Reconfigurable Control Systems (RCS) operate in dynamic environments where increasing demands for reconfiguration introduce significant uncertainties that complicate the design process. A major challenge is designing RCS with a Configuration Space (CS) that adapts to evolving operational contexts while maintaining fundamental system properties such as safety. In this paper, we propose Reconfiguration Control Q-Learning (ReCQ-Learning), an enhanced Q-learning algorithm for context-aware CS exploration. Our approach implements the reconfiguration controller as a Reinforcement Learning Reconfiguration Agent (RLRA) that recognizes and adapts to different reconfiguration contexts: Predefined-to-Predefined (P2P), Predefined-to-Foreseeable (P2F), and Predefined-to-New (P2N). These contexts represent increasing levels of uncertainty in the reconfiguration environment. The algorithm introduces novel exploration strategies that leverage this context taxonomy alongside validity constraints to efficiently guide the RLRA through the configuration space. This exploration algorithm serves dual purposes: during the offline design phase, it enables the RLRA to generate a valid initial CS by exploring primarily P2P and P2F contexts; during runtime, it handles reconfiguration triggers across all context types by learning optimized policies, with particular emphasis on developing robust strategies for P2N scenarios. The runtime learning feedback can then be used to improve the initial design specifications, creating an iterative improvement cycle between design and operation phases. Experimental validation using the FESTO CP Factory case study demonstrates that ReCQ-Learning significantly outperforms traditional approaches in exploration efficiency, configuration validity, and context adaptation across all reconfiguration scenarios.

To allow further automatic big data management, and to reduce data resource allocation problems, we propose an approach for embedded systems reconfiguration. The reconfiguration of embedded systems consists of adding new servers and clusters for exchanging the big data. For that, we propose a methodology based on an automatic programming approach to deploy new data servers based on NoSQL performances. These servers will be interlinked to get generated data from an embedded system. This will increase the number of satisfied transactions and queries with an optimal data access latency. Moreover, to evaluate the performance of our model, we propose a new definition based on committing full queries and the ability of all the servers to manage and process the generated big data with the constraint of time. To test and validate the proposed approach, a software tool named SFRBDM (Software for Real-time Big Data Management) has been developed. This software tool allows users to simulate the functioning of embedded systems, inject, generate data, and ensure recoveries between data servers and clusters. SFRBDM also allows calculating a better resource allocation of the simulated embedded databases with a higher performance based on NoSQL. The experimental study performed over multiple servers, with different structures and sizes, validates the efficiency of our proposed approach.

Opacity is a security property that guarantees the confidentiality of secret information from a partial observer of the system. To address this problem, we propose a modular approach that uses Supervisory and Control Theory to design a global supervisor for Modular Discrete Event Systems (MDES). This approach assumes the attacker can observe the shared alphabet between modules, and takes into account the modular structure of the system; which significantly decreases computational complexity compared to monolithic methods. To achieve this, we introduce a reduced-complexity algorithm, based on the Hyper Symbolic Observation Graph. The HSOG is an abstraction graph that considers only events that have a direct impact on the opacity. This reduces the number of states to be considered, making the algorithm more efficient.

Fog computing is rising as a dominant and modern computing model that thrives on delivering Internet of Things (IoT) computations. It is an addition to cloud computing, helping it handle services needing a faster response. A well-built dynamic service placement strategy could improve fog performance. This paper proposes a dynamic service placement strategy in a fog infrastructure for real-time applications. The main idea of this work is to perform a multi*objective optimization on response time and available resources in fog networks. Hence, depending on the fog node's response time and available computational resources, the algorithm will choose the fittest node to send the service in real-time. Finally, we model and evaluate our proposed strategy in the iFogSim-simulated Fog infrastructure. Results of the simulation studies demonstrate significant improvement in response time and resource utilization over several other strategies, improving the fog network's performance.

The design of Reconfigurable Control Systems (RCS) is a tremendous task since that their increasing need for reconfiguration imposes the anticipation of all potential runtime changes at design time, which is in most cases infeasible. To deal with the high dynamicity of RCS at design-time we propose a novel design approach, RLReC (Reinforcement Learning-based Reconfiguration Control) allowing for the improvement of reconfiguration knowledge through online (runtime) exploration. This approach combines the benefits of meta-modelling with Reinforcement Learning (RL) principals for reconfiguration knowledge modelling in order to handle early and effective exploration of various design choices. In this work, the reconfiguration controller is an autonomous software agent designed as a RL agent (RLRA) that handles both offline (exploitation) and online (exploration) learning through interactions with the controlled system at runtime. In particular, the RLRA has to select reconfiguration rules that were not selected before, which is known as exploration. The main aim of this proposal is to cope with design-time unplanned changes by giving the RLRA the ability of improving its knowledge through online learning feedback. We illustrate the use of our contributions with a running example from the manufacturing domain.

