Optimal Planning for Architecture-Based Self-Adaptation Via Model Checking of Stochastic Games

Architecture-based approaches to self-adaptation rely on architectural descriptions to reason about the best way of adapting the structure and behavior of software-intensive systems at runtime, either by choosing among a set of predefined adaptation strategies, or by automatically generating adaptation plans. Predefined strategy selection has a low computational overhead and facilitates dealing with uncertainty (e.g., by accounting explicitly for contingencies derived from unexpected outcomes of actions), but requires additional designer effort regarding the specification of strategies and is unable to guarantee optimal solutions. In contrast, runtime plan generation is able to explore a richer solution space and provide optimal solutions in some cases, but is more limited when dealing with uncertainty, and incurs higher computational overheads. In this paper, we propose an approach to optimal adaptation plan generation for architecture-based self-adaptation via model checking of stochastic multiplayer games (SMGs). Our approach enables: (i) trade-off analysis among different qualities by means of utility functions and preferences, and (ii) explicit modeling of uncertainty in the outcome of adaptation actions and the behavior of the environment. Basing on the concepts embodied in the Rainbow framework for self-adaptation, we illustrate our approach in Znn.com, a case study that reproduces the infrastructure for a news website.