Most software-driven systems we commonly use in our daily life are
huge hierarchical assemblings of components. This observation runs
from the micro-scale (multi-core chips) to the macro-scale (data
centers), and from hardware systems (telecommunication networks) to
software systems (choreographies of web services). The main
characteristics of these pervasive applications are size,
complexity, heterogeneity, and modularity (or concurrency). Besides,
several such systems are actively used before they are fully
mastered, or they have grown so much that they now raise new
problems that are hardly manageable by human operators. While these
systems and applications are becoming more essential, or even
critical, the need for their reliability, efficiency
and manageability becomes a central concern in computer
science. The main objective of SUMO is to develop theoretical
tools to address such challenges, according to the following axes.

Several disciplines in computer science have of course addressed some of the issues raised by large systems. For example, formal methods (essentially for verification purposes), discrete-event systems (diagnosis, control, planning, and their distributed versions), but also concurrency theory (modelling and analysis of large concurrent systems). Practical needs have oriented these methods towards the introduction of quantitative aspects, such as time, probabilities, costs, and their combinations. This approach drastically changes the nature of questions that are raised. For example, verification questions become the reachability of a state in a limited time, the average sojourn duration in a state, the probability that a run of the system satisfies some property, the existence of control strategies with a given winning probability, etc. In this setting, exact computations are not always appropriate as they may end up with unaffordable complexities, or even with undecidability. Approximation strategies then offer a promising way around, and are certainly also a key to handling large systems. Approaches based on discrete-event systems follow the same trend towards quantitative models. For diagnosis aspects, one is interested in the most likely explanations to observed malfunctions, in the identification of the most informative tests to perform, or in the optimal placement of sensors. For control problems, one is of course interested in optimal control, in minimizing communications, in the robustness of the proposed controllers, in the online optimization of QoS (Quality of Service) indicators, etc.

While the above questions have already received partial answers, they remain largely unexplored in a distributed setting. We focus on structured systems, typically a network of dynamic systems with known interaction topology, the latter being either static or dynamic. Interactions can be synchronous or asynchronous. The state-space explosion raised by such systems has been addressed through two techniques. The first one consists in adopting true-concurrency models, which take advantage of the parallelism to reduce the size of the trajectory sets. The second one looks for modular or distributed "supervision" methods, taking the shape of a network of local supervisors, one per component. While these approaches are relatively well understood, their mixing with quantitative models remains a challenge (as an example, there exists no proper setting assembling concurrency theory with stochastic systems). This field is largely open both for modeling, analysis and verification purposes, and for distributed supervision techniques. The difficulties combine with the emergence of data-driven distributed systems (as web services or data centric systems), where the data exchanged by the various components influence both the behaviors of these components and the quantitative aspects of their reactions (e.g. QoS). Such systems call for symbolic or parametric approaches for which a theory is still missing.

Some existing distributed systems like telecommunication networks,
data centers, or large-scale web applications have reached sizes and
complexities that reveal new management problems. One can no longer
assume that the model of the managed systems is static and fully
known at any time and any scale. To scale up the management methods
to such applications, one needs to be able to design reliable
abstractions of parts of the systems, or to dynamically build a part
of their model, following the needs of the management functions to
realize. Besides, one does not wish to define management objectives
at the scale of each single component, but rather to pilot these
systems through high-level policies (maximizing throughput,
minimizing energy consumption, etc.) These distributed systems and
management problems have connections with other approaches for the
management of large structured stochastic systems, such as Bayesian
networks (BN) and their variants. The similarity can actually be
made more formal: inference techniques for BN rely on the concept of
conditional independence, which has a counterpart for networks of
dynamic systems and is at the core of techniques like
distributed diagnosis, distributed optimal planning, or the
synthesis of distributed controllers. The potential of this
connection is largely unexplored, but it suggests that one could
derive from it good approximate management methods for large
distributed dynamic systems.

Since its creation in 2015, SUMO has successfully developed formal methods for large quantitative systems, in particular addressing verification, synthesis and control problems. Our current motivation is to expand this by putting emphasis on new concerns, such as algorithm efficiency, imprecision handling, and the more challenging objective of addressing incomplete or missing models. In the following we list a selection of detailed research goals, structured into four axes according to model classes: quantitative models, large systems, population models, and data-driven models. Some correspond to the pursuit of previously obtained results, others are more prospective.

