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

Javier Cámara, David Garlan, Bradley Schmerl and Ashutosh Pandey.


In Proceedings of the 10th DADS Track of the 30th ACM Symposium on Applied Computing, Salamanca, Spain, 13-17 April 2015.

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Abstract
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 run time, either by choosing among a set of predefi ned adaptation strategies, or by automatically generating adaptation plans. Prede fined strategy selection has allow computational overhead and facilitates dealing with uncertainty (e.g., by accounting explicitly for contingencies derived from unexpected outcomes of actions), but requires additional designer e ffort regarding the specifi cation of strategies and is unable to guarantee optimal solutions. In contrast, run time 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 di fferent 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.

Keywords: Assurance, Model Checking, Planning, Self-adaptation, Stochastic Games.  
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