ProDSPL: Proactive self-adaptation based on Dynamic Software Product Lines

Abstract

Dynamic Software Product Lines (DSPLs) are a well-accepted approach for self-adaptation at runtime. In the context of DSPLs, there are plenty of reactive approaches that apply countermeasures as soon as a context change happens, but they often imply making many reconfigurations, which makes the system more unstable. In this paper we propose a proactive approach, ProDSPL, that exploits an automatically learnt model of the system, which anticipates future variations of the system, and generates the best DSPL configuration that can soften the negative impact of future events on the quality requirements of the system. ProDSPL formulates the problem of the generation of dynamic reconfigurationas as a proactive controller over a prediction horizon, which includes a mapping of the valid configurations of the DSPL into linear constraints. Our approach is evaluated and compared against a reactive approach, DAGAME, based also on a DSPL, which uses a genetic algorithm to generate quasi-optimal feature model configurations at runtime. The evaluation with a mobile game and randomly generated feature models shows that ProDSPL gives good results with regard to the quality of the configurations generated that tries to anticipate future events and it always enforces the system to make the least possible reconfigurations.

Towards Self-Adaptive Game Logic

“…Creative coding is a form of programming in which the goal is generally considered to be some form of generative artwork or video game, typically created via abstractions over existing programming languages [10]. Processing (Java) and p5.js (JavaScript) 1 are two such abstractions that facilitate rapid development of creative coding applications. As such, creative coding can additionally be used to introduce students to high-level programming and game design concepts while minimizing the overhead and/or learning curve of graphics libraries (e.g., OpenGL, WebGL, SDL, etc.).…”

“…Cámara et al investigated how an SAS can optimize network latency via gamification and by modeling it as a stochastic multiplayer game [6]. Other methods of adaptation, among many, include the use of dynamic software product lines to update behaviors at run time [1], Bayesian optimization with fuzzy systems [16], and artificial intelligence [20].…”

RuVa: A Runtime Software Variability Algorithm

“…Hence, the reconfiguration to solve such problems require checking real-time constraints dynamically that are modeled using a constraint language. Some recent works like the ProDSPL approach [35] suggest a DSPL able to anticipate to future variations and generate the best configuration proactively as the variability model is transformed into linear constraints that are optimized as part of the decision-making process. The approach is evaluated using mobile strategy games where some features are configured at runtime.…”

Analysing Product Lines of Concurrent Systems with Coloured Petri Nets

“…Some authors analyse different aspects of DSPLs (but not for Petri nets). For instance, Sawyer et al [26] use constraint solving to find the optimal configuration of self-adaptive systems, Olaechea et al [27] employ trace checking to analyse the quality of service of all configurations of a DSPL, Göttmann et al [28] translate DSPLs into timed automata to analyse the worst/best execution time of reconfiguration sequences, Ayala et al [29] analyse DSPLs to predict the impact of recon- figurations on the system behaviour, and Quinton et al [30] detect inconsistencies that may arise upon evolving a DSPL. Compared to them, our proposal relies on a translation of Petri nets into CPNs, which enables the analysis of properties based on model checking and the reachability graph.…”