Discrete Event systems are dynamic systems with two main characteristics: their set of states is discrete and their dynamic is event driven (as opposed to time driven). In this paper, we study a security property for DES called opacity. A system$\mathcal{T}$, partially observed by a third party -called an attacker- is said to be opaque if the attacker can never conclude from its provided interface that$\mathcal{T}$is in a secret state. Given a critical system that may leak confidential information, an attacker and a subset of controllable actions, we propose an approach to synthesize a controller that enforces the system's opacity. This controller is designed as a function that applies, at run time, on the current executions to disable any controllable action that eventually leads to the violation of the system's opacity. Our approach is based on a novel graph called a Hyper Symbolic Observation Graph. The language obtained under control is proven to be maximal whatever is the relationship between the attacker and the controller observations.

Today NoSQL databases have become unavoidable that more and more companies are turning to these solutions. The variety of non-relational databases resides in their data storage structure to face the big amount of data and their modeling as well. Hence, our paper presents a unified schema modeling for NoSQL solutions based on a mapping approach. Indeed, NoSQL refers to a family of database management systems (DBMS) that departs from the traditional paradigm of relational databases. These systems remain efficient in a continuous scalability case. Among the NoSQL solutions our paper deals with Columns, Document and Graph oriented databases to test the feasibility of our proposed approach.

The Resource Provisioning (RP) and Task Scheduling (TS) issues has become an attractive paradigms in cloud industry, this is due to the increasing demand for the services provided by virtual machines that are structured by physical servers owned by the data centers of cloud service providers (CSPs). In this paper, we propose a new model based on multi-agent system for the RP and TS reducing the cost of energy using Deep Reinforcement Learning DRL. A Quantile Regression Deep Q Network (QR-DQN) algorithm generates an appropriate policy and the optimal long-term decisions. A set of experiments show the efficiency of our proposed scheduling approach and the performance of our task allocation method..

In this study, Artificial Neural Networks (ANN) were used to model parameters of scheduling of reconfigurable embedded systems containing resource constraints for applications running in real-time. The main goal is to implement a neural network based approach for real-time scheduling in order to handle real-time constraints in execution scenarios. Many techniques have been proposed for both the planning of tasks and reducing energy consumption. This paper presents a new hybrid contribution that handles the real-time scheduling of embedded systems by keeping energy consumption at a low power depending on the combination of Dynamic Voltage Scaling (DVS) and the energy Priority Earlier Deadline First (PEDF) algorithm. Indeed, in our original proposed approach, an other combination of DVS and time feedback can be used to scale the frequency by dynamically adjusting the operating voltage. The originality of our algorithm appears by allowing medium priority tasks to be executed more quickly before their deadline while decreasing their ability to be send again to the waiting list in order to ensure the execution of a task with the lowest voltage possible.

The prevalence of mobile computing platforms and the Internet of Things applications has triggered a quest to evolve energy-efficient software systems. Thus, addressing energy efficiency at the architectural level is a booming topic of interest. By this paper, we aim to propose an energy-driven architecture that encloses the most relevant artifacts tackled throughout the software design to analyze energy consumption. Our approach combines the meta-modeling and the graph transformations to formalize the understudied system and to survey the energy consumption. The energy consumption analysis is held on different alternative solutions of software architecture in a precocious stage for design more energy-efficient applications.

ENASE 2019, 14th International Conference on Evaluation of Novel Approaches to Software Engineering, Heraklion, GRECE, 04-/05/2019 - 05/05/2019

The design of adaptive real-time & embedded systems (RTES) is a hard task due to the complexity of both software/hardware features and the variability in the operational environment. This paper presents a new design pattern intended to support and facilitate the co-modeling and scheduling analysis of RTES. The contribution of this pattern is that is designed to i) support scheduling analysis allowing adaptation mechanisms ii) model all the software/hardware features and allocation in the same view iii) annotate the system with functional and non-functional properties using a high-level modeling language. The generated model from the pattern contains all the needed information to perform the schedulability tests. As a proof of concepts, we present experimental results for an automobile system.

