utilizes model predictive control to facilitate timely and efficient system adaptations, improving both performance and adaptability. Meanwhile, we perform requirement adaptation to update the contextual goal model, which in turn updates the objective function and constraints of the controller. Our experimental evaluations across two scenarios demonstrate the significant benefits of our approach in enhancing system performance.

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.

Modern software runs in a dynamic, uncertain environment, and should satisfy multiple goals simultaneously. In order to allow software to respond to changes in the environment or user requirements and meet user goals continuously, an effective solution is to make the software self-adaptive. The adaptation capacity of software is provided by rules. As the complexity of self-adaptive software grows, designing and managing adaptation rules becomes increasingly challenging. To tackle this problem, some methods have been proposed to obtain adaptation rules automatically at runtime. However, these methods don't take the changes of user requirements into account sufficiently. When the user's preference of goals changes at runtime, adaptation rules usually need to be generated from scratch. It may produce huge computation cost. To overcome this limitation, we propose a multi-goal oriented approach for adaptation rules generation. This approach ensures that we can efficiently generate adaptation rules. We apply the approach to an unmanned underwater vehicles system. The experimental results show that our method is practical and highly-efficient in software reconfiguration under changing user's preference of goals.