Abstract: Self-adaptive systems have the ability to adapt themselves to changes in the environment and internal dynamics to achieve particular goals. Self-adaptation is typically realized using control loops. When systems are large, complex, and heterogeneous, a single control loop may not be sufficient. Given the central role control loops play in the way we conceptualize, design, and implement self-adaptive systems, their patterns of interaction should be made explicit to foster comprehension and analysability, and to serve as the basis for developing guidelines and documenting common design choices for engineers. Because of the diversity of ways that existing self-adaptive systems organize the various self-adaptation activities, it is timely to reflect on these systems to: (a) consolidate the knowledge in this area, and (b) to develop a systematic approach for describing different control schema. In this paper, we contribute with a simple notation for describing control schema with multiple control loops, which we believe helps in achieving (b), and we use this notation to describe a number of existing self-adaptation patterns, to begin to fulfill (a). From our study, we outline numerous remaining research challenges and future work in this area.

Abstract: Modern software systems are often compositions of entities that increasingly use self-adaptive capabilities to improve their behavior to achieve systematic quality goals. Self-adaptive managers for each component system attempt to provide locally optimal results, but if they cooperated and potentially coordinated their efforts it might be possible to obtain more globally optimal results. The emergent properties that result from such composition and cooperation of self-adaptive systems are not well understood, difficult to reason about, and present a key challenge in the evolution of modern software systems. For example, the effects of coordination patterns and protocols on emergent properties such as the resiliency of the collectives need to be understood when designing these systems. In this paper we propose that probabilistic model checking of stochastic multiplayer games (SMG) provides a promising approach to analyze, understand, and reason about emergent properties in collectives of adaptive systems (CAS). Probabilistic Model Checking of SMGs is a technique particularly suited to analyzing emergent properties in CAS, since SMG models capture: (i) the uncertainty and variability intrinsic to the CAS and its execution environment in the form of probabilistic and nondeterministic choices, and (ii) the competitive/cooperative aspects of the interplay among the constituent systems of the CAS. Analysis of SMGs allows us to reason about things like the worst case scenarios, which constitutes a new contribution to understanding emergent properties in CAS. We investigate the use of SMGs to show how they can be useful in analyzing the impact of communication topology for collections of fully cooperative systems defending against an external attack.