Using quantitative analysis to implement autonomic IT systems

The software underpinning today’s IT systems needs to adapt dynamically and predictably to rapid changes in system workload, environment and objectives. We describe a software framework that achieves such adaptiveness for IT systems whose components can be modelled as Markov chains. The framework comprises (i) an autonomic architecture that uses Markov-chain quantitative analysis to dynamically adjust the parameters of an IT system in line with its state, environment and objectives; and (ii) a method for developing instances of this architecture for real-world systems. Two case studies are presented that use the framework successfully for the dynamic power management of disk drives, and for the adaptive management of cluster availability within data centres, respectively

CADS*: Computer-Aided Development of self-* systems

We present the prototype tool CADS* for the computer-aided development of an important class of self-* systems, namely systems whose components can be modelled as Markov chains. Given a Markov chain representation of the IT components to be included into a self-* system, CADS* automates or aids (a) the development of the artifacts necessary to build the self-* system; and (b) their integration into a fully-operational self-* solution. This is achieved through a combination of formal software development techniques including model transformation, model-driven code generation and dynamic software reconfiguration

Approximating values of generalized-reachability stochastic games

Simple stochastic games are turn-based 2½-player games with a reachability objective. The basic question asks whether one player can ensure reaching a given target with at least a given probability. A natural extension is games with a conjunction of such conditions as objective. Despite a plethora of recent results on the analysis of systems with multiple objectives, the decidability of this basic problem remains open. In this paper, we present an algorithm approximating the Pareto frontier of the achievable values to a given precision. Moreover, it is an anytime algorithm, meaning it can be stopped at any time returning the current approximation and its error bound

Concurrent Bounded Model Checking

The Definitive Version can be found in the ACM Digital Library here: http://dx.doi.org/10.1145/2693208.2693240issue_date: January 2015 numpages: 5 acmid: 2693240 keywords: Bounded Model Checking, Concurrency, Symbolic Executionissue_date: January 2015 numpages: 5 acmid: 2693240 keywords: Bounded Model Checking, Concurrency, Symbolic Executionissue_date: January 2015 numpages: 5 acmid: 2693240 keywords: Bounded Model Checking, Concurrency, Symbolic Executio

Safety Verification for Deep Neural Networks with Provable Guarantees (Invited Paper)

Computing systems are becoming ever more complex, increasingly often incorporating deep learning components. Since deep learning is unstable with respect to adversarial perturbations, there is a need for rigorous software development methodologies that encompass machine learning. This paper describes progress with developing automated verification techniques for deep neural networks to ensure safety and robustness of their decisions with respect to input perturbations. This includes novel algorithms based on feature-guided search, games, global optimisation and Bayesian methods

Formal methods for the analysis of wireless network protocols

In this thesis, we present novel software technology for the analysis of wireless networks, an emerging area of computer science. To address the widely acknowledged lack of formal foundations in this field, probabilistic model checking, a formal method for verification and performance analysis, is used. Contrary to test and simulation, it systematically explores the full state space and therefore allows reasoning about all possible behaviours of a system.This thesis contributes to design, modelling, and analysis of ad-hoc networks and randomised distributed coordination protocols.First, we present a new hybrid approach that effectively combines probabilistic model checking and state-of-the-art models from the simulation community in order to improve the reliability of design and analysis of wireless sensor networks and their protocols. We describe algorithms for the automated generation of models for both analysis methods and their implementation in a tool.Second, we study spatial properties of wireless sensor networks, mainly with respect to Quality of Service and energy properties.Third, we investigate the contention resolution protocol of the networking standard ZigBee. We build a generic stochastic model for this protocol and analyse Quality of Service and energy properties of it. Furthermore, we assess the applicability of different interference models.Fourth, we explore slot allocation protocols, which serve as a bandwidth allocation mechanism for ad-hoc networks. We build a generic model for this class of protocols, study real-world protocols, and optimise protocol parameters with respect to Quality of Service and energy constraints. We combine this with the novel formalisms for wireless communication and interference models, and finally we optimise local (node) and global (network) routing policies.This is the first application of probabilistic model checking both to protocols of the ZigBee standard and protocols for slot allocation.This thesis is not currently available in ORA