Verified control and estimation for cloud computing

In this thesis we propose formal verification as a way to produce rigorous performance guarantees for resource control and estimation mechanisms in cloud computing. In particular, with respect to control, we focus on an automated resource provisioning mechanism, commonly referred to as auto-scaling, which allows resources to be acquired and released on demand. However, the shared environment, along with the exponentially large space of available parameters, makes the configuration of auto-scaling policies a challenging task. To address this problem, we propose a novel approach based on performance modelling and formal verification to produce performance guarantees on particular rule-based auto-scaling policies. We demonstrate the usefulness and efficiency of our techniques through a detailed validation process on two public cloud providers, Amazon EC2 and Microsoft Azure, targeting two cloud computing models, Infrastructure as a Service (IaaS) and Platform as a Service (PaaS), respectively. We then develop novel solutions for the problem of verifying state estimation algorithms, such as the Kalman filter, in the context of cloud computing. To achieve this, we first tackle the broader problem of developing a methodology for verifying properties related to numerical and modelling errors in Kalman filters. This targets more general applications such as automotive and aerospace engineering, where the Kalman filter has been extensively applied. This allows us to develop a general framework for modelling and verifying different filter implementations operating on linear discrete-time stochastic systems, and ultimately tackle the more specific case of cloud computing

Quantitative Verification of Kalman Filters

Kalman filters are widely used for estimating the state of a system based on noisy or inaccurate sensor readings, for example in the control and navigation of vehicles or robots. However, numerical instability or modelling errors may lead to divergenceof the filter, leading to erroneous estimations. Establishing robustness against such issues can be challenging. We propose novel formal verification techniques and software to perform a rigorous quantitative analysisof the effectiveness of Kalman filters. We present a general framework for modelling Kalman filterimplementations operating on linear discrete-time stochastic systems, and techniques to systematically construct a Markov model of the filter's operation using truncation and discretisation of the stochasticnoise model. Numerical stability and divergence properties are then verified using probabilistic model checking. We evaluate the scalability and accuracy ofour approach on two distinct probabilistic kinematic models and four Kalman filter implementations

Origin, diversification and substrate specificity in the family of NCS1/FUR transporters.

International audienceNCS1 proteins are H(+)/Na(+) symporters specific for the uptake of purines, pyrimidines and related metabolites. In this article, we study the origin, diversification and substrate specificity of fungal NCS1 transporters. We show that the two fungal NCS1 sub-families, Fur and Fcy, and plant homologues originate through independent horizontal transfers from prokaryotes and that expansion by gene duplication led to the functional diversification of fungal NCS1. We characterised all Fur proteins of the model fungus Aspergillus nidulans and discovered novel functions and specificities. Homology modelling, substrate docking, molecular dynamics and systematic mutational analysis in three Fur transporters with distinct specificities identified residues critical for function and specificity, located within a major substrate binding site, in transmembrane segments TMS1, TMS3, TMS6 and TMS8. Most importantly, we predict and confirm that residues determining substrate specificity are located not only in the major substrate binding site, but also in a putative outward-facing selective gate. Our evolutionary and structure-function analysis contributes in the understanding of the molecular mechanisms underlying the functional diversification of eukaryotic NCS1 transporters, and in particular, forward the concept that selective channel-like gates might contribute to substrate specificity