We study the problem of identifiability of the total effect of an intervention from observational time series only given an abstraction of the causal graph of the system. Specifically, we consider two types of abstractions: the extended summary causal graph which conflates all lagged causal relations but distinguishes between lagged and instantaneous relations; and the summary causal graph which does not give any indication about the lag between causal relations. We show that the total effect is always identifiable in extended summary causal graphs and we provide necessary and sufficient graphical conditions for identifiability in summary causal graphs. Furthermore, we provide adjustment sets allowing to estimate the total effect whenever it is identifiable.

The 5G system should be flexible to cope with diversity of functions and infrastructure, in an efficient way while supporting a wide range of services associated with a set of requirements through Service Function Chaining (SFC). Fault management plays a paramount role to fulfill the SFC requirements towards reliability of telecommunication networks. Model-based (MB) approaches are knowledge-based systems that reason about a system from an explicit representation of its structure and functional behavior through a dependency model. MB techniques solve novel diagnosis problems and provide explanations for their decisions. However, current MB methods suffer from some limitations due to network virtualization challenges: lack of network visibility and dynamic topologies. To tackle those limitations, we propose a self-modeling approach and an active diagnosis process for virtual networks that considers two types of knowledge to build the model: acquired knowledge and learned knowledge provided by fault injection to expand and validate the proposed model. The experimental results from their application to a real-world virtual IP Multimedia Subsystem (vIMS) use case show that the self-modeling and the active diagnosis procedures are effective in determining the root cause(s) of a failure and explaining fault propagation.

Model based machine learning (MBML) techniques solve novel diagnosis problems and provide explanations for their decisions. However, current MBMLs suffer some limitations, since virtualization of network brings new challenges such as the dynamic topology and elasticity. Those limitations include the high dependency on previous knowledge and the difficulty to represent the model. To face those limitations, we propose SAKURA: a root cause analysis framework for the virtual Ip Multimedia Subsystem (vIMS). SAKURA is composed of a self-modeling and a constraints solver.

The advent of 5G and the ever increasing stringent requirements in terms of bandwidth, latency, and quality of service pushes the boundaries of what is feasible with legacy Mobile Network Operators' technologies. Network Functions Virtualization (NFV) is a promising attempt at solving some of these challenges that is widely adopted by the industry and specified by the standardization bodies. In essence, NFV is about running Network Functions (NFs) as virtualized workloads on commodity hardware. This may optimize deployment costs and simplify the lifecycle management of NFs, but it introduces new fault management challenges and issues. In this paper, we propose a comprehensive state of the art of fault management techniques. We address the impact of virtualization in fault management. We propose a new classification of the recent fault management research achievements in the network virtualization environments and compare their major contributions and shortcomings.

In component-based safety-critical real-time systems it is crucial to determine which component(s) caused the violation of a required system-level safety property, be it to issue a precise alert, or to determine liability of component providers. In this paper we present an approach for blaming in real-time systems whose component specifications are given as timed automata. The analysis is based on a single execution trace violating a safety property P. We formalize blaming using counterfactual reasoning ("what would have been the outcome if component C had behaved correctly?") to distinguish component failures that actually contributed to the outcome from failures that had no impact on the violation of P. We then show how to effectively implement blaming by reducing it to a model-checking problem for timed automata, and demonstrate the feasibility of our approach on the models of a pacemaker and of a chemical reactor.

A system-on-chip (SoC) contains numerous intellectual property blocks, or IPs. Protocol mismatches between IPs may affect the system-level functionality of the SoC. Mismatches are addressed by introducing converters to control inter-IP interactions. Current approaches towards converter generation find limited practical application as they use restrictive models, lack formal rigour, handle a small subset of commonly encountered mismatches, and/or are not scalable. We propose a formal technique for SoC design using incremental converter synthesis . The proposed formulation provides precise models for protocols and requirements, and provides a scalable algorithm that allows adding multiple components and requirements to an SoC incrementally. We prove that the technique is sound and complete. Experimental results obtained using real-life AMBA benchmarks show the scalability and wide range of mismatches handled by our approach.

