RESEARCH CENTRE

Inria Centre
at Université Grenoble Alpes

2023
ACTIVITY REPORT

IN PARTNERSHIP WITH:

Université de Grenoble Alpes

Project-Team

CTRL-A

Control for safe Autonomic computing
systems

IN COLLABORATION WITH: Laboratoire d’Informatique de Grenoble (LIG)

DOMAIN

Networks, Systems and Services,
Distributed Computing

THEME

Distributed Systems and middleware

Contents

Project-Team CTRL-A

1 Team members, visitors, external collaborators

2 Overall objectives

3 Research program

4 Application domains

5 Social and environmental responsibility

5.1 Footprint of research activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2 Impact of research results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6 Highlights of the year

6.1 Events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7 New software, platforms, open data

7.1 New software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.1 Heptagon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.2 New platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

8 New results

8.1 Design support for Control in Autonomic Computing . . . . . . . . . . . . . . . . . . . . . .
8.1.1 Models and control for multiple loops . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.1.2 Software Architectures for multiple loops . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . .
8.1.3 Discrete Control and reactive languages
8.2 Self-adaptative distributed systems in Cloud-Edge and HPC . . . . . . . . . . . . . . . . . .
8.2.1 Sustaining performance while reducing energy consumption with a Control Theory
Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.2.2 RJMS-level dynamical resource harvesting in HPC clusters . . . . . . . . . . . . . . .
8.2.3 Combining Scheduling and Autonomic Computing for Parallel Computing Resource

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Management

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
8.2.4 Self-adaptive Control of Fog/Edge-based Services using Constraint Solving . . . . . 10
8.2.5 Self-adaptive support for Could-Edge Cyber-physical Systems : Smart Grids Use Case 11
8.3 Cyber-Security & Self-protection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
8.3.1 Intrusion detection in industrial control systems . . . . . . . . . . . . . . . . . . . . . 11
8.3.2 Resilience and reaction in Industrial Control Systems
. . . . . . . . . . . . . . . . . . 12
8.3.3 Automated risk analysis, and Embedded program verification . . . . . . . . . . . . . 12

9 Bilateral contracts and grants with industry

12
9.1 Bilateral grants with industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

10 Partnerships and cooperations

13
10.1 International initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
10.1.1 Participation in other International Programs . . . . . . . . . . . . . . . . . . . . . . . 13
10.2 National initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
10.2.1 PEPR Cybersecurity, project SuperviZ . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
10.2.2 PEPR Cloud, project Taranis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
10.2.3 PEPR TASE, project Tasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
10.2.4 ANR RADYAL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

11 Dissemination

15
11.1 Promoting scientific activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
11.1.1 Scientific events: organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
11.1.2 Scientific events: selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
11.1.3 Journal
11.1.4 Invited talks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
11.1.5 Research administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
11.2 Teaching - Supervision - Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
11.2.1 Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
11.2.2 Supervision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
11.2.3 Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

12 Scientific production

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12.1 Major publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
12.2 Publications of the year
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
12.3 Cited publications

Project CTRL-A

1

Project-Team CTRL-A

Creation of the Project-Team: 2017 June 01

Keywords

Computer sciences and digital sciences

A1.1.2. – Hardware accelerators (GPGPU, FPGA, etc.)

A1.1.4. – High performance computing

A1.1.5. – Exascale

A1.1.9. – Fault tolerant systems

A1.1.10. – Reconfigurable architectures

A1.3. – Distributed Systems

A1.3.5. – Cloud

A1.3.6. – Fog, Edge

A1.4. – Ubiquitous Systems

A1.6. – Green Computing

A2.1.9. – Synchronous languages

A2.2. – Compilation

A2.3.1. – Embedded systems

A2.5.1. – Software Architecture & Design

A2.5.2. – Component-based Design

A2.5.4. – Software Maintenance & Evolution

A2.6.2. – Middleware

A2.6.4. – Ressource management

A4.9.1. – Intrusion detection

A4.9.3. – Reaction to attacks

A6.4. – Automatic control

Other research topics and application domains

B4.5. – Energy consumption

B5.1. – Factory of the future

B6.1. – Software industry

B6.1.1. – Software engineering

B6.1.2. – Software evolution, maintenance

B6.4. – Internet of things

B6.5. – Information systems

B6.6. – Embedded systems

B8.1. – Smart building/home

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Inria Annual Report 2023

1 Team members, visitors, external collaborators

Research Scientist

• Eric Rutten [Team leader, INRIA, Researcher, HDR]

Faculty Members

• Raphaël Bleuse [UGA, Associate Professor]

• Gwenaël Delaval [UGA, Associate Professor]

• Stéphane Mocanu [GRENOBLE INP, Associate Professor, HDR]

Post-Doctoral Fellow

• Ghada Moualla [UGA]

PhD Students

• Quentin Guilloteau [UGA, until Nov 2023]

• Kouds Halitim [INRIA, from Nov 2023]

• Mareva Hotellier [NAVAL GROUP]

• Lea Astrid Kenmogne Mekemte [GRENOBLE-INP, from Nov 2023]

• Jolahn Vaudey [UGA]

Technical Staff

• Jonathan Bleuzen [INRIA, Engineer]

• Nelson Nkawa [GRENOBLE-INP, Engineer, from Mar 2023 until Sep 2023]

• Nelson Nkawa [INRIA, Engineer, until Feb 2023]

Interns and Apprentices

• Lilian Gardon [INRIA, Intern, from May 2023 until Jul 2023]

• Rosa Pagano [INRIA, Intern, from Mar 2023 until Aug 2023]

Administrative Assistant

• Maria Immaculada Presseguer [INRIA]

External Collaborator

• Bogdan Robu [UGA, Gipsa-lab]

Project CTRL-A

3

2 Overall objectives

Objective: control support for autonomic computing

CTRL-A is motivated by the observation that computing systems, large (data centers) or small (embed-
ded), are more and more required to be adaptive to the dynamical fluctuations of their environments
and workloads, evolutions of their computing infrastructures (mobile, shared, or subject to faults), or
changes in application modes and functionalities. Their administration, traditionally managed by human
system administrators, needs to be automated in order to be efficient, safe and responsive. Autonomic
Computing [27] is the approach that emerged in the early 2000’s in distributed systems to answer that
challenge, in the form of feedback loops for self-administration control. These loops address object-
ives like self-configuration (e.g. in service-oriented systems), self-optimization (resource consumption
management e.g., energy), self-healing (fault-tolerance, resilience), self-protection (security and privacy).
Therefore, there is a pressing and increasing demand for methods and tools to design controllers for
self-adaptive computing systems, that ensure quality and safety of the behavior of the controlled system.
The critical importance of the quality of control on performance and safety in automated systems, in
computing as elsewhere, calls for a departure from traditional approaches relying on ad hoc techniques,
often empirical, unsafe and application-specific solutions.

The main objective of the CTRL-A project-team is to develop a novel framework for model-based
design of controllers in Autonomic Computing, exploiting techniques from Control Theory [26], par-
ticularly Discrete Event Systems [31], but also other forms. We want to contribute generic Software
Engineering methods and tools for developers to design appropriate controllers for their particular
reconfigurable architectures, software or hardware, and integrate them at middleware level. We want to
improve concrete usability of techniques from Control Theory by specialists of computing systems [7],
and to provide tool support for our methods in the form of specification languages and compilers, as well
as software architectures.

We address policies for self-configuration, self-optimization (resource management, low power),

self-healing (fault tolerance) and self-protection (security).

3 Research program

Modeling and control techniques for autonomic computing

Our research activity is mainly targeted at models and architectures, with also a notable part devoted
to applications and case studies, in co-operations with specialists of the application domains, either
academic researchers (e.g. in HPC) or industrial partners (e.g., CEA, Orange labs, in IoT). We adopt a
strategy of parallel investigation of, on the one hand, generic models and tools for the design support
for control in Autonomic Computing, and, on the other hand, experimental identification of needs and
validation of proposals. Therefore we have activities related to several application domains, for each of
which we build co-operations with specialists, for example middleware platforms for Cloud systems [3],
HPC architectures (e.g., multi-core [11]), Dynamic Partial Reconfiguration in FPGA-based hardware [6]
and the IoT and smart environments [8].