Security is a fundamental property in the modeling of real-time embedded systems. Unfortunately, integrating this property is a hard task for a designer due to their small background concerning this feature. Thankfully, the design pattern can provide a practical solution to integrate security through an abstraction mode. However, Nowadays the number of design pattern is increasing, for that reason, the selection of suitable pattern is a fundamental challenge for designers. In this context, we propose in this position paper an approach to integrating security pattern in the phase of modeling of real-time embedded systems. To solve the problem of selection pattern we propose in our approach to use the ontology-based solution, and thus, we propose some methods to guarantee the performance of the systems after integrating.

Dynamic voltage/frequency scaling (DVFS) has been an efficient technique in exploiting multi-processors configurable characteristics. However, for large class of embedded real time applications, changing processors frequency interferes with tasks deadline guarantee. Under homogenous multiprocessor architecture, we are interested in intelligent offline/online reconfiguration based on fuzzy inference system. This paper investigates the efficiency of using fuzzy decision in multiprocessor architecture configuration. Goals of decision making are system schedulability guarantee and energy consumption optimization. Experiments concern real time periodic tasks scheduled with global EDF and realized over a developed simulation environment that supports DVFS technique. Different simulations were realized on H264 Video Decoder in order to evaluate the proposed approach response.

ICSOFT 2018, 13th International Conference on Software Technologies, Porto, PORTUGAL, 26-/07/2018 - 28/07/2018

We have examined the use of fault injections to evaluate the dependability of a transport layer protocol (i.e., TCP) in wireless sensor networks (WSN). We have focused on the layer's service i.e., congestion control, then, we have defined workload, faultload and dependability measures. The workload has two main components which are: the WSN architecture and the execution profile. We have defined in this paper the fault that may face different services and present their implementations in order to specify the faultload. We have introduced two dependability measures which are tolerance threshold and recovery time. We have demonstrated that the communication is interrupted after exceeding a given tolerance threshold (below this limit, the service employs a recovery time to resume execution). Our benchmarking targets eight different congestion control algorithms and presents several experiments to assess their dependability. According to our measures, BIC is the most dependable algorithm whereas Newreno and veno are the least dependable.

In this paper, the Optimal Discrete Controller Synthesis technique is applied in order to obtain an optimal routing scheme for Wireless Sensor Networks (WSNs). A centralized controller is automatically computed and it is then distributed in such the way to provide a local choice elements to each node to select its next-hop that can provide shortest, and more energy efficient routing paths towards the sink based in two criteria: minimizing the intermediate hop count and balancing the energy consumption over the entire network.

In nowadays, robotics database management systems are increasing. These systems ensure good storage of data and with big data analytic, a new approach demands new structures and methods for collecting, recording, and analyzing enterprise data. This paper work deals with the NoSQL databases which are the secret of the continual progression data that new data management solutions have been emerged. They crossed several areas as personalization, profile management, big data in real-time, content management, catalogue, view of customers, mobile applications, internet of things, digital communication and fraud detection. Machine learning, for example, thrives on more data, so smart machines can learn more and faster, the Robotics are our use of case to focus on our Test. The implementation of NoSQL for Robotics wrestle all the data they acquire into usable form because with the ordinary type of Robotics we are facing very big limits to manage and find the exact information in real-time. Our original proposed approach was demonstrated by experimental studies and running example used as a use case.

This paper work deals with the implementation of a neural networks based approach for real-time scheduling of embedded systems composed by Operating Systems (OS) tasks in order to handle real-time constraints in execution scenarios.In our approach, many techniques have been proposed for both the planning of tasks and reducing energy consumption.In fact, a combination of Dynamic Voltage Scaling (DVS) and time feedback can be used to scale the frequency dynamically adjusting the operating voltage.In this study, Artificial Neural Networks (ANNs) were used for modeling the parameters that allow the real-time scheduling of embedded systems under resources constraints designed for real-time applications running.Indeed, we present in this paper a new hybrid contribution that handles the real-time scheduling of embedded systems, low power consumption depending on the combination of DVS and Neural Feedback Scheduling (NFS) with the energy Priority Earlier Deadline First (PEDF) algorithm.Experimental results illustrate the efficiency of our original proposed approach.

While most embedded systems are designed for real-time applications, they suffer from resource constraints. Many techniques have been proposed for real-time task scheduling to reduce energy consumption. A combination of Dynamic Voltage Scaling (DVS) and feedback scheduling can be used to scale dynamically the frequency by adjusting the operating voltage, and to improve the run-time reliability of embedded systems. We present in this paper a novel hybrid contribution that handles real-time scheduling of embedded systems and low power consumption based on the combination of DVS and Neural Feedback Scheduling NFS with the priority-energy earliest-deadline-first (PEDF) algorithm.