We introduce CoSyMA, a tool for automatic controller synthesis for incrementally stable switched systems based on multi-scale discrete abstractions. The tool accepts a description of a switched system represented by a set of differential equations and the sampling parameters used to define an approximation of the state-space on which discrete abstractions are computed. The tool generates a controller - if it exists - for the system that enforces a given safety or time-bounded reachability specification. We illustrate by examples the synthesized controllers and the significant performance gains during their computation.

For the design of real-time embedded systems, analysis of performance and resource utilization at an early stage is crucial to evaluate design choices. Network Calculus and its variants provide the tools to perform such analyses for distributed systems processing streams of tasks, based on a max-plus algebra. However, the underlying model employed in Network Calculus cannot capture correlations between the availability of different resources and between the arrivals of tasks, leading to overly conservative performance bounds for some frequently used system topologies. We present a model based on timing constraints relative to pairs of streams, endowed with an analysis technique that can handle such correlations.

When available, discrete abstractions provide an appealing approach to controller synthesis. Recently, an approach for computing discrete abstractions of incrementally stable switched systems has been proposed, using the notion of approximate bisimulation. This approach is based on sampling of time and space where the sampling parameters must satisfy some relation in order to achieve a certain precision. Particularly, the smaller the sampling period, the finer the lattice approximating the state-space and the larger the number of states in the abstraction. This renders the use of these abstractions for synthesis of fast switching controllers computationally prohibitive. In this paper, we present a novel class of multiscale discrete abstractions for switched systems that allows us to deal with fast switching while keeping the number of states in the abstraction at a reasonable level. The transitions of our abstractions have various durations: for transitions of longer duration, it is sufficient to consider abstract states on a coarse lattice; for transitions of shorter duration, it becomes necessary to use finer lattices. These finer lattices are effectively used only on a restricted area of the state-space where the fast switching occurs. We show how to use these abstractions for multiscale synthesis of self-triggered switching controllers for reachability specifications under time optimization. We illustrate the merits of our approach by applying it to the boost DC-DC converter.

This paper introduces a theory of fault recovery for component-based models. In our framework, a model is specified in terms of a set of atomic components that are incrementally composed and synchronized by a set of glue operators. We define what it means for such models to provide a recovery mechanism, so that the model converges to its normal behavior in the presence of faults. We identify corrector (atomic or composite) components whose presence in a model is essential to guarantee recovery after the occurrence of faults. We also formalize component-based models that effectively separate recovery from functional concerns.

Genetic regulatory networks usually encompass a multitude of complex, interacting feedback loops. Being able to model and analyze their behavior is crucial for understanding their function. However, state space explosion is becoming a limiting factor in the formal analysis of genetic networks. This paper explores a modular approach for verification of reachability properties. A framework for component-based modeling of genetic regulatory networks, based on a modular discrete abstraction, is introduced. Then a compositional algorithm to efficiently analyze reachability properties of the model is proposed. A case study on embryonic cell differentiation involving several hundred cells shows the potential of this approach.

MOTIVATION: Investigating the relation between the structure and behavior of complex biological networks often involves posing the question if the hypothesized structure of a regulatory network is consistent with the observed behavior, or if a proposed structure can generate a desired behavior. RESULTS: The above questions can be cast into a parameter search problem for qualitative models of regulatory networks. We develop a method based on symbolic model checking that avoids enumerating all possible parametrizations, and show that this method performs well on real biological problems, using the IRMA synthetic network and benchmark datasets. We test the consistency between IRMA and time-series expression profiles, and search for parameter modifications that would make the external control of the system behavior more robust. AVAILABILITY: GNA and the IRMA model are available at http://ibis.inrialpes.fr/.

In order to increase their stealth, malware commonly use the self-modification property of programs. By doing so, programs can hide their real code so that it is difficult to define a signature for it. But then, what is the meaning of those programs: the obfuscated form, or the hidden one? Furthermore, from a computability perspective, it becomes hard to speak about the program since, its own code varies over time. To cope with these issues, we provide an operational semantics for self-modifying programs and we show that they can be constructively rewritten to a non-modifying program.

We present a framework integrating specification and scheduler generation for real time systems. In a first step, the system, which can include arbitrarily designed tasks (cyclic or sporadic, with or without precedence constraints, any number of resources and CPUs) is specified as a timed Petri net. In a second step, our tool generates the most general non preemptive online scheduler for the specification, using a controller synthesis technique.