The main objective of CTRL-A translates into a number of scientific axes :

1. Design support for Control in Autonomic Computing : under the angle of Models and control
(e.g., Discrete Event Systems and reactive languages), or at the level of Software Components and
Architectures (e.g., for separation of concerns, coordination of multiple autonomic managers :
Control, ML, RJMS, or application/infrastructure-levels);

2. Self-adaptative distributed systems and Cloud-Edge/HPC : e.g., RJMS-level dynamical resource
harvesting in HPC clusters ; node-level energy management through RAPL ; reproducibility of
experimental validation.

3. CyberSecurity & Self-protection in Industrial Control Systems : intrusion detection ; automated risk
analysis ; validation of conformity to IEC 62443 standard; self-protection, resilience and reaction by
self-reconfiguration ; applications to Smart-Grid infrastructures ; experimental lab.

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Inria Annual Report 2023

Achieving the goals of CTRL-A requires multidisciplinarity and expertise from several domains. The
expertise in Autonomic Computing and programming languages is covered internally by members of
the CTRL-A team. On the side of theoretical aspects of control, we have active external collaborations
with researchers specialized in Control Theory, in the domain of Discrete Event Systems as well as in
classical, continuous control. Additionally, an important requirement for our research to have impact is
to have access to concrete, real-world computing systems requiring reconfiguration control. We target
autonomic computing at different scales, in embedded systems or in cloud infrastructures, which are
traditionally different domains. This is addressed by external collaborations, with experts in either
hardware or software platforms, who are generally missing our competences on model-based control of
reconfigurations.

4 Application domains

We attack the problem of designing well-regulated and efficient self-adaptive computing systems by the
development of novel strategies for their runtime management. Therefore the kind of application domains
that we typically target involve computing systems with relatively coarse-grain computation tasks (e.g.
image processing or HPC tasks, components or services, control functions in Industrial Control Systems).
They must be run on distributed heterogeneous architectures. Runtime, unpredictable variations can
come from the environment (e.g., data values, user inputs, physical sensors), the application (e.g.,
functional modes depending on algorithm progress, computation phases, or business processes), or the
infrastructure (e.g., resource overload, faults, temperature variations, communication network variations,
cyber-attacks).

The general control problem then consists of deciding at runtime the choice of which implementation
or version of tasks to dynamically deploy or redeploy on which computing resources, in order to enforce
high-level strategies involving objectives in terms of constraints, optimization, logical invariance or
reachability. The design of such controllers involves the design of appropriate sensors and actuators in
the computing infrastructures. It is based on the use of modeling and decision formalisms of different
kinds according to the application characteristics.

The objectives of CTRL-A are achieved and evaluated in both of our main application domains,

thereby exhibiting their similarities from the point of view of reconfiguration control.

Self-adaptive and reconfigurable computing systems, in Cloud-Edge and HPC

One main application domain for the research of CTRL-A concerns Cloud-Edge and High-Performance
Computing. In these contexts, tasks can be achieved following a choice of implementations or versions,
such as in, e.g., service oriented approaches. Each implementation has its own characteristics and
requirements, e.g., w.r.t. resources consumed and QoS offered. The systems execution infrastructures
present heterogeneity, with different computing processors, a variety of peripheral devices (e.g., I/O,
video port, accelerators), and different means of communications. This hardware or middleware level
also presents adaptation potential, e.g., in varying quantities of resources or sleep and stand-by modes.

The kinds of control problems encountered in these self-adaptive systems concern the navigation in
the configurations space defined by choice points at the levels of applications, tasks, and architecture.
The pace of control is more sporadic, and slower than the instruction-level computation performance
inside the large-grain tasks.

In this application area, we currently focus especially on the runtime management of resources for
energy objectives and digital soberness, e.g. at the level of a data-center by dynamically harvesting
unused resources, or at node level by dynamically adjusting frequency under QoS constraints. Ongoing
or recent cooperations in the application domain feature Qarnot Computing (challenge Inria PULSE),
Orange labs, Nokia, Argonne National Laboratories (USA) (JLESC).

Project CTRL-A

5

Industrial Control Systems, w.r.t. their cybersecurity and Cloud-Edge self-adaptive
virtualization

Another general application domain to confront our approaches and models is Industrial Control Systems
(ICS), which can be seen as a form of Cyber-Physical Systems (CPS) and IoT, more specifically Industry 4.0
related infrastructures, like SCADA. In this application domain we particularly focus on Cyber-Security
problems, considered at the operational level, in terms of Intrusion Detection Systems (IDS), as well as
reaction to attacks, in the form of self-adaptive resilience and self-protection. In a context of evolution
of technologies in ICS, namely their softwarization and virtualization, we also apply our approaches of
the Cloud-Edge application domain, e.g. in virtualized control of Smart Grids. The adaptation problems
concern both the functional aspects of the applications, and the middleware support deployment and
reconfiguration issues.

Ongoing or recent cooperations in the application domain feature Naval Group, CEA, and RTE (the

French energy transportion company).

5 Social and environmental responsibility

5.1 Footprint of research activities

In the year 2023, we are still trying to moderate the travels of the team, and favoring submissions and
publications in journals.

Our activities in energy-efficient management of computing infrastructures involve running experi-
ments on large computing infrastructures e.g., using Grid 5000, where we spend approximatively 290k
core·hours of computing.

5.2 Impact of research results

We have research activities w.r.t. energy efficiency in computing systems, at the levels of nodes (RAPL)
as well as at the higher level of grids (RJMS, CiGri), which are contributing to a better mastered energy
consumption in computing.

On a longer term, we orient our research towards topics explicitely targeting environmental as well
as social impacts, ìn the form of user involvement through usage choices. In line with our topic of
autonomic management, self-adaptive systems and their control, for example, we consider control
objectives involving trade-offs between performance or QoS and economy of resources and impact, so
that users can choose a level of sobriety, and possibly limited or degraded quality, thereby allowing for
potential resource and energy savings. Our starting cooperation with Qarnot Computing has a potential
for involving not only technical considerations but also societal and regulatory constraints, or user and
customer choices.

The perspectives involve the notion of computing within limits, especially when they are varying
dynamically, and which can be undergone (e.g., resilience when submitted to cyber-attacks or faults) or
chosen (e.g., accepting lower quality outside of phases requiring higher levels due to urgency).

6 Highlights of the year

6.1 Events

Eric Rutten is co-editor, with Sophie Cerf from the Spirals team at Inria Lille and Alessandro Papadopoulos
from Mälardalen University (Sweden), for the ACM Transactions on Autonomous and Adaptive Systems
(TAAS) special issue on Control for Computing Systems.

Quentin Guilloteau and colleagues designed and proposed a tutorial on Control for Computing,
targeted at an audience of Computer Scientists with no background in Control Theory (which is the
general case), and made available online : tutorial. This tutorial has been proposed to the public at five
occasions in 2023 : sessions.

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Inria Annual Report 2023

7 New software, platforms, open data

7.1 New software

7.1.1 Heptagon

Keywords: Compilers, Synchronous Language, Controller synthesis

Functional Description: Heptagon is an experimental language for the implementation of embedded
real-time reactive systems. It is developed inside the Synchronics large-scale initiative, in col-
laboration with Inria Rhones-Alpes. It is essentially a subset of Lucid Synchrone, without type
inference, type polymorphism and higher-order. It is thus a Lustre-like language extended with
hierchical automata in a form very close to SCADE 6. The intention for making this new language
and compiler is to develop new aggressive optimization techniques for sequential C code and
compilation methods for generating parallel code for different platforms. This explains much of
the simplifications we have made in order to ease the development of compilation techniques.

