The research will be conducted in the Inria centre at the University Grenoble Alpes. Inria is the French national institute for research in digital science and technology. Grenoble is an attractive city in the heart of the Alps, easily reachable from Paris, but also at a crossroads from Italy, Switzerland and Lyon, with a large student population and a number of cultural and sports facilities.
This postdoc grant is part of the ANR ADAPT project, that gathers 3 partners: Inria, Verimag, and FEMTO-ST. A PhD student and an engineer are also specifically dedicated to the project. Travel expenses are covered within the limits of the scale in force.

Context - Control for Sustainable Computing

Control Theory has recently been successfully applied to manage the resources of computing systems, such as cloud services [1], high performance computing [2] or mobile applications [3]. Such software systems are typically a set of homogenous, distributed, communicating entities, possibly with a hierarchical architecture. Control is mostly used to handle their variability, e.g. in their workload or environment, and ensures the best performances [4]. This work is rather focused on ensuring properties, such as frugality and resilience, given the large and growing impact of computing systems and the increasing advent of extreme events.


Approach - Distributed Control Architecture

This project aims at using control theory tools for hierarchical and distributed software systems [5], e.g. control of multi-agent systems [6], with the aim of their adaptation. The architecture of structured and interacting elements, including controllers, are defined using formal tools (i.e. component-based system). Previous works of the team have contributed to the flexible design and development of hierarchically structured cyber-physical systems, which elements are adapted with optimal control [7]. Additionnally, the team has developped the Chips (Control of Hierarchical Interconnected Programmable Systems) language for modeling the control of an adaptable discrete system [8].
The main challenges in the control design are (i) the hierarchical structure of the entities, organized based on spatial or temporal aspects, (ii) the distribution in a large number of elements with low-computing capacity, (iii) the unreliable communication, leading to non-uniform sampling or event-based sampling, (iv) the low-reliability of the hardware, causing possibly stochastic behavior, (v) the highly unfavorable conditions, leading to highly variable behavior with extremely low availability of resources.

This general problem will also be studied with an application to energy/functionality management of modular robots forming programmable matter (e.g. a set of identical connected elements with low computing capacity), in collaboration with 2 PhD students (specialized in formal methods, and robotics & energy).

Research Directions

The general objective of the project is to study frugality and resilience as a control problem in computing systems: i) express the problem of performance degradation for resource usage limitation, and ii) consider resilience of structured distributed systems.

The project covers the following research directions:

• state the problem of degradation of performance for resource usage limitation, e.g. with inspiration from brownout approaches [1],
• explore the distribution of control on a set of homogeneous systems, and extend to hierarchically structured systems, e.g. extending approximate simulation results [9].
• consider the resilience of the control system, under highly unreliable hardware and communication with high variability.

References

[1] Klein, C., Maggio, M., Årzén, K. E., & Hernández-Rodriguez, F. (2014, May). Brownout: Building more robust cloud applications. In Proceedings of the 36th International Conference on Software Engineering (pp. 700-711).
[2] Ismail Hawila, Sophie Cerf, Raphaël Bleuse, Swann Perarnau, Eric Rutten. Adaptive Power Control for Sober High-Performance Computing. CCTA 2022 - 6th IEEE Conference on Control Technology and Applications, Aug 2022, Trieste, Italy. pp.1-8. ⟨hal-03765849⟩
[3] Sophie Cerf, Bogdan Robu, Nicolas Marchand, Sara Bouchenak. Privacy protection control for mobile apps users. Control Engineering Practice, 2023, 134 (May), pp.105456. ⟨10.1016/j.conengprac.2023.105456⟩. ⟨hal-03977386⟩
[4] Shevtsov, S., Berekmeri, M., Weyns, D., & Maggio, M. (2017). Control-theoretical software adaptation: A systematic literature review. IEEE Transactions on Software Engineering, 44(8), 784-810.
[5] Scattolini, R. (2009). Architectures for distributed and hierarchical model predictive control–a review. Journal of process control, 19(5), 723-731.
[6] Dorri, A., Kanhere, S. S., & Jurdak, R. (2018). Multi-agent systems: A survey. Ieee Access, 6, 28573-28593.[7] Simon Bliudze, Sophie Cerf, Olga Kouchnarenko. A Hybrid Modelling Approach for Hierarchical Control of Structured CPSs. Components Operationally: Reversibility and System Engineering, Claudio Antares Mezzina; Alan Schmitt, Jun 2025, Lille, France. pp.175-196, ⟨10.1007/978-3-031-99717-4_10⟩. https://inria.hal.science/hal-05158948
[8] Anna Gallone, Simon Bliudze, Sophie Cerf, Olga Kouchnarenko. Fancy some Chips for your TeaStore? Modeling the control of an adaptable discrete system. WACA 2025 - First Workshop on Adaptable Cloud Architectures, Giuseppe De Palma; Saverio Giallorenzo, Jun 2025, Lille, France. pp.58-78, ⟨10.4204/EPTCS.438.4⟩. https://inria.hal.science/hal-05158958
[9] Girard, A., & Pappas, G. J. (2006, December). Hierarchical control using approximate simulation relations. In Proceedings of the 45th IEEE Conference on Decision and Control (pp. 264-269). IEEE.