Abstract: Aiming at the adjustment lag problem of reactive self-adaptive software systems, a proactive self-adaptive approach based on long short-term memory (LSTM) prediction driven is proposed. In this approach, LSTM neural network prediction technology is embedded in the analysis phase of monitor-analyze-plan -execute-knowledge (MAPE-K) control model; Operating data relating to self-adaptive environments, self-adaptive qualities, and self-adaptive goals, and historical data are used for classification prediction to form a self-adaptive early warning mechanism, which can effectively improve the proactive selfadaptive ability of software systems and reduce the lag influence of reactive self-adaptive decision-making at the same time. In order to illustrate the initiative, robustness and effectiveness of this approach, evaluation on the classic distribution tele-assistance system (dTAS) platform is carried out. Experimental results show that the proposed approach can provide early warning to self-adaptive demand, and enable software systems to complete proactive self-adaptive adjustment when necessary.

Key words: software self-adaptation, proactive self-adaptation, prediction driven, selfadaptive early warning