The current version of the compiler includes the following features: - Inclusion of discrete controller
synthesis within the compilation: the language is equipped with a behavioral contract mechanisms,
where assumptions can be described, as well as an "enforce" property part. The semantics of this
latter is that the property should be enforced by controlling the behaviour of the node equipped
with the contract. This property will be enforced by an automatically built controller, which will act
on free controllable variables given by the programmer. This extension has been named BZR in
previous works. - Expression and compilation of array values with modular memory optimization.
The language allows the expression and operations on arrays (access, modification, iterators). With
the use of location annotations, the programmer can avoid unnecessary array copies.

URL: https://gitlab.inria.fr/synchrone/heptagon

Contact: Gwenaël Delaval

Participants: Adrien Guatto, Brice Gelineau, Cédric Pasteur, Eric Rutten, Gwenaël Delaval, Léonard

Gérard, Marc Pouzet

Partners: UGA, ENS Paris, Inria, LIG

7.2 New platforms

Participants:

Stéphane Mocanu.

Hardware-in-the-loop simulation software Web site: G-ICS. Self-assessment:

• Software Family

– utility: Utility, (see SAE, Section 3.4).

• Audience:

– universe: wide-audience software (aims to be usable by a wide public, to become the

reference software in its area, etc.).

• Evolution and maintenance:

– lts: long term support.

• Duration of the Development (Duration): years

• Description : The embedded software on the electronic boards of the G-ICS HIL systems. The
electronic board schematics as well as the PCB, embedded software, communication protocol
specification and software interfaces with various simulators are provided in open source
[30].

Project CTRL-A

8 New results

8.1 Design support for Control in Autonomic Computing

8.1.1 Models and control for multiple loops

Participants:

Raphaël Bleuse, Eric Rutten.

7

We work on the general notion of Software Engineering for designing controllers for Self-Adaptive
Systems, and particularly the potential contribution of Control Theory to provide for Assurances in Self-
Adaptive Software Systems (book chapter [7]). We propose to consider feedback control as a behavioural
model-based instanciation of the MAPE-K loop in Autonomic Computing (book chapter [10]). We
consider that complex systems can require multiple loops, motivated by the fact that different sub-
problems can require combinations of different decision and control techniques.

One particularly interesting topic is the combination of Control and Machine Learning. In our team
we propose to address them by considering their composition (particularly Reinforcement and Neural
Networks) with controllers based on Control Theory (particularly deterministic), in order to maintain
guarantees on the behaviors of the managed system. As a result we performed a survey of the state of the
art in interactions between RL and deterministic control, some of them classic, others less explored [13].
This work is done in cooperation with the Spirals team at Inria Lille : Sophie Cerf.

Another case of high potential is to consider the combination of Control and Scheduling. In the
context of resource harvesting in HPC (see Section 8.2.3), we start considering the coordination of a
controller regulating the injection of best-effort jobs with the OAR scheduler in the RJMS (Resource and
Jobs Management System) of CiGri. This topic was presented at JLESC 2023 - 15th Workshop of the Joint
Laboratory for Extreme Scale Computing [18], and has been the object of one chapter of the PhD of
QuentinGuilloteau [20]. (see Section 8.2.2).

This work is done in cooperation with the Spirals team at Inria Lille : Sophie Cerf, the Datamove team

at Inria Grenoble : Olivier Richard, and with Gipsa-lab in Grenoble : Bogdan Robu.

8.1.2 Software Architectures for multiple loops

Participants:

Eric Rutten.

We study the question of multiple loops coordination also from the point of view of Software Archi-
tectures, generalizing from the similarities and recurring patterns appearing in use-cases. In the past
we have worked in the framework of software components-based approaches (JSS [1],TSE [3]) involving
proposals for modularity and hierarchy of autonomic discrete controllers. In another series of works,
targeting the self-adaptation of reconfigurable hardware, namely DPR FPGA (TECS [2]), we considered
the management of a combination of mission-level and computing platform-level objectives (CBSE14
[6]). In other, more applicative work (ICCAC17 [33]) related to a rule-based middleware (COORD17, [8]) ,
we proposed a design framework for reliable multiple Autonomic Loops, motivated by the management
of different functionalities, at different levels of the system, and/or with different decision models. Part of
the ideas emerging from that work was followed upon in the different context of Cyber-Physical Systems
and the CPS4EU project, where we explore software architectures for self-adaptative middleware support
for IoT and CPS. We proposed the separation of concerns between self-adaptation at the different levels of
applications or functionality on the one side, and infrastructure and resources on the other side (ECSA20,
HICSS22 [9, 34]).

Recent developments were performed with application to a use case in smart grids, provided by a

cooperation with RTE (see Section 8.2.5) [14] (a journal paper is under submission on this topic).

Further developments of these ideas are ongoing, in relation to the notion of computing within
limits, where dynamical changes of the limits are reacted unpon by reconfigurations at both levels of

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Inria Annual Report 2023

redeployment on the current architecture and of reconfigurztion of the application (e.g. in a degraded
mode). This research direction is going to be explored further in the context of different projects :

• the Tasting (see Section 10.2.3), in cooperation with RTE for the applications in smart grid control

functions management ;

• the RADYAL project (see Section 10.2.4) where reconfiguration targets both Machine Learning
applications (acting on algorithms parameters) and hardware architectures (acting on variables
precision) ;

• further, in the Taranis project (see Section 10.2.2), in the context of Cloud and Edge computing

systems.

8.1.3 Discrete Control and reactive languages

Participants: Gwenaël Delaval, Jolahn Vaudey, Stéphane Mocanu, Eric Rutten.

Our work in reactive programming for autonomic computing systems is focused on the specification
and compilation of declarative control objectives, under the form of contracts, enforced upon classical
mode automata as defined in synchronous languages. The compilation involves a phase of Discrete
Controller Synthesis, integrating the tool ReaX, in order to obtain an imperative executable code. The
programming language Heptagon / BZR (see Section 7.1.1 ) integrates our research results [5].

Recent work concerns a methodology for the evaluation of controllers. We are considering that Dis-
crete Controller Synthesis produces results that are correct by construction w.r.t. the formal specification,
but in practice there remains to evaluate the obtained controller quantitatively, to check e.g., whether it
is not overconstrained, and effectively producing the expected impact on the overall system behavior.
We consider our work on self-protection (see Section 8.3.2) as a use case, evaluating the improvement of
resilience of a system in the presence of attacks.

We used Heptagon/BZR as a simulation tool, to compare a program embedding a synthesized control-
ler, with a similar program either without controller, or with a simple controller programmed manually,
without use of discrete controller synthesis. The environment (alarms from an intrusion detection sys-
tem) has been modeled also in Heptagon/BZR as a Markov chain, that can be simulated with an ad hoc
Heptagon library. We then measure several values for each program version: average number of steps
before the system get to a “safe” state (state where one remote processing unit do not work anymore
because of the attacks), evolution in time of the average number of “programs” in “safe” mode. This
evaluation by simulation confirm that the program with the synthesized controller is more efficient
w.r.t. these measurements. In some specific cases, we are also able to compare the values obtained by
simulation, with theoretical optimal values computed from the Markov chain of the environment. A
journal paper is under submission on this topic.

8.2

Self-adaptative distributed systems in Cloud-Edge and HPC

HPC (High-Performance Computing) systems have increasingly become more varying in their behavior,
in particular in aspects such as performance and power consumption, thereby encountering problems
also known in the Cloud, and the fact that they are becoming less predictable demands more runtime,
autonomic management [11]. We explore related issues along the following topics.

8.2.1 Sustaining performance while reducing energy consumption with a Control Theory Approach

Participants:

Raphaël Bleuse, Jonathan Bleuzen, Kouds Halitim, Eric Rutten.