The analysis and control of quantitative models will remain at the heart of a large part of our
research activities. In particular, we have two starting
collaborative projects focusing on timed models,
namely our ANR project TickTac and our collaboration with MERCE. The
main expected outcome of TickTac is an open-source tool implementing
the latest algorithms and allowing for quick prototyping of new
algorithms. Several other topics will be explored in these
collaborations, including robustness issues, game-theoretic problems, as well as the
development of efficient algorithms, e.g. based on CEGAR approach or
specifically designed for subclasses of automata (e.g. automata with
few clocks and/or having a specific structure, as
in ).
Inspired by our collaboration with Alstom, we also aim at
developing symbolic techniques for analysing non-linear timed models.

Stochastic models are another important focus for our research. On the one hand, we want to pursue our work on the optimization of non-standard properties for Markov decision processes, beyond the traditional verification questions, and explore e.g. long-run probabilities, and quantiles. Also, we aim at lifting our work on decisiveness from purely stochastic , to non-deterministic and stochastic models in order to provide approximation schemes for the probability of (repeated) reachability properties in infinite-state Markov decision processes.
On the other hand, in order to effectively handle large stochastic systems, we will
pursue our work on approximation techniques. We aim at deriving simpler models, enjoying or preserving specific properties, and at determining the appropriate level of abstraction for a given system. One needs of course to quantify the approximation degrees (distances), and to preserve essential features of the original systems (explainability). This is a connection point between formal methods and the booming learning methods.

Regarding diagnosis/opacity issues, we will explore further the quantitative aspects. For diagnosis, the theory needs extensions to the case of incomplete or erroneous models, and to reconfigurable systems, in order to develop its applicability (see Sec. ). There is also a need for non-binary causality analysis (e.g. performance degradations in complex systems). For opacity, we aim at quantifying the effort attackers must produce vs how much of a secret they can guess. We also plan to synthesize robust controllers resisting to sensor failures/attacks.

Part of the background of SUMO is on the analysis and management of concurrent and modular/distributed systems, which we view as two main approaches to address state explosion problems. We will pursue the study of these models (including their quantitative features): verification of timed concurrent systems, robust distributed control of modular systems, resilient control to coalitions of attackers, distributed diagnosis, modular opacity analysis, distributed optimal planning, etc. Nevertheless, we have identified two new lines of effort, inspired by our application domains.

Reconfigurable systems. This is mostly motivated by applications at the convergence of virtualization techs with networking (Orange and Nokia PhDs). Software defined networks, either in the core (SDN/NFV) or at the edge (IoT) involve distributed systems that change structure constantly, to adapt to traffic, failures, maintenance, upgrades, etc. Traditional verification, control, diagnosis approaches (to mention only those) assume static and known models that can be handled as a whole. This is clearly insufficient here: one needs to adapt existing results to models that (sometimes automatically)
change structure, incorporate new components/users or lose some, etc. At the same time, the programming paradigms for such systems (chaos monkey) incorporate resilience mechanisms, that should be considered by our models.

Hierarchical systems. Our experience with the regulation of subway lines (Alstom) revealed that large scale complex systems are usually described at a single level of granularity. Determining the appropriate granularity is a problem in itself. The control of such systems, with humans in the loop, can not be expressed at this single level, as tasks become too complex and require extremely skilled staff. It is rather desirable to describe models simultaneously at different levels of granularity, and to perform control at the appropriate level: humans in charge of managing the system by high level objectives, and computers in charge of implementing the appropriate micro-control sequences to achieve these tasks.

We want to step up our effort in parameterized verification of systems consisting of many identical components, so-called population models. In a nutshell our objectives summarize as "from Boolean to quantitative".

Inspired by our experience on the analysis of populations of yeasts, we aim at developping the quantitative analysis and control of population models, e.g. using Markov decision processes together with quantitative properties, and focusing on generating strategies with fast convergence.

