Engineering Resilient Collective Adaptive Systems by Self-Stabilisation

TL;DR

This paper presents a methodology combining formal theory and simulation to engineer resilient, self-stabilising collective adaptive systems, enabling reliable and efficient operation in complex, dynamic networked environments.

Contribution

It introduces a formal framework based on field calculus for designing self-stabilising behaviors and demonstrates how to empirically evaluate different implementations for performance optimization.

Findings

Identified the largest fragment of field calculus that guarantees self-stabilisation.

Developed reusable building blocks for information spreading and aggregation.

Showed how to select implementations that enhance performance without compromising stability.

Abstract

Collective adaptive systems are an emerging class of networked computational systems, particularly suited in application domains such as smart cities, complex sensor networks, and the Internet of Things. These systems tend to feature large scale, heterogeneity of communication model (including opportunistic peer-to-peer wireless interaction), and require inherent self-adaptiveness properties to address unforeseen changes in operating conditions. In this context, it is extremely difficult (if not seemingly intractable) to engineer reusable pieces of distributed behaviour so as to make them provably correct and smoothly composable. Building on the field calculus, a computational model (and associated toolchain) capturing the notion of aggregate network-level computation, we address this problem with an engineering methodology coupling formal theory and computer simulation. On the one hand, functional properties are addressed by identifying the largest-to-date field calculus fragment generating self-stabilising behaviour, guaranteed to eventually attain a correct and stable final state despite any transient perturbation in state or topology, and including highly reusable building blocks for information spreading, aggregation, and time evolution. On the other hand, dynamical properties are addressed by simulation, empirically evaluating the different performances that can be obtained by switching between implementations of building blocks with provably equivalent functional properties. Overall, our methodology sheds light on how to identify core building blocks of collective behaviour, and how to select implementations that improve system performance while leaving overall system function and resiliency properties unchanged.