Project CTRL-A

9

We explore a form of trade-off between performance and resource and energy consumption, with the
aim to sustain performance while reducing energy consumption with a Control Theory approach. The
infrastructure is considered at a level close to the hardware, in that we use the RAPL (Running Average
Power Limit) mechanism available in Intel processors. We exploit heterogeneity as an opportunity: as
applications dynamically undergo variations in workload, due to phases or data/compute movement
between devices, one can dynamically adjust power across compute elements to save energy without
impacting performance. With an aim toward an autonomous and dynamic power management strategy
for current and future HPC architectures, we explore the use of control theory for the design of a dynamic
power regulation method, periodically monitoring application progress and choosing at runtime a
suitable power cap for processors. Thanks to a preliminary offline identification process, we derive
a model of the dynamics of the system and a proportional-integral (PI) controller. We evaluate our
approach on top of an existing resource management framework, the Argo Node Resource Manager,
deployed on several clusters of Grid’5000, using a standard memory-bound HPC benchmark.

Building upon a methodology and first results (EuroPar21 [4]), we improved the robustness and
reusability of controllers by leveraging adaptive control . New results consist of an approach that incor-
porates cascaded control strategies, such as PI control and MPC (Model Predictive Control), integrated
into the Argo Node Resource Manager framework [25].

A journal paper is under finalization on these topics.
Amongst perspectives, we are considering to use it as a background in our starting research in the

WP5 of challenge Inria-Qarnot Computing "PULSE" (see Section 9.1).

This work is done in cooperation with the Spirals team at Inria Lille : Sophie Cerf, with whom we

co-advised the MSc internship of Kouds Halitim in the Spirals team at Inria Lille [25].

This work is also done in cooperation with Swann Perarnau (Argonne National Lab., Chicago, IL) in

the framework of the JLESC : Joint Laboratory on Extreme Scale Computing (see Section 10.1.1 ).

8.2.2 RJMS-level dynamical resource harvesting in HPC clusters

Participants:

Raphaël Bleuse, Jonathan Bleuzen, Quentin Guilloteau, Rosa Pagano,
Bogdan Robu, Eric Rutten.

This resource harvesting problem is found in the context of CiGri, a simple, lightweight, scalable
and fault tolerant grid system which exploits the unused resources of a set of computing clusters. CiGri
harvests and exploits the unused resources of a set of computing clusters, by injecting best-effort jobs
on top of the prioritary applications. We consider autonomic administration for scientific workflows
management through a control theoretical approach for maximizing usage while avoiding overload.

We propose a model described by parameters related to the key aspects of the infrastructure thus
achieving a deterministic dynamical representation that covers the diverse and time-varying behaviors
of the real computing system. We studied simple forms of PI control, as well as adaptive control and
an extension with model free control. We first considered essentially the performance of harvesting
itself, then integrated the problem of Distributed File Server load, that can heavily disturb prioritary
applications. This approach was also the topic of the Master’s thesis in Control Theory of Rosa Pagano
[23]. We performed a comparative study with regard to the reusability of controllers when deployed on
varying target platforms, or subjected to varying load patterns. A journal paper is under submission on
this topic.

Another result of this activity is the design and implementation of a tutorial on Control for Computing,
targeted at an audience of Computer Scientists with no background in Control Theory (which is the
general case), and made available online : tutorial. This tutorial has been proposed to the public at several
occasions : sessions.

We put an emphasis on reproducibility of experiments, for which new methodologicalresults have
been obtained (COMPAS23 [17]), as well as on frugality with the reduction of the cost of these experiments,
with results in Simulating a Multi-Layered Grid Middleware [21], and Folding a Cluster containing a
Distributed File-System [22].

This work is the object of the PhD of Quentin Guilloteau [20].

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Inria Annual Report 2023

This work is done in cooperation with the Datamove team of Inria/LIG (O. Richard), and Gipsa-lab (B.
Robu), and it is the topic of the PhD thesis in Computer Science of Quentin Guilloteau. This work is also
done in cooperation with the Spirals team at Inria Lille : Sophie Cerf.

8.2.3 Combining Scheduling and Autonomic Computing for Parallel Computing Resource Manage-

ment

Participants: Raphaël Bleuse, Lilian Gardon, Quentin Guilloteau, Bogdan Robu,

Eric Rutten.

This research topic aims at studying the relationships between scheduling and autonomic computing
techniques to manage resources for parallel computing platforms. The performance of such platforms
has greatly improved (149 petaflops as of November 2019 [32]) at the cost of a greater complexity: the
platforms now contain several millions of computing units. While these computation units are diverse,
one has to consider other constraints such as the amount of free memory, the available bandwidth, or the
energetic envelope. The variety of resources to manage builds complexity up on its own. For example,
the performance of the platforms depends on the sequencing of the operations, the structure (or lack
thereof ) of the processed data, or the combination of application running simultaneously.

Scheduling techniques offer great tools to study/guaranty performances of the platforms, but they
often rely on complex modeling of the platforms. They furthermore face scaling difficulties to match the
complexity of new platforms. Autonomic computing manages the platform during runtime (on-line) in
order to respond to the variability. This approach is structured around the concept of feedback loops.
The scheduling community has studied techniques relying on autonomic notions, but it has failed to link
the notions up.

We are starting to address this topic at the general level of a state of the art of relations between the
two domains, and also at the more concrete and specific level of a real-world use-case, in the context of
CiGri as above. Indeed this context features a RJMS (Resources and Jobs Management System) involving
the OAR scheduler. Therefore we are identifying coordination with the previously described controller
and OAR, in particular in such way that OAR is able to notify the controller of upcoming rises or falls of
activity in prioritary tasks, and we are exploring how this information can be exploited by the controller,
by adopting for example a Feed Forward approach.

This work is done in cooperation with the Datamove team of Inria/LIG (O. Richard), and Gipsa-lab (B.

Robu), and it is in the topic of the PhD thesis in Computer Science of Quentin Guilloteau [20].

Amongst perspectives of this topic, we are considering to use it as a background in our starting

research in the WP6 of defi Inria-Qarnot Computing "PULSE" (see Section 9.1).

8.2.4 Self-adaptive Control of Fog/Edge-based Services using Constraint Solving

Participants: Ghada Moualla, Eric Rutten.

The Fog/Edge computing model has become a popular approach for supporting user services, namely
IoT applications. However, automated resource provisioning is needed to cope with workload changes
on the fly while meeting service-level agreements. Autonomous computing offers a self-management
approach that reduces system complexity and facilitates intuitive service delivery for operators and users.
In this topic, we propose an elastic infrastructure solution that leverages adaptive features to handle
changing service conditions, such as workload spikes to prevent performance degradation. Our solution
integrates a CPLEX-optimized constraint-based model into an autonomous control loop to react to
environmental changes and improve the efficiency and agility of the system. We are currently studying
the integration to constraints models of dynamical aspect, so that speed or acceleration of variations in
the system can be taken into account in the reaction.

A perspective of this topic is to use it as a background in our starting research in the Tasting project of

PEPR TASE (see Section 10.2.3).

Project CTRL-A

11

8.2.5 Self-adaptive support for Could-Edge Cyber-physical Systems : Smart Grids Use Case

Participants:

Stéphane Mocanu, Eric Rutten.

In this work we consider self-adaptation at the level of Software Architectures, targeted at the domain
of Cyber-Physical Systems where Cloud-Edge infrastructures are being adopted in application domains
like Smart Grids. This activity took place in the framework of the H2020 project CPS4EU.

