Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: New Results

Infrastructure-level support

We apply the results of the previous axes of the team's activity 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 [13] , towards a general view of Feedback Control as MAPE-K loop in Autonomic Computing [21] .

Autonomic Cloud and Big-Data systems

Coordination in multiple-loop autonomic Cloud systems

Participants : Soguy Gueye, Gwenaël Delaval, Eric Rutten.

Complex computing systems are increasingly self-adaptive, with an autonomic computing approach for their administration. Real systems require the co-existence of multiple autonomic management loops, each complex to design. However their uncoordinated co-existence leads to performance degradation and possibly to inconsistency. There is a need for methodological supports facilitating the coordination of multiple autonomic managers. To tackle this problem, we take a global view and underscore that Autonomic Management Systems (AMS) are intrinsically reactive, as they react to flows of monitoring data by emitting flows of reconfiguration actions. Therefore we propose a new approach for the design of AMSs, based on synchronous programming and discrete controller synthesis techniques. They provide us with high-level languages for modeling the system to manage, as well as means for statically guaranteeing the absence of logical coordination problems. Hence, they suit our main contribution, which is to obtain guarantees at design time about the absence of logical inconsistencies in the taken decisions. We detail our approach, illustrate it by designing an AMS for a realistic multi-tier application, and evaluate its practicality with an implementation [10] .

In order to coordinate managers without breaking their natural modularity. we address the problem with a method stressing modularity, and focusing on the discrete control of the interactions of managers. We make proposals for the distributed execution of modular controllers, first in synchronized way, and then relaxing this synchronization. We apply and validate our method on a multi-loop multi-tier system in a data-center [16] .

We addressed these problems in the context of the ANR project Ctrl-Green, in cooperation with LIG (N. de Palma) in the framework of the PhD of S. Gueye and the post-doc of N. Berthier.

Control for Big data

Participants : Bogdan Robu [Gipsa-lab] , Mihaly Berekmeri [Gipsa-lab] , Nicolas Marchand [Gipsa-lab] .

Companies have a fast growing amounts of data to process and store, a data explosion is happening next to us. Currently one of the most common approaches to treat these vast data quantities is the MapReduce parallel programming paradigm. While it?s use is widespread in the industry, ensuring performance constraints, while also minimizing costs, provides considerable challenges. To deal with these issues we propose a control theoretical approach, based on techniques that have already proved their usefulness in the control community. We developed an algorithm to create the first linear dynamic model for a Big Data MapReduce Cloud system, running a concurrent workload. Furthermore we identify two important control use cases: relaxed performance - minimal resource and strict performance. We developed the first feedback control mechanism for such systems. Then to minimize the number of control actuations, an event-based feedback controller was also introduced. Furthermore to address the strict performance challenges a feedforward controller that efficiently suppresses the effects of large workload size variations is developed. On top of this issues an optimal predictive control which deals with concurrent objectives (dependability and performance) is implemented. The approach is validated online in a benchmark running in a real 60 node MapReduce cluster, using a data intensive Business Intelligence [22] , [23] .

This work is performed in cooperation with LIG (S. Bouchenak) in the framework of the PhD of M. Berekmeri.

Reconfiguration control in DPR FPGA

Participant : Eric Rutten.

Dynamically reconfigurable hardware has been identified as a promising solution for the design of energy efficient embedded systems. However, its adoption is limited by the costly design effort including verification and validation, which is even more complex than for non dynamically reconfigurable systems. We worked on this topic in the context of a ensign environment, developed in the framework of the ANR project Famous, in cooperation with LabSticc in Lorient and Inria Lille (DaRT team) [12] . We proposed a tool-supported formal method to automatically design a correct-by-construction control of the reconfiguration. By representing system behaviors with automata, we exploit automated algorithms to synthesize controllers that safely enforce reconfiguration strategies formulated as properties to be satisfied by control. We design generic modeling patterns for a class of reconfigurable architectures, taking into account both hardware architecture and applications, as well as relevant control objectives. We validate our approach on two case studies implemented on FPGAs [1] .

We are currently valorizing results in more publications [12] , [9] , and extending the use of control techniques by evaluating the new tool ReaX developed at Inria Rennes (Sumo).

We are starting a new ANR project called HPeC, within which some of these topics will be extended, especially regarding hierarchical and modular control, and logico-numeric aspects.

Autonomic memory management in HPC

Participants : Naweiluo Zhou, Gwenaël Delaval, Bogdan Robu, Eric Rutten.

Parallel programs need to manage the time trade-off between synchronization and computation. A high parallelism may decrease computing time but meanwhile increase synchronization cost among threads. Software Transactional Memory (STM) has emerged as a promising technique, which bypasses locks, to address synchronization issues through transactions. A way to reduce conflicts is by adjusting the parallelism, as a suitable parallelism can maximize program performance. However, there is no universal rule to decide the best parallelism for a program from an offline view. Furthermore, an offline tuning is costly and error-prone. Hence, it becomes necessary to adopt a dynamical tuning-configuration strategy to better manage a STM system. Autonomic control techniques begin to receive attention in computing systems recently. Control technologies offer designers a framework of methods and techniques to build autonomic systems with well-mastered behaviors. The key idea of autonomic control is to implement feedback control loops to design safe, efficient and predictable controllers, which enable monitoring and adjusting controlled systems dynamically while keeping overhead low. We propose to design feedback control loops to automate the choice of parallelism at runtime and diminish program execution time.

In the context of the action-team HPES of the Labex Persyval-lab( ) (see 9.1 ), this work is performed in cooperation with LIG (J.F. Méhaut) in the framework of the PhD of N. Zhou.

Control of smart environments

Participants : Adja Sylla, Mengxuan Zhao, Eric Rutten, Hassane Alla [Gipsa-lab] .

Generic supervision architecture

New application domains of control, such as in the Internet of Things (IoT) and Smart Environments, require generic control rules enabling the systematization and the automation of the controller synthesis. We worked on an approach for the generation of Discrete Supervisory Controllers for these applications. A general modeling framework is proposed for the application domain of smart home. We formalize the design of the environment manager as a Discrete Controller Synthesis (DCS) problem, w.r.t. multiple constraints and objectives, for example logical issues of mutual exclusion, bounding of power peaks. We validate our models and manager computations with the BZR language and an experimental simulator This work was performed in cooperation with Orange labs (G. Privat) in the framework of the Cifre PhD of M. Zhao [8] .

Rule-based specification

In the context of IoT applications like mart home environments, the rules for programming in the LINC framework are used as a flexible tool to govern the relations between sensors and actuators. Runtime coordination and formal analysis becomes a necessity to avoid side effects mainly when applications are critical. In cooperation with CEA LETI/DACLE, we are working on a case study for safe applications development in IoT and smart home environments [17] .