As for broadcast networks, the challenge is to model the mobility of nodes (representing mobile ad hoc networks) in a faithful way. The obtained model should reflect on the one hand, the placement of nodes at a given time instant, and on the other hand, the physical movement of nodes over time. In this context, we will also use game theory techniques which allows one to study cooperative and conflictual behaviors of the nodes in the network, and to synthesize correct-by-design systems in adversarial environments.

As a new application area, we target randomized distributed algorithms. Our goal is to provide probabilistic variants of threshold automata to represent fault-tolerant randomized distributed algorithms, designed for instance to solve the consensus problem. Most importantly, we then aim at developing new parameterized verification techniques, that will enable the automated verification of the correctness of such algorithms, as well as the assessment of their performances (in particular the expected time to termination).

In this axis, we will investigate whether fluid model checking and mean-field approximation techniques apply to our problems. More generally, we aim at a fruitful cross-fertilizing of these approaches with parameterized model-checking algorithms.

In this axis, we will consider data-centric models, and in particular their application to crowd-sourcing. Many data-centric models such as Business Artifacts orchestrate simple calls and answers to tasks performed by a single user. In a crowd-sourcing context, tasks are realized by pools of users, which may result in imprecise, uncertain and (partially) incompatible information. We thus need mechanisms to reconcile and fuse the various contributions in order to produce reliable information. Another aspect to consider concerns answers of higher-order: how to allow users to return intentional answers, under the form of a sub-workflow (coordinated set of tasks) which execution will provide the intended value. In the framework of the ANR Headwork we will build on formalisms such as GAG (guarded attribute grammars) or variants of business artifacts to propose formalisms adapted to crowd-sourcing applications, and tools to analyze them. To address imprecision, we will study techniques to handle fuzziness in user answers, will explore means to set incentives (rewards) dynamically, and to set competence requirements to guide the execution of a complex workflow, in order to achieve an objective with a desired level of quality.

In collaboration with Open Agora, CESPA and University of Yaoundé (Cameroun) we intend to implement in the GAG formalism some elements of argumentation theory (argumentation schemes, speech acts and dialogic games) in order to build a tool for the conduct of a critical discussion and the collaborative construction of expertise. The tool would incorporate point of view extraction (using clustering mechanisms), amendment management and consensus building mechanisms.

We are concerned with one important lesson derived from our involvement in several application domains. Most of our background gets in force as soon as a perfect model of the system under study is available. Then verification, control, diagnosis, test, etc. can mobilize a solid background, or suggest new algorithmic problems to address. In numerous situations, however, assuming that a model is available is simply unrealistic. This is a major bottleneck for the impact of our research. We therefore intend to address this difficulty, in particular for the following domains.

The smart-city trend aims at optimizing all functions of future cities with the help of digital technologies. We focus on the segment of urban trains, which will evolve from static and scheduled offers to reactive and eventually on-demand transportation offers. We address two challenges in this field. The first one concerns the optimal design of robust subway lines. The idea is to be able to evaluate, at design time, the performance of time tables and of different regulation policies. In particular, we focus on robustness issues: how can small perturbations and incidents be accommodated by the system, how fast will return to normality occur, when does the system become unstable? The second challenge concerns the design of new robust regulation strategies to optimize delays, recovery times, and energy consumption at the scale of a full subway line. These problems involve large-scale discrete-event systems, with temporal and stochastic features, and translate into robustness assessment, stability analysis and joint numerical/combinatorial optimization problems on the trajectories of these systems.

Telecommunication-network management is a rich provider of research topics for the team, and some members of SUMO have a long background of contacts and transfer with industry in this domain. Networks are typical examples of large distributed dynamic systems, and their management raises numerous problems ranging from diagnosis (or root-cause analysis), to optimization, reconfiguration, provisioning, planning, verification, etc. They also bring new challenges to the community, for example on the modeling side: building or learning a network model is a complex task, specifically because these models should reflect features like the layering, the multi-resolution view of components, the description of both functions, protocols and configuration, and they should also reflect dynamically-changing architectures. Besides modeling, management algorithms are also challenged by features like the size of systems, the need to work on abstractions, on partial models, on open systems, etc. The networking technology is now evolving toward software-defined networks, virtualized-network functions, multi-tenant systems, etc., which reinforces the need for more automation in the management of such systems.

Data centers are another example of large-scale modular dynamic and reconfigurable systems: they are composed of thousands of servers, on which virtual machines are activated, migrated, resized, etc. Their management covers issues like troubleshooting, reconfiguration, optimal control, in a setting where failures are frequent and mitigated by the performance of the management plane. We have a solid background in the coordination of the various autonomic managers that supervise the different functions/layers of such systems (hardware, middleware, web services, ...) Virtualization technologies now reach the domain of networking, and telecommunication operators/vendors evolve towards providers of distributed open clouds. This convergence of IT and networking strongly calls for new management paradigms, which is an opportunity for the team.

A current trend is to involve end-users in collection and analysis of data. Exemples of this trend are contributive science, crisis-management systems, and crowd-sourcing applications. All these applications are data-centric and user-driven. They are often distributed and involve complex, and sometimes dynamic workflows. In many cases, there are strong interactions between data and control flows: indeed, decisons taken regarding the next tasks to be launched highly depend on collected data. For instance, in an epidemic-surveillance system, the aggregation of various reported disease cases may trigger alerts. Another example is crowd-sourcing applications where user skills are used to complete tasks that are better performed by humans than computers. In return, this requires addressing imprecise and sometimes unreliable answers. We address several issues related to complex workflows and data. We study declarative and dynamic models that can handle workflows, data, uncertainty, and competence management.

Once these models are mature enough, we plan to build prototypes to experiment them on real use cases from contributive science, health-management systems, and crowd-sourcing applications. We also plan to define abstraction schemes allowing formal reasonning on these systems.

Rituraj Singh has received the BDA 2021 PhD Thesis Award.

addresses reliability of timed systems in the setting of resilience, that considers the behaviors of a system when unspecified timing errors such as missed deadlines occur. Given a fault model that allows transitions to fire later than allowed by their guard, a system is universally resilient (or self-resilient) if after a fault, it always returns to a timed behavior of the non-faulty system. It is existentially resilient if after a fault, there exists a way to return to a timed behavior of the non-faulty system, that is, if there exists a controller which can guide the system back to a normal behavior. We show that universal resilience of timed automata is undecidable, while existential resilience is decidable, in EXPSPACE. To obtain better complexity bounds and decidability of universal resilience, we consider untimed resilience, as well as subclasses of timed automata.

The Connected Multi-Agent Path Finding (CMAPF) problem asks for a plan to move a group of agents in a graph while respecting a connectivity constraint. In

, we study a generalization of CMAPF in which the graph is not entirely known in advance, but is discovered by the agents during their mission. We present a framework introducing this notion and study the problem of searching for a strategy to reach a configuration in this setting. We prove the problem to be PSPACE-complete when requiring all agents to be connected at all times, and NEXPTIME-complete in the decentralized case, regardless of whether we consider a bound on the length of the execution.

In

, we consider feedback control systems where sensor readings may be compromised by a malicious attacker intending on causing damage to the system. We study this problem at the supervisory layer of the control system, using discrete event systems techniques. We assume that the attacker can edit the outputs from the sensors of the system before they reach the supervisory controller. In this context, we formulate the problem of synthesizing a supervisor that is robust against the class of edit attacks on the sensor readings and present a solution methodology for this problem. This methodology blends techniques from games on automata with imperfect information with results from supervisory control theory of partially-observed discrete event systems. Necessary and sufficient conditions are provided for the investigated problem.

Software-Defined Network (SDN) technology provides the possibility to turn the network infrastructure into a dynamic programmable fabric capable of meeting the application needs in real-time. Thanks to the independence of the control plane from the data plane, the control entity, generally called as controller, has also the flexibility to implement proprietary complex algorithms. Within such a dynamic and complex environment, (long version ) advocates for applying formal verification methods and more precisely composition model checking to ensure the correct behavior of the overall SDN system at design phase. To illustrate this purpose, it proposes to build different comprehensive formal models of a typical SDN platform selected here as a study object. Thorough performance results related to each model are provided and discussed. Thanks to such formal verifications, it is possible to pinpoint issues such as the one regarding network isolation within a complex SDN architecture. Although dealing with formal methods, this document attempts to strike a balance between theory, experimental work and network architecture discussion.

In

, we analyse how train delays propagate in a metro network due to disturbances and disruptions when different recovery strategies are implemented. Metro regulators use traffic management policies to recover from delays as fast as possible, return to a predefined schedule, or achieve an expected regularity of train arrivals and departures. We use as a metro traffic simulator SIMSTORS, which is based on a Stochastic Petri Net variant and simulates a physical system controlled by traffic management algorithms. To model existing metro networks, SIMSTORS has been mainly used with rule-based traffic management algorithms. In this work, we enhance traffic management strategies. We integrate SIMSTORS and the AGLIBRARY optimization solver in a closed-loop framework. AGLIBRARY is a deterministic solver for managing complex scheduling and routing problems. We formulate the real-time train rescheduling problem by means of alternative graphs, and use the decision procedures of AGLIBRARY to obtain rescheduling solutions. Several operational issues have been investigated throughout the use of the proposed simulation-optimization framework, among which how to design suitable periodic or event-based rescheduling strategies, how to setup the traffic prediction horizon, how to decide the frequency and the length of the optimization process. The Santiago Metro Line 1, in Chile, is used as a practical case study. Experiments with this framework in various settings show that integrating the optimization algorithms provided by AGLIBRARY to the rule-based traffic management embedded in SIMSTORS optimizes performance of the network, both in terms of train delay minimization and of service regularity.

Broadcast networks allow one to model networks of identical nodes communicating through message broadcasts. Their parameterized verification aims at proving a property holds for any number of nodes, under any communication topology, and on all possible executions. In , we focus on the coverability problem which dually asks whether there exists an execution that visits a configuration exhibiting some given state of the broadcast protocol. Coverability is known to be undecidable for static networks, i.e. when the number of nodes and communication topology is fixed along executions. In contrast, it is decidable in PTIME when the communication topology may change arbitrarily along executions, that is for reconfigurable networks. Surprisingly, no lower nor upper bounds on the minimal number of nodes, or the minimal length of covering execution in reconfigurable networks, appear in the literature. In this paper we show tight bounds for cutoff and length, which happen to be linear and quadratic, respectively, in the number of states of the protocol. We also introduce an intermediary model with static communication topology and non-deterministic message losses upon sending. We show that the same tight bounds apply to lossy networks, although, reconfigurable executions may be linearly more succinct than lossy executions. Finally, we show NP-completeness for the natural optimisation problem associated with the cutoff.

Distributed algorithms typically run over arbitrary many processes and may involve unboundedly many rounds, making the automated verification of their correctness challenging. In the following papers, we addressed the verification of (randomized) fault-tolerant distributed algorithms.

Randomized fault-tolerant distributed algorithms pose a number of challenges for automated verification: (i) parameterization in the number of processes and faults, (ii) randomized choices and probabilistic properties, and (iii) an unbounded number of asynchronous rounds. This combination makes verification hard. Challenge (i) was recently addressed in the framework of threshold automata.

Weak adversaries are a way to model the uncertainty due to asynchrony in randomized distributed algorithms. They are a standard notion in correctness proofs for distributed algorithms, and express the property that the adversary (scheduler), which has to decide which messages to deliver to which process, has no means of inferring the outcome of random choices, and the content of the messages. In , we introduce a model for randomized distributed algorithms that allows us to formalize the notion of weak adversaries. It applies to randomized distributed algorithms that proceed in rounds and are tolerant to process failures. For this wide class of algorithms, we prove that for verification purposes, the class of weak adversaries can be restricted to simple ones, so-called round-rigid adversaries, that keep the processes tightly synchronized. As recently a verification method for round-rigid adversaries has been introduced, our new reduction theorem paves the way to the parameterized verification of randomized distributed algorithms under the more realistic weak adversaries.

In

we develop a notion of data-driven lazy services by building up from the model of guarded attributed grammars that we previously introduced in the context of distributed collaborative systems. We abstract from this model and limit somewhat its expressiveness so that it can comply more broadly to SOA principles. We introduce an improvement on subscription management to optimize the distributed implementation of lazy services. A service oriented architecture (SOA) aims to structure complex distributed systems in terms of re-usable components, called services. To guarantee a good service interoperability these services must be weakly coupled and their description must be separated from their implementations. The interface of a service provides information on how it can be invoked: the logical location where it can be invoked, the supported communication protocol and the types of its input (parameters) and output (result). Traditionally, a service can only be invoked when its parameters are fully defined and, symmetrically, these services only return their results after they have been totally processed. In this work, we promote a more liberal view of services by allowing them to consume their data lazily (i.e., as they need it) and produce their results incrementally (i.e., as they are produced).

Crowdsourcing is a way to solve problems that need human contribution. Crowdsourcing platforms distribute replicated tasks to workers, pay them for their contribution, and aggregate answers to produce a reliable conclusion. A fundamental problem is to infer a consensual answer from the set of returned results. Another problem is to obtain this answer at a reasonable cost: unlimited budget allows hiring experts or large pools of workers for each task but a limited budget forces to use resources at best. Last, crowdsourcing platforms have to detect and ban malevolent users (also known as "spammers") to achieve good accuracy of their answers. This paper considers crowdsourcing of simple Boolean tasks. We first define a probabilistic inference technique, that considers difficulty of tasks and expertise of workers when aggregating answers. We then propose CrowdInc, a greedy algorithm that reduces the cost needed to reach a consensual answer. CrowdInc distributes resources dynamically to tasks according to their difficulty. The algorithm solves batches of simple tasks in rounds that estimate workers expertize, tasks difficulty, and synthesizes a plausible aggregated conclusion and a confidence score using Expectation Maximization. The synthesized values are used to decide whether more workers should be hired to increase confidence in synthesized answers. We show on several benchmarks that CrowdInc achieves good accuracy, reduces costs and we compare its performance to existing solutions. We then use the estimation of CrowdInc to detect spammers and study the impact of spammers on costs and accuracy.

Crowdsourcing platforms provide tools to replicate and distribute micro tasks (simple, independent work units) to crowds and assemble results. However, real-life problems are often complex: they require to collect, organize or transform data, with quality and costs constraints. work considers dynamic realization policies for complex crowdsourcing tasks. Workflows provide ways to organize a complex task in phases and guide its realization. The challenge in , is then to deploy a workflow on a crowd, i.e., allocate workers to phases so that the overall workflow terminates, with good accuracy of results and at a reasonable cost. Standard "static" allocation of work in crowdsourcing affects a fixed number of workers per micro-task to realize and aggregates the results. In , we define new dynamic worker allocation techniques that consider progress in a workflow, quality of synthesized data, and remaining budget. Evaluation on a benchmark shows that dynamic approaches outperform static ones in terms of cost and accuracy.

considers the fault diagnosis problem in large scale telecommunication networks. The focus is on software defined networks (SDN) deployed over a cloud infrastructure, for example through containers via Kubernetes. Numerous approaches to root-cause analysis for such systems rely on learning techniques, which hardly resist to the changing structure of these networks and fails at providing explanations. To circumvent these limitations, we aim at model-based methods, capable of explaining fault propagations from root causes down to symptom patterns. This raises several difficulties: how to automatically build a model of such a system, how to track its evolution, what is the appropriate modeling granularity, how to validate the model (soundness and completeness), and finally how to use it for diagnosis purposes. This paper develops a “self-modeling" methodology for these large systems, capturing resources dependencies from physical/virtual equipment up to software components and high-level functions and procedures like the opening of a call session. This model is translated into a Bayesian network, used as the support for diagnosis algorithms. The approach is illustrated on a real case: vIMS, a virtualized version of the IP Multimedia Subsystem.

Several researchers of SUMO are involved in the joint research lab of Nokia Bell Labs France and Inria. We participate in the common research team SAPIENS (Smart Automated and Programmable Infrastructures for End-to-end Networks and Services), previously named “Softwarization of Everything.” This team involves several other Inria teams: Convecs, Diverse and Spades. SUMO focuses on the management of reconfigurable systems, both at the edge (IoT based applications) and in the core (

virtualized IMS systems). In particular, we study control and diagnosis issues for such systems. A PhD student is involved in the project: Abdul Majith (started in January 2019) on Controller Synthesis of Adaptive Systems, supervised by Hervé Marchand, Ocan Sankur and Dinh Thai Bui (Nokia Bell Labs).

Several researchers of SUMO are involved in a collaboration on the verification of real-time systems with the "Information and Network Systems" Team (INSv) led by David Mentré of the "Communication & Information Systems (CIS)" Division of MERCE Rennes). The members of the team at MERCE work on different aspects of formal verification. Currently the SUMO team and MERCE jointly supervise a Cifre PhD student (Emily Clement) funded by MERCE since fall 2018; the thesis is about robustness of reachability in timed automata and will be defended in the beginning of 2022. Moreover we collaborate with Reiya Noguchi, a young engineer, who was member of MERCE, on leave of a Japanese operational division of Mitsubishi and hosted by the SUMO team one day per week since the beginning of 2019; Reiya returned in Japan this year but we continue the collaboration with him. We work with him and Merce on the consistency of timed requirements.

SUMO takes part in I/O Lab, the common lab of Orange Labs and Inria, dedicated to the design and management of Software Defined Networks. Our activities concern the diagnosis of malfunctions in virtualized multi-tenant networks.

The IPSCO project aims to develop a new customer support platform for digital companies and public services. Both by setting up intelligent mechanisms for filtering and processing requests from the public (customers and partners) and by providing a reflective vision of the processes implemented in the responses to these requests. In addition, to provide a robust response to small teams, the solution will enable the effective management of expert user communities to foster their autonomy and the emergence of best practices.

ANR TickTac: Efficient Techniques for Verification and Synthesis of Real-Time Systems (2019-2023)

The aim of TickTac is to develop novel algorithms for the verification and synthesis of real-time systems using the timed automata formalism. One of the project's objectives is to develop an open-source and configurable model checker which will allow the community to compare algorithms. The algorithms and the tool will be used on a motion planning case study for robotics.

ANR HeadWork: Human-Centric Data-oriented WORKflows
(2016-2022)

The objective of this project is to develop techniques to facilite development, deployment, and monitoring of crowd-based participative applications. This requires handling complex workflows with multiple participants, incertainty in data collections, incentives, skills of contributors, ... To overcome these challenges, Headwork will define rich workflows with multiple participants, data and knowledge models to capture various kind of crowd applications with complex data acquisition tasks and human specificities. We will also address methods for deploying, verifying, optimizing, but also monitoring and adapting crowd-based workflow executions at run time.

ANR MAVeriQ: Methods of Analysis for Verification of Quantitative properties (2021-2025)

The objective of this project is to develop unified frameworks for quantitative verification of timed, hybrid, and stochastic systems. We believe such a unification is possible because common patterns are used in many cases. The project targets in particular:

The aim of MAVeriQ is to progress towards this unification, by gathering skills on timed and stochastic systems and on quantitative verification under a common roof, to jointly address open challenges in quantitative model-checking and quantitative validation. One such challenge we will address is robustness of quantitative models, that is, resilience to small perturbations, which is crucial for implementability. Unified methods developed in the project (such as robustness analysis and simulation techniques) will be showcased in different case studies in the domain of CPS (in particular automotive control),showing that such a system can be verified in different ways without leaving this framework.

The team collaborates with the following researchers:

All members of the team regularly write reviews for the main conferences in our areas of expertise (LICS, ICALP, CAV, Concur, FTTCS, STACS, FoSSaCS, RV, WoDES, CDC, ...).

Nathalie Bertrand: OPODIS'21, December 2021.
Distributed algorithms: a challenging playground for model checking.

Post-Doc

PhD Students.

Master Students.

Past Master Students.

Current Master Students.

Bachelor Students.

Several members of the team took part in the organization of “J'peux pas, j'ai informatique”, a 1-day event for maths and computer-science teachers in secondary schools and high schools, about gender stereotypes in computer science.