Analyzing Resilience Properties of Different Topologies of Collective Adaptive Systems

Abstract

Abstract-Modern software systems are often compositions of entities that increasingly use self-adaptive capabilities to improve their behavior to achieve systemic quality goals. Selfadaptive 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 a 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.

Probabilistic Model Checking and Autonomy

“…PRISM-games (2) enables the modelling and analysis of TSGs against the extended PRISM logic (see Definition 11) and multi-objective specifications. Applications using TSGs and PRISM-games to model autonomous systems include autonomous urban driving (72), smart grids (65), human-in-the-loop planning (75,76), managing collections of autonomic systems (77,78) and self-adaption (79). In addition, GIST (80) allows the analysis of ω-regular properties of TSGs and GAVS+ ( 81) is a generalpurpose tool for algorithmic game solving including TSGs.…”

A network tool to analyse and improve robustness of system architectures

“…They found that there is an optimum number of (Sharman and Yassine, 2004) Simple bus, multiple bus, auxiliary or weak or subsidiary buses, planar triangular clusters, tetrahedron of clusters, three-level design hierarchy. (Yassine and Naoum-Sawaya, 2016) Random, diagonal, block diagonal, local, hierarchical, dependent, small world, scale-free (Hölttä-Otto et al, 2012) Integral, bus-like, modular (Min et al, 2016) Integral, linear-modular, bus-modular (Rivkin and Siggelkow, 2007) Random, local, small-world, block-diagonal, preferential attachment, scale-free, centralised, hierarchical, diagonal, dependent (Glazier et al, 2015) Ring, mesh, star, fully connected, line, tree bus (Selva et al, 2016) Combining, assigning, partitioning, down-selecting, connecting (bus and star, ring, mesh tree), permuting (Baldwin et al, 2014) Core periphery, multi-core, hierarchical hubs that will give a positive influence on quality, highlighting the importance of the management of hubs during the early design stages. Network science studies of hubs in engineering systems include Braha & Bar-Yam (2004a, Mehrpouyan et al (2014), Braha (2016) and Piccolo et al (2018).…”

HaiQ

“…In the scenario [28], an external attacker uses a dened amount of available resources to attempt to breach members of the CAS (e.g., by placing a high number of requests). Each CAS member has the ability to detect the attack, defend itself against it by employing a xed set of defense resources, and has the ability to notify other members of the CAS of the attack.…”

“…This contrasts with ad-hoc solutions that demand developing specic infrastructure and are error-prone. The analysis of a CAS scenario similar to the one in Section 6.1.3 [28] required combining the PRISM preprocessor [48] with scripts that demanded topologies to be encoded as matrixes in separate text les, leading to multiple trial and error rounds (due to errors in matrix encodings, script tuning). For TAS, the problem has also been solved employing a custom template engine and a python script that generates probabilistic models based on analysis of Alloy specications [15].…”