As an applicative use-case of our Software Architectures approach from Section 8.1.2, we con-
sider Smart Grid management (HICSS’22 [34]). We consider self-adaptive security in such Cloud-Edge
infrastructures-based CPS. Security risk assessment is an important challenge in the design of Cyber Phys-
ical Systems (CPS). Even more importantly, the intrinsically dynamical nature of these systems, due to
changes in their environment, as well as evolutions in their infrastructures, makes them self-adaptive sys-
tems, where security aspects have to be considered in terms of management of detections and reactions
for self-protection. In this work, we propose an approach to autonomously mitigate the threats in each
reconfiguration at application or infrastructure levels of CPS. We propose and implement a framework for
self-adaptive security : software architecture, design method, and integration with model-based decision.
We use Attack-Defense Trees for modeling threats, and our approach involves security risk assessment,
taking into account its balancing and coordination with quality of service aspects. We formulate and
formalize the on-line decision problem to be solved at each cycle of the self- adaptation control loop
in terms of Constraint Programming (CP) modeling and resolution. The CP model implements a set
of constraints that allow to specify secure configurations, evaluated regarding their impact on system
performance to pinpoint the most relevant one portraying a good balance between the security and
quality of service. We perform validation of our approach with its application to Smart Grids, more
particularly to an industrial case study from RTE. A journal paper is under submission on this topic.

At a different level, we consider another use-case from RTE, focused on the substation level, under the
angle of questions of resilience, seen under the approach of self-adaptation, and more particularly as self-
protection in response to attacks of the network. The problem is to allocate and reallocate dynamically a
set of control functions upon a distributed computing infrastructure, with self-adaptation to variations
and perturbations. We define and implement the decision model using constraint programming, to
describe the space of possible configurations, as well as the constraints and objectives formalizing the
operators strategies. This model is used in simulation and implementation, calling the constraints solver
at each cycle of the self-adaptation control loop. It offers design assistance and rapid prototyping to
automation designers, to explore choices of solutions in requirements and strategies [14].

A perspective of this topic is to use it as a background in our starting research in the Tasting project of

PEPR TASE (see Section 10.2.3), also in coopération with RTE.

This work is done in cooperation with RTE (the French Energy Transportion company) : Guillaume

Giraud.

8.3 Cyber-Security & Self-protection

CTRL-A team is participating in the PEPR Cybersecurity reseach projetc SuperviZ. Stéphane Mocanu is
the leader of the Platform workpackage of SuperviZ (Section 10.2.1).

8.3.1 Intrusion detection in industrial control systems

Participants: Mike Da Silva, Estelle/Maréva Hotellier, Lea Astrid Kenmogne

Mekemte, Stéphane Mocanu.

First results on process oriented sequential attacks detection were obtained during Oualid Koucham’s

PhD and published recently in [28] together with a general alert correlation framework.

A complete intrusion detection and alert correlation framework was proposed and process oriented
IDS and correlator were synthesised, implemented and available in open-source on-line (see Section 7.2

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Inria Annual Report 2023

and G-ICS). Smart-grid applications on intrusion detection and impact on dependability were presented
in [29].

We further develop the results for distributed and hierarchical systems in the PhD thesis of Estelle
Hotellier. Some first results on the attacks on industrial speed driver controlled via CanOpen were
presented in August 2021 in the local Barbhack Hacking conference.

We recently extended Zeek IDS detection capabilities to CAN networks and the code will soon be

freely available.

A first version was presented in [19] ; a full version appears in [12].

8.3.2 Resilience and reaction in Industrial Control Systems

Participants: Gwenaël Delaval, Stéphane Mocanu, Eric Rutten, Jolahn Vaudey.

As consequences of attacks on Industrial Control Systems may be dramatic, an important topic in
ICS cybersecurity is the improvement of cyber-resilience. Reaction in case of attacks is also a crucial
and sensitive topic. Our approach for both resilience and reaction problems is based on the notion of
self-protection, where self-adaptation takes the form of self-reconfiguration of the architecture. Based on
a first approach developed in the PhD of Kabir-Querrec, and experience on modelling reconfiguration
with DES, we formalized recently the self-protection problems as a DES control problems. A model and a
formulation of the reconfiguration problem was specified in Heptagon/BZR (IFAC World 2020 conference
[24]).

We are currently working on a method to evaluate the effectiveness of the obtained controllers related

to section 8.1.3.

This is the topic of the PhD thesis in Computer Science of Jolahn Vaudey.

8.3.3 Automated risk analysis, and Embedded program verification

Participants: Nelson Nkawa, Mike Da Silva, Stéphane Mocanu.

One of our research topics is in automated risk analysis, with the specification of a DSML dedicated
to the automated analysis of the security of industrial control systems based on their safety properties.
The idea is to extract the devices characteristic and the flow cartography from the configuration files and
enrich the model with the description of the network infrastructure and available security measures.

Based on public vulnerability databases, a STRIDE threat model will be automatically constructed
and a list of suggested measures proposed. An incipient proof of concept of automatic flow cartography
based on configuration files was proposed in the PhD of Maëlle Kabir-Querrec.

Results on extending STRIDE modelling to ICS and automatic generation of attack scenarios where

published in [15, 16].

9 Bilateral contracts and grants with industry

9.1 Bilateral grants with industry

Naval Group

Participants:

Estelle Hotellier, Stéphane Mocanu.

We have a cooperation with Naval Group, around the PhD grant of Estelle Hotellier, on the topic of
intrusion detection in complex Industrial Control Systems (ICSs), as described in Section 8.3.1. We are

Project CTRL-A

13

interested in Process-Aware attacks i.e. attacks that target the physical integrity of systems. We consider
the hybrid nature of ICSs and our methodology applies for event-driven and continuous dynamical
systems. We aim at developing a behavioral network traffic Intrusion Detection System (IDS) based on
the ICS characterization through security properties. To do so, we extract system safety properties from
standards, devices programs or system specifications and synthesize them into security patterns. These
patterns are then monitored by our IDS which is in charge of raising alerts.

CEA

Participants: Mike da Silva, Stéphane Mocanu.

We have a cooperation with CEA, around the PhD grant of Mike da Silva, as described in Section 8.3.3.
This PhD topic objective is to provide an automatic vulnerability extraction from a security oriented ICS
architecture model. Existing modeling languages (SCL for substation and AutomationML for industrial
automation) provide support for controller hardware and network accessible data description but not for
complete data flow and network infrastructure description nor for vulnerabilities and their effects. We
extend existing languages with support for network infrastructure modeling including security controls
and data flow description together with a vulnerability data-base support. We will rely on public CVE
data bases and an extensive study of industrial protocols formal verification including support for high-
availability networks. The results of the automatic architecture model processing is used for threat
modeling, attack scenario construction, attack impact assessment and eventually security controls choice
assistance.

Qarnot computing

Participants:

Raphaël Bleuse, Kouds Halitim, Bogdan Robu, Eric Rutten.

We have a cooperation with Qarnot computing in the framework of the Inria challenge PULSE, with
the support of Ademe, on the topic of pushing carbon-neutral services towards the edge. Particularly, we
are involved in WP5 on the Control of emissions of intensive computation tasks, and WP6, which we are
coordinating, on the efficient hybridation of heterogeneous computing tasks.

RTE

Participants:

Stéphane Mocanu, Eric Rutten.

We have a cooperation with RTE (the French Energy Transportion company) : Guillaume Giraud,
following our recent work in the H2020 CPS4EU project. It is continuing in the new project Tasting
(Section 10.2.3) of the PEPR TASE.

10 Partnerships and cooperations

10.1 International initiatives

10.1.1 Participation in other International Programs

Participants:

Raphaël Bleuse, Eric Rutten.

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Inria Annual Report 2023

We participate in the JLESC, Joint Laboratory for Extreme Scale Computing, with partners INRIA, the
University of Illinois, Argonne National Laboratory, Barcelona Supercomputing Center, Jülich Supercom-
puting Centre and RIKEN AICS. We started a cooperation with Argonne National Labs, in the framework
of a project on improving the performance and energy efficiency of HPC applications using autonomic
computing techniques (see Section 8.2.1).

This work is done in cooperation with the Spirals team at Inria Lille : Sophie Cerf.

10.2 National initiatives

10.2.1 PEPR Cybersecurity, project SuperviZ

Participants: Nelson Nkawa, Stéphane Mocanu, Eric Rutten.

We participate in the PEPR Cybersecurity research project SuperviZ.
Stéphane Mocanu is the leader of the Platform workpackage of SuperviZ.

10.2.2 PEPR Cloud, project Taranis

Participants:

Raphaël Bleuse, Eric Rutten.

In the framework of the PEPR Cloud, Ctrl-A is participating in the project Taranis (Model, Deploy,
Orchestrate, and Optimize Cloud Applications and Infrastructure), particularly in WP 3 : Orchestration of
services and ressources.

We will cooperate with teams Spirals at Inria Lille and Stack at Inria in Nantes, on topics amongst :
integration of Control and Constraints, and of Control and Scheduling, as model and decision tools in
autonomic managers, integration of temporal aspects in reconfiguration management.

10.2.3 PEPR TASE, project Tasting

Participants:

Stéphane Mocanu, Eric Rutten.

In the framework of the PEPR TASE (Technologies Avancées des Systèmes Énergétiques), Ctrl-A is
participating in the project Tasting (TrAnsformation of the energy SysTem for a better resilience and
flexibility with enhanced digitalization), particularly in :

• WP1 : Infrastructure reliability and security with a post-doc position on : specification of the distrib-
uted architecture and reconfiguration strategy for the communication and control infrastructure

• WP2 : Distributed architectures of cyber-physical systems with a post-doc position on : methods
for attacks detection by events correlation between network traffic observations and logs from
control equipment

• WP3 : Ease deployment on hardware with post-doc positions on :

– model-based control (constraints solving) of self-adaptive deployment of distributed applica-

tions on the Cloud-Edge infrastructures

– reactive infrastructures for rapid protection in case of process perturbation

Project CTRL-A

10.2.4 ANR RADYAL

Participants:

Bogdan Robu, Eric Rutten.

15

Ctrl-A participates in the ANR project (in the ANR call : AI computing hardware architectures and
accelerators in the context of Edge Computing) called Radyal Resource-Aware DYnamically Adaptable
machine Learning, in cooperation with INSA Lyon / LIRIS (Stefan Duffner), the TARAN team, Inria / Irisa,
Rennes (Marcello Traiola), and the MODUS team, UGA / GIPSA-lab, Grenoble (Bogdan Robu).

We will work on the analysis of self-adaptationreconfiguration spaces in the dimensions of Application
(DNN algorithms), environment (applicative aspects e.g., lighting, obstruction in image analysis), and in-
frastructure and implementation configuration and deployment (involving hardware with reconfigurable
precision and mapping).

11 Dissemination

Participants:

Raphaël Bleuse, Gwenaël Delaval, Stéphane Mocanu, Eric Rutten.

11.1 Promoting scientific activities

11.1.1 Scientific events: organisation

Member of the organizing committees Stéphane Mocanu is participating in the steering committee of
RESSI (Rendez-Vous de la Recherche et de l’Enseignement de la Sécurité des Systèmes d’Information)
Ressi.

Eric Rutten is participating in the steering committee of FETCH (École d’hiver Francophone sur les
Technologies de Conception des Systèmes Embarqués Hétérogènes) the Winter School on Heterogeneous
Embedded Systems Design Technologies, for the 2023 and 2024 editions Fetch.

11.1.2 Scientific events: selection

Member of the conference program committees Raphaël Bleuse is PC member for IPDPS 2023.

Eric Rutten is IEEE Control Systems Society (CSS) Associate Editor, Technology Conference Editorial
Board Technology Conferences Editorial Board (TCEB), and PC member for CCTA 23 and CCTA 24. Eric
Rutten is PC member for CPS& IoT 23, PECS 22, CoDIT 23, WODES 24 ; as well as ASMECC Workshop at
ACSOS 2023, PECS Workshop at Euro-PAR 2023, and MSR 23.

Reviewer Eric Rutten and Gwenaël Delaval are reviewers for Ifac World 23.

Raphaël Bleuse is reviewer for CoDIT. 23.

11.1.3 Journal

Member of the editorial boards Eric Rutten is co-editor for the ACM Transactions on Autonomous and
Adaptive Systems (TAAS) special issue on Control for Computing Systems.

Reviewer - reviewing activities Stéphane Mocanu is reviewer for IEEE Communications Magazine, and
for Electronic Research Archive.

Eric Rutten is reviewer for FGCS.

11.1.4 Invited talks

Eric Rutten has been invited at the Velvet Days, of the GDR GPL and the GT YODA, on deployment,
reconfiguration, adaptation and DevOps in Nantes, 13-14 dec. 2023.

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Inria Annual Report 2023

11.1.5 Research administration

Raphaël Bleuse is member of the team organizing the LIG keynotes.

Gwenaël Delaval is elected member at the Academic Council (Conseil Académique) of University

Grenoble Alpes (UGA) for the Confédération Générale du Travail trade union.

Eric Rutten is a named member of the Scientific Board (Bureau Scientifique) of LIG (Lig). He co-

organised the LIG workshop of axes WAX.

Eric Rutten has a mission as Correspondent for Scientific Relations between Inria Grenoble and CEA

until june 2023.

11.2 Teaching - Supervision - Juries

11.2.1 Teaching

• R. Bleuse; computer architecture; 42h tutorials/practicals; L1; Univ.@ Grenoble Alpes

• R. Bleuse; network architecture; 34h; L1; Univ.@ Grenoble Alpes

• R. Bleuse; C language; 14h lectures/practicals; L2; Univ.@ Grenoble Alpes

• R. Bleuse; application development and deployment; 30h; L2; Univ.@ Grenoble Alpes

• R. Bleuse; network architecture; 10h tutorials/practicals; L2; Univ.@ Grenoble Alpes

• R. Bleuse; advanced virtualization; 35h lectures/tutorials/practicals; L3; Univ.@ Grenoble Alpes

• R. Bleuse; continuous integration; 28h lectures/tutorials/practicals; L3; Univ.@ Grenoble Alpes

• R. Bleuse; software development automation (DevOps); 40h lectures/tutorials/practicals; L3; Univ.@

Grenoble Alpes

• Licence : G. Delaval, Bases du développement logiciel, modularité et tests, 15h lecture/tutorials,

15h lab, L2, Univ. Grenoble Alpes

• Licence : G. Delaval, Algorithmique et programmation impérative, 16h30 lab, L2, Univ. Grenoble

Alpes

• Master : G. Delaval, Compilation project, 4 weeks software project tutoring, M1, Univ. Grenoble

Alpes

• Master : S. Mocanu, Computer Networks and Cybersecurity, 16h class, 34h lab, M1, Grenoble-

INP/ENSE3

• Master : S. Mocanu, Industriel Computer Networks, 8h class, 8h lab, niveau (M1, M2), M2, Grenoble-

INP/ENSE3

• Master : S. Mocanu, Reliability, 10h class, 8h lab, M2, Grenoble-INP/ENSE3

• Master : S. Mocanu, Intrusion Detection and Defense in Depth labs, niveau M2, Grenoble-ENSE3/ENSIMAG

11.2.2 Supervision

• PhD in progress : Estelle Hotelier (CIFRE grant) ;Intrusion Detection in Complex Hybrid Industrial
Systems, started April 2021; co-advised by Stéphane Mocanu with Franck Sicard and Julien Francq
(Naval Group).

• PhD in progress: Mike Da Silva (CEA grant) ; Automated Risk Analysis for Industrial Systems, started
October 2021; co-advised by Stéphane Mocanu with Maxime Puys and Pierre-Henri Thevenon
(CEA-Leti).

Project CTRL-A

17

• PhD completed : Quentin Guilloteau (UGA) ; An autonomic approach to the dynamic management
of recources in HPC clusters ; started oct. 20 ; defended dec. 23 ; co-advised by Eric Rutten with O.
Richard, Datamove team Inria/LIG.

• PhD in progress : Jolahn Vaudey (UGA),Self-reconfiguration of industrial systems applied to cyber-
resilience ; started Oct. 2022 ; co-advised by Stéphane Mocanu, Gwenaël Delaval, Eric Rutten.

• PhD in progress : Kouds Halitim (Inria), Efficicient task hybridization in heterogeneous computing:
practical combinations of Control and Scheduling theories ; started Nov. 2023 ; co-advised by
Raphaël Bleuse, Eric Rutten, Bogdan Robu.

• PhD in progress : Lea Astrid Kenmogne Mekemte (UGA), Explainable IA for Network Intrusion

Detection in Industrial Control Systems ; started Nov. 2023 ; advised by Stéphane Mocanu.

11.2.3 Juries

Eric Rutten is member of the PhD dissertation committee of Charilaos Skandylas, Linnaeus University,
Sweden, August 18th 2023 : Design and Analysis of Self-Protection: Adaptive Security for Software-
Intensive Systems.

Eric Rutten is member of the upcoming (2024) PhD dissertation committee of Jeroen Verbakel,

Eindhoven University of Technology, The Netherlands.

Eric Rutten is member of the CSI for the PhD of Paul DAOUDI (co-advised by Christophe Prieur and

Bogdan Robu, Gipsa-lab).

12 Scientific production

12.1 Major publications

[1] F. Alvares, E. Rutten and L. Seinturier. ‘A Domain-specific Language for The Control of Self-adaptive
Component-based Architecture’. In: Journal of Systems and Software (Jan. 2017). URL: https://ha
l.archives-ouvertes.fr/hal-01450517.

[2] X. An, E. Rutten, J.-P. Diguet and A. Gamatié. ‘Model-based design of correct controllers for dynam-
ically reconfigurable architectures’. In: ACM Transactions on Embedded Computing Systems (TECS)
15.3 (Feb. 2016). URL: https://hal.inria.fr/hal-01272077.

[3] N. Berthier, E. Rutten, N. De Palma and S. M.-K. Gueye. ‘Designing Autonomic Management
Systems by using Reactive Control Techniques’. In: IEEE Transactions on Software Engineering 42.7
(July 2016), p. 18. URL: https://hal.inria.fr/hal-01242853.

[4]

S. Cerf, R. Bleuse, V. Reis, S. Perarnau and E. Rutten. ‘Sustaining Performance While Reducing Energy
Consumption: A Control Theory Approach’. In: Lecture Notes in Computer Science. EURO-PAR
2021 - 27th International European Conference on Parallel and Distributed Computing. Vol. 12820.
Euro-Par. Lisbon, Portugal: Springer, 1st Sept. 2021, pp. 334–349. DOI: 10.1007/978-3-030-8566
5-6_21. URL: https://hal.inria.fr/hal-03259316.

[5] G. Delaval, E. Rutten and H. Marchand. ‘Integrating Discrete Controller Synthesis in a Reactive
Programming Language Compiler’. In: journal of Discrete Event Dynamic System, jDEDS, special
issue on Modeling of Reactive Systems 23.4 (2013), pp. 385–418. URL: http://dx.doi.org/10.100
7/s10626-013-0163-5.

[6] S. M.-K. Gueye, G. Delaval, E. Rutten, D. Heller and J.-P. Diguet. ‘A Domain-specific Language
for Autonomic Managers in FPGA Reconfigurable Architectures’. In: ICAC 2018 - 15th IEEE In-
ternational Conference on Autonomic Computing. Trento, Italy: IEEE, Sept. 2018, pp. 1–10. URL:
https://hal.archives-ouvertes.fr/hal-01868675.

[7] M. Litoiu, M. Shaw, G. Tamura, N. M. Villegas, H. Müller, H. Giese, R. Rouvoy and E. Rutten. ‘What
Can Control Theory Teach Us About Assurances in Self-Adaptive Software Systems?’ In: Software
Engineering for Self-Adaptive Systems 3: Assurances. Ed. by R. de Lemos, D. Garlan, C. Ghezzi and
H. Giese. Vol. 9640. LNCS. Springer, May 2017. URL: https://hal.inria.fr/hal-01281063.

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Inria Annual Report 2023

[8] M. Louvel, F. Pacull, E. Rutten and A. N. Sylla. ‘Development Tools for Rule-Based Coordination
Programming in LINC’. In: 19th International Conference on Coordination Languages and Models
(COORDINATION). Ed. by J.-M. Jacquet and M. Massink. Vol. LNCS-10319. Coordination Models
and Languages. Part 2: Languages and Tools. Neuchâtel, Switzerland: Springer International Pub-
lishing, June 2017, pp. 78–96. DOI: 10.1007/978-3-319-59746-1_5. URL: https://hal-cea.a
rchives-ouvertes.fr/cea-01531019.

[9] M. T. Moghaddam, E. Rutten, P. Lalanda and G. Giraud. ‘IAS: an IoT Architectural Self-adaptation
Framework’. In: ECSA 2020 - 14th European Conference on Software Architecture. L’Aquila, Italy,
14th Sept. 2020, pp. 1–16. URL: https://hal.inria.fr/hal-02900674.

[10] E. Rutten, N. Marchand and D. Simon. ‘Feedback Control as MAPE-K loop in Autonomic Comput-
ing’. In: Software Engineering for Self-Adaptive Systems III. Assurances. Vol. 9640. Lecture Notes
in Computer Science. Springer, Jan. 2018, pp. 349–373. DOI: 10.1007/978-3-319-74183-3_12.
URL: https://hal.inria.fr/hal-01285014.

[11] N. Zhou, G. Delaval, B. Robu, E. Rutten and J.-F. Méhaut. ‘An Autonomic-Computing Approach
on Mapping Threads to Multi-cores for Software Transactional Memory’. In: Concurrency and
Computation: Practice and Experience 30.18 (Sept. 2018), e4506. DOI: 10.1002/cpe.4506. URL:
https://hal.archives-ouvertes.fr/hal-01742690.

12.2 Publications of the year

International journals

[12] E. Hotellier, F. Sicard, J. Francq and S. Mocanu. ‘Standard specification-based intrusion detection
for hierarchical industrial control systems’. In: Information Sciences 659 (Feb. 2024), p. 120102. DOI:
10.1016/j.ins.2024.120102. URL: https://hal.science/hal-04395667.

International peer-reviewed conferences

[13] S. Cerf and E. Rutten. ‘Combining neural networks and control: potentialities, patterns and per-
spectives’. In: Proceedings of The 22nd World Congress of the International Federation of Automatic
Control. IFAC 2023 - 22nd World Congress of the International Federation of Automatic Control.
Yokohama, Japan, July 2023. URL: https://inria.hal.science/hal-04060379.

[14]

S. Chehida, K. Fellah, E. Rutten, G. Giraud and S. Mocanu. ‘Model-based Self-adaptive Management
in a Smart Grid Substation’. In: 2023 IEEE 28th International Conference on Emerging Technologies
and Factory Automation (ETFA. ETFA 2023 - IEEE 28th International Conference on Emerging
Technologies and Factory Automation. Sinaia, Romania: IEEE, 2023, pp. 1–8. URL: https://hal.s
cience/hal-04224198.

[15] M. Da Silva, M. Puys, P.-H. Thevenon and S. Mocanu. ‘PLC Logic-Based Cybersecurity Risks Iden-
tification for ICS’. In: ARES 2023 - 18th International Conference on Availability, Reliability and
Security. Benevento, Italy, 29th Aug. 2023, pp. 1–10. DOI: 10 . 1145 / 3600160 . 3605067. URL:
https://hal.science/hal-04165414.

[16] M. Da Silva, M. Puys, P.-H. Thevenon, S. Mocanu and N. Nkawa. ‘Automated ICS template for
STRIDE Microsoft Threat Modeling Tool’. In: ARES 2023 - 18th International Conference on Avail-
ability, Reliability and Security. Benevento, Italy, 29th Aug. 2023, pp. 1–7. DOI: 10.1145/3600160
.3605068. URL: https://hal.science/hal-04165419.

National peer-reviewed Conferences

[17] Q. Guilloteau, A. Faure, M. Poquet and O. Richard. ‘Comment rater la reproductibilité de ses
expériences ?’ In: ComPAS 2023 Conférence francophone en informatique. 1-9. Annecy, France,
4th July 2023, à paraître. URL: https://hal.science/hal-04132438.

Project CTRL-A

19

Conferences without proceedings

[18] R. Bleuse and É. Rutten. ‘Composition of Scheduling and Control Theory Techniques’. In: JLESC
2023 - 15th Workshop Joint Laboratory for Extreme Scale Computing. Bordeaux, France, 21st Mar.
2023, pp. 1–10. URL: https://inria.hal.science/hal-04050732.

[19] E. Hotellier, F. Sicard, J. Francq and S. Mocanu. ‘Système de détection des intrusions distribué
pour les systèmes industriels’. In: RESSI 2023 - Rendez-Vous de la Recherche et de l’Enseignement
de la Sécurité des Systèmes d’Information. Neuvy sur Barangeon, France, 2023, pp. 1–3. URL:
https://hal.science/hal-04124168.

Doctoral dissertations and habilitation theses

[20] Q. Guilloteau. ‘Control-based runtime management of HPC systems with support for reproducible
experiments’. Université Grenoble Alpes, 11th Dec. 2023. URL: https://hal.science/tel-0438
9290.

Reports & preprints

[21] Q. Guilloteau. Simulating a Multi-Layered Grid Middleware. 19th May 2023. URL: https://hal.s

cience/hal-04101015.

[22] Q. Guilloteau, O. Richard, R. Bleuse and E. Rutten. Folding a Cluster containing a Distributed

File-System. 2023. URL: https://hal.science/hal-04038000.

Other scientific publications

[23] R. Pagano. ‘Adaptive control of HPC clusters for server overload avoidance’. Milano (I): Politecnico

Milano, 19th Dec. 2023. URL: https://inria.hal.science/hal-04390558.

12.3 Cited publications

[24] G. Delaval, A. Hore, S. Mocanu, L. Muller and E. Rutten. ‘Discrete Control of Response for Cyberse-
curity in Industrial Control’. In: IFAC 2020 - IFAC World Congress 2020. Proc. of the 21st IFAC World
Congress. Berlin, Germany, July 2020, pp. 1–8. URL: https://hal.archives-ouvertes.fr/hal-
02569406.

[25] K. Halitim. ‘Enhancing Efficiency through Control theory in Compute-Intensive Applications’. MA

thesis. Spirals, Inria Lille, Oct. 2023, p. 73. URL: https://inria.hal.science/hal-04357812.

[26]

[27]

J. Hellerstein, Y. Diao, S. Parekh and D. Tilbury. Feedback Control of Computing Systems. Wiley-IEEE,
2004.

J. O. Kephart and D. M. Chess. ‘The Vision of Autonomic Computing’. In: IEEE Computer 36.1 (Jan.
2003), pp. 41–50.

[28] O. Koucham, S. Mocanu, G. Hiet, J.-M. Thiriet and F. Majorczyk. ‘Cross-domain Alert Correlation
methodology for Industrial Control Systems’. In: Computers and Security 118.July (Apr. 2022),
p. 102723. DOI: 10.1016/j.cose.2022.102723. URL: https://hal.science/hal-03636549.

[29] S. Mocanu and J.-M. Thiriet. ‘Real-Time Performance and Security of IEC 61850 Process Bus
Communications’. In: Journal of Cyber Security and Mobility 10.2 (Apr. 2021), pp. 1–42. DOI: 10.13
052/jcsm2245-1439.1021. URL: https://hal.archives-ouvertes.fr/hal-03192264.

[30] M. Puys, P.-H. Thevenon, S. Mocanu, M. Gallissot and C. Sivelle. ‘SCADA cybersecurity awareness
and teaching with Hardware-In-The-Loop platforms’. In: Journal of Wireless Mobile Networks,
Ubiquitous Computing, and Dependable Applications 13.1 (2022), pp. 4–32. DOI: 10.22667/JOWUA.
2022.03.31.004. URL: https://hal.science/hal-03636896.

[31] P. Ramadge and W. Wonham. ‘On the Supervisory Control of Discrete Event Systems’. In: Proceedings

of the IEEE 77.1 (Jan. 1989).

20

Inria Annual Report 2023

[32] E. Strohmaier, J. Dongarra, H. Simon and M. Meuer. TOP500 list. URL: https://www.top500.org

/lists/ (visited on 07/01/2020).

[33] A. N. Sylla, M. Louvel, E. Rutten and G. Delaval. ‘Design Framework for Reliable Multiple Autonomic
Loops in Smart Environments’. In: 2017 IEEE International Conference on Cloud and Autonomic
Computing (ICCAC). Tucson, AZ, United States, Sept. 2017. URL: https://hal-cea.archives-o
uvertes.fr/cea-01570026.

[34] M. Tourchi Moghaddam, E. Rutten and G. Giraud. ‘Hierarchical Control for Self-adaptive IoT
Systems A Constraint Programming-Based Adaptation Approach’. In: HICSS 2022 - Hawaii Interna-
tional Conference on System Sciences. Hawaii, United States, Jan. 2022, pp. 1–10. URL: https://inr
ia.hal.science/hal-03461137.
<scratchpad>
- Main topic: Inria Centre at Université Grenoble Alpes
- Key points:
  - Research Centre focused on Autonomic Computing and Control Theory
  - Project CTRL-A aims to develop a novel framework for model-based design of controllers in Autonomic Computing
  - Collaboration with industry partners, such as CEA, Orange labs, and Naval Group
  - Research areas include Self-adaptive distributed systems, Cyber-Security, and Self-protection in Industrial Control Systems
- Structure: The document is divided into sections, including Activity Report, Project-Team, Domain, Theme, Contents, Management, Dissemination, and Partnerships and cooperations
- Potential artifacts to ignore: Web-related elements, such as links, HTML tags, and images
</scratchpad>

After analyzing the document, I have identified the following key points:

* The Inria Centre at Université Grenoble Alpes is a research centre focused on Autonomic Computing and Control Theory.
* The Project CTRL-A aims to develop a novel framework for model-based design of controllers in Autonomic Computing, with a focus on Self-adaptive distributed systems, Cyber-Security, and Self-protection in Industrial Control Systems.
* The project involves collaboration with industry partners, such as CEA, Orange labs, and Naval Group.
* The research areas include Self-adaptive distributed systems, Cyber-Security, and Self-protection in Industrial Control Systems.
* The document also mentions various projects and initiatives, such as PEPR Cybersecurity, PEPR Cloud, and PEPR TASE.

<final_summary>
The Inria Centre at Université Grenoble Alpes is a research centre focused on Autonomic Computing and Control Theory, with a specific emphasis on developing a novel framework for model-based design of controllers in Autonomic Computing. The Project CTRL-A aims to address this challenge, with a focus on Self-adaptive distributed systems, Cyber-Security, and Self-protection in Industrial Control Systems. The project involves collaboration with industry partners, such as CEA, Orange labs, and Naval Group, and covers various research areas, including Self-adaptive distributed systems, Cyber-Security, and Self-protection in Industrial Control Systems.
The Inria Centre at Université Grenoble Alpes is a research centre focused on Autonomic Computing and Control Theory, with a specific emphasis on developing a novel framework for model-based design of controllers in Autonomic Computing. The Project CTRL-A aims to address this challenge, with a focus on Self-adaptive distributed systems, Cyber-Security, and Self-protection in Industrial Control Systems. The project involves collaboration with industry partners, such as CEA, Orange labs, and Naval Group, and covers various research areas, including Self-adaptive distributed systems, Cyber-Security, and Self-protection in Industrial Control Systems.