Section: New Results

Design methods for reconfiguration controller design in computing systems

We apply the results of the previous axes of the team's activity, as well as other control techniques, to a range of infrastructures of different natures, but sharing a transversal problem of reconfiguration control design. From this very diversity of validations and experiences, we draw a synthesis of the whole approach, towards a general view of Feedback Control as MAPE-K loop in Autonomic Computing [23] [9].

High-Performance Computing

Participants : Agustin Yabo, Soguy Mak Kare Gueye, Gwenaël Delaval, Stéphane Mocanu, Bogdan Robu, Eric Rutten.

Automated regulation and software transactional memory

A parallel program needs to manage the trade-off between the time spent in synchronisation and computation. This trade-off is significantly affected by its parallelism degree. A high parallelism degree may decrease computing time while increasing synchronisation cost. We performed work on dynamic control of thread parallelism and mapping. We address concurrency issues via Software Transactional Memory (STM). We implement feedback control loops to automate management of threads and diminish program execution time.

This work was performed in the framework on the PhD of Naweiluo Zhou, and published in the journal on Concurrency and Computation: Practice and Experience [13].

A Control-Theory based approach to minimize cluster underuse

HPC systems are facing more and more variability in their behavior, related to e.g., performance and power consumption, and the fact that they are less predictable requires more runtime management. One such 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. This work resulted in first results addressing the problem of automated resource management in an HPC infrastructure, using techniques from Control Theory to design a controller that maximizes cluster utilization while avoiding overload. We put in place a mechanism for feedback (Proportional Integral, PI) control system software, through a maximum number of jobs to be sent to the cluster, in response to system information about the current number of jobs processed. Additionally, we developed a Model-Predictive Controller to improve the performance of the system.

This work is done in cooperation with the Datamove team of Inria/LIG, and Gipsa-lab. It was the topic of the Master's thesis of Agustin Yabo [25]. Preliminary results were published in the AIScience workshop (Autonomous Infrastructure for Science) of the HPDC conference [19].

Reconfiguration control in DPR FPGA

DPR FPGA and discrete control for reconfiguration

Implementing self-adaptive embedded systems, such as UAV drones, involves an offline provisioning of the several implementations of the embedded functionalities with different characteristics in resource usage and performance in order for the system to dynamically adapt itself under uncertainties. We propose an autonomic control architecture for self-adaptive and self-reconfigurable FPGA-based embedded systems. The control architecture is structured in three layers: a mission manager, a reconfiguration manager and a scheduling manager. In this work we focus on the design of the reconfiguration manager. We propose a design approach using automata-based discrete control. It involves reactive programming that provides formal semantics, and discrete controller synthesis from declarative objectives.

This work is in the framework of the ANR project HPeC (see Section 8.2.1), and is published in the International Workshop on High Performance and Dynamic Reconfigurable Systems and Networks (DRSN 2018), part of the HPCS 2018 conference [17] ; for the evaluation of the apllication of logico-numeric control, in the CCTA 18 conference [15] ; for the proposal of a Domain Specific Language, in the ICAC 2018 conference [16].

Mission management and stochastic control

In the Mission Management workpackage of the ANR project HPeC, a concurrent control methodology is constructed for the optimal mission planning of a U.A.V. in stochastic environnement. The control approach is based on parallel ressource sharing Partially Observable Markov Decision Processes modeling of the mission. The parallel POMDP are reduced to discrete Markov Decision Models using Bayesian Networks evidence for state identification. The control synthesis is an iterative two step procedure : first MDP are solved for the optimisation of a finite horizon cost problem ; then the possible ressource conflicts between parallel actions are solved either by a priority policy or by a QoS degradation of actions, e.g., like using a lower resolution version of the image processing task if the ressource availability is critical.

This work was performed in the framework on the PhD of Chabha Hireche, and published in the journal on Sensors [24], [12].

IoT

Participants : Neïl Ayeb, Adja Sylla, Gwenaël Delaval, Stéphane Mocanu, Eric Rutten.

Control of smart buildings

A smart environment is equipped with numerous devices (i.e., sensors, actuators) that are possibly distributed over different locations (e.g., rooms of a smart building). These devices are automatically controlled to achieve different objectives related, for instance, to comfort, security and energy savings. Our work proposes a design framework based on the combination of the rule based middleware LINC and the automata based language Heptagon/BZR (H/BZR). It consists of: an abstraction layer for the heterogeneity of devices, a transactional execution mechanism to avoid inconsistencies and a controller that, based on a generic model of the environment, makes appropriate decisions and avoids conflicts. A case study with concrete devices, in the field of building automation, is presented to illustrate the framework.

This work is in the framework of the cooperation with CEA (see Section 7.1), and is published in the CCTA 2018 conference [20].

Device management

The research topic is targeting an adaptative and decentralized management for the IoT. It will contribute design methods for processes in virtualized gateways in order to enhance IoT infrastructures. More precisely, it concerns Device Management in the case of large numbers of connected sensors and actuators, as can be found in Smart Home and Building, Smart Electricity grids, and industrial frameworks as in Industry 4.0.

This work is in the framework of the Inria/Orange labs joint laboratory (see Section 7.2.1), and supported by the CIFRE PhD thesis grant of Neïl Ayeb, starting dec. 2017.

Security in SCADA industrial systems

We focus mainly on vulnerability search, automatic attack vectors synthesis and intrusion detection. Model checking techniques are used for vulnerability search and automatic attack vectors construction. Intrusion detection is mainly based on process-oriented detection with a technical approach from run-time monitoring. The LTL formalism is used to express safety properties which are mined on an attack-free dataset. The resulting monitors are used for fast intrusion detections.

A demonstrator of attack/defense scenario in SCADA systems will be built on the existing G-ICS lab (hosted by ENSE3/Grenoble-INP).

This work is in the framework of the ANR project Sacade on cybersecurity of industrial systems (see Section 8.2.2) [18] [22] [21].

The work is also supported by Grenoble Alpes Cybersecurity Institute (see Section 8.1.1).

Ongoing work concerns the complementary topic of analysis and identification of reaction mechanisms for self-protection in cybersecurity, where, beyond classical defense mechanisms that detect intrusions and attacks or assess the kind of danger that is caused by them, we explore models and control techniques for the automated reaction to attacks, in order to use detection information to take the appropriate defense and repair actions.