The profile for Modeling and Analysis of Real-Time and Embedded systems (MARTE) defines a framework for annotating non-functional properties of embedded systems. In particular, the SAM (Schedulability Analysis Model) sub-profile offers stereotypes for annotating UML models with the needed information which will be extracted to fulfil a scheduling phase. However, SAM does not allow designers to specify data to be used in the context of adaptive systems development. It is in this context that we propose an extension for the MARTE profile, and especially the sub-profile Schedulability Analysis Modeling, to include adaptation mechanisms in scheduling view. We illustrate the advantages and effectiveness of our proposal by modeling a FESTO case study as an Adaptive Real-Time and Embedded system.

This research paper deals with the modeling and code generation of Reconfigurable Control Systems (RCS) following UML and B methods. Reconfiguration means dynamic changes of the system behavior at run-time according to well-defined conditions to adapt it to its environment. A reconfiguration scenario is applied as a response to user requirements or any possible evolution in its environment. We affect a Reconfiguration Agent (RA) to RCS to apply an automatic reconfiguration. A new approach called (R-UML-B) is proposed. It consists of three complementary phases: UML specification, B specification and the simulation phase. The first phase models the RCS following UML class and state diagrams. The second phase translates UML specification into B specification according to the well-defined rules and R-UML-B formalism to define the Behavior, Control, Listener, Database and Executive modules of the RCS. Then, we determine the refinement model and the code generation of the B abstract model in C code. We verify the RCS by following the B method in order to guarantee the consistency and the correctness of the specification, refinement and code generation levels. The third phase imports the generated C code to implement a simulator, named B Simulator in order to test and validate the proposed approach. All the contributions of this work are applied to the benchmark production system EnAS.

The emergence of new paradigms in information technology environments has lead to a dramatic growth in the amount of available web services. This raises many challenges in service computing and specifically in service composition problems. When there are multiple web services that offer the same functionalities, we need to select the best one according to its non-functional criteria (e.g. response time, price, reliability). However, proceeding in an exhaustive search is very time consuming and cannot be adopted in large scale systems. Skyline is a technique that helps reducing the size of our search space and comes as a complementary approach to the optimization methods. In fact, Skyline consists in preselecting the best candidates in the search space according to their non-functional criteria based on dominance relationships. This paper deals with some of the issues of the Skyline computation. In fact, used dominance relationships may suffer from looseness and data sparsity. Hence, the size of the Skyline may still be too large and cannot optimize service composition. We propose a new approach to overcome those problems by refining the Skyline with fuzzy techniques inspired by the Topsis method. Experimental evaluation demonstrates the effectiveness of the proposed concept and the efficiency of our implementation.

Several real-time embedded system must be dynamically reconfigured to account for hardware/software faults and/or maintain acceptable performances. Depending on the run-time environment, some reconfigurations might be unfeasible, i.e., they violate some real-time constraints of the system. In this paper, we deal with the development of dynamically reconfigurable embedded systems in terms of the production of execution schedules of system tasks (feasible configuration) under hard real-time constraints. More specifically, we propose an approach that starts from a set of reconfigurations to construct a Software Product Line that can be reused in a predictive and organized way to derive real-time embedded systems. To make sure that the SPL offers various feasible reconfigurations, we define an intelligent agent that automatically checks the system's feasibility after a reconfiguration scenario is applied on a multiprocessor embedded system. This agent dynamically determines precious technical solutions to define a new product whenever a reconfiguration is unfeasible. The set of products thus defined by the agent can then be unified into an SPL. The originality of our approach is its capacity to extract, from the unfeasible configurations of an embedded system, an SPL design enriched with real-time constraints and modeled with a UML Marte profile. The SPL design can assist in the comprehension, reconfiguration as well as evolution of the SPL in order to satisfy real-time requirements and to obtain a feasible system under normal and overload conditions.

Web services composition consists of combining web services features in order to respond to complex requests. Giving the large and exponentially growing number of web services offering similar features, services selection should be QoS-driven. Besides, the composition should also support and adapt to dynamic changes occurring during the composite service execution. In this context, this paper proposes an architecture supporting dynamic composition and global QoS Optimization. It also proposes an Ant Colony Optimization based algorithm for the QoS aware service selection.

Service composition is arising as an effective process to ensure the integration of multiple atomic web services in order to create customized composite web services. However, the exploding number of the deployed service candidates that is constantly increasing makes the process of choosing the best service candidates an important challenge. When there are multiple web services that offer the same functionalities, we need to select the best one according to its non-functional criteria (e.g. response time, price, reliability). Skyline is a technique that helps reducing the size of our search space and comes as a complementary approach to the optimization methods. In fact, Skyline consists in preselecting the best candidates in the search space according to their non-functional criteria. Most of the current Skyline computation relies on a strict dominance relationship called Pareto-dominance. In this paper, we propose a new method to compute the Skyline points with a fuzzy approach which allows taking into consideration the users preferences. We will through this paper show how we could construct a consistent fuzzy model to overcome the limitations of web service composition computation. Experimental evaluation demonstrates the effectiveness of the proposed concept and the efficiency of our implementation.

The tremendous growth in the amount of available web services raised many challenges in service computing and made the process of choosing the best service candidates an important challenge. Skyline is a technique that helps reducing the size of our search space and comes as a complementary approach to the optimization methods. In fact, Skyline consists in preselecting the best candidates in the search space according to their non-functional criteria. Those web services are considered optimal as they are not dominated by any other point in the search space. However, the data sparsity and the looseness of the dominance relationship used in comparing services pose some issues as the size of the Skyline may be still too large. Recommendation systems can overcome the limitations of Skyline computation by suggesting to the user the most relevant services according to his preferences. In this paper, we propose a new approach using collaborative filtering techniques to recommend to the users the best services according to the submitted request. Experimental evaluation demonstrates the effectiveness of the proposed concept and the efficiency of our implementation.

The paper deals with the modeling and verification of B method-based reconfigurable control systems. Reconfiguration means the dynamic changes of the system behavior at run-time according to well-defined conditions to adapt it to its environment. A reconfiguration scenario is applied as a response to improve the system's performance, or also to recover and prevent hardware/software errors, or also to adapt its behavior to new requirements according to the environment evolution. A new extension called Reconfigurable B “R-B” is proposed to specify reconfigurable control systems. It consists of two modules: Behavior and Control. The first defines all possible behaviors of the system, and whereas the second is a set of reconfiguration functions applied to change the system from a behavioral configuration to another one at run-time. We verify a reconfigurable control system by using the B method. The goal is to guarantee the consistency and the correctness of the abstract specification level. The second contribution of this paper deals with the verification of the reconfigurable system by avoiding redundant checking of different behaviors sharing similar operations. In order to control the complexity of verification, an optimal algorithm is developed and a prototyped tool called “Check R-B” is implemented. The paper's contribution is applied to a benchmark production system FESTO.

The paper deals with the real-time scheduling of reconfigurable embedded systems which can change their behavior at run-time by adding, removing, or also updating OS tasks according to external events or also user requirements. We propose a new approach that checks the system's feasibility of the tasks that we assume periodic and a periodic while minimizing their response times. An agent-based architecture is proposed to provide run-time technical solutions for users in order to reach again the system's feasibility after any reconfiguration scenario. The effectiveness and the performance of the designed approach is evaluated through simulation studies.

In this paper we introduce a aspect oriented modeling based-approach to build real-time application, which consists of transforming standard application into real-time ones at design time. This is performed by weaving real-time constraints, such as timing, scheduling, synchronization and architecture deployment into the standard application. We introduce a rule which defines how real-time constraints will be woven. Depending on the design language, this rule will have a set of instantiation values. We guess, in this paper, the case of UML designed standard applications which will be processed into a MARTE (UML profile for Modeling and Analyzing Real-Time and Embedded systems) real-time application model. We introduce our approach through a case study from the literature on real-time and embedded systems.

The paper deals with reconfigurable software component-based embedded control systems. We define a Control Component as a software unit to support control tasks of the system which is assumed to be a network of components with precedence constraints. We define an agent-based architecture to handle automatic reconfigurations under well-defined conditions by creating, deleting or updating components to bring the whole system into safe and optimal behaviors. We model the agent by nested state machines where states correspond to other state machines in order to cover all reconfiguration forms. Several complex networks can implement the system where each one is executed at a given time when a corresponding reconfiguration scenario is automatically applied by the agent. To check the correctness of each one of them, we apply in several steps a refinement-based approach that automatically specifies feasible Control Components according to the formalism Net Condition/ Event Systems (abbr. NCES) which is an extension of Petri nets. The model checker SESA is automatically applied in each step to verify deadlock properties of new generated components, and it is manually used to verify CTL-based properties according to user requirements. Two Industrial Benchmark Production Systems FESTO and EnAS available in our research laboratory are