Managing uncertainty in self-adaptive systems with plan reuse and stochastic search.

Many software systems operate in environments where change and uncertainty are the rule, rather than exceptions. Techniques for self-adaptation allow these systems to automatically respond to environmental changes, yet they do not handle changes to the adaptive system itself, such as the addition or removal of adaptation tactics. Instead, changes in a self-adaptive system often require a human planner to redo an expensive planning process to allow the system to continue satisfying its quality requirements under different conditions; automated techniques typically must replan from scratch. We propose to address this problem by reusing prior planning knowledge to adapt in the face of unexpected situations. We present a planner based on genetic programming that reuses existing plans. While reuse of material in genetic algorithms has recently applied successfully in the area of automated program repair, we find that naïvely reusing existing plans for self-* planning actually results in a loss of utility. Furthermore, we propose a series of techniques to lower the costs of reuse, allowing genetic techniques to leverage existing information to improve planning utility when replanning for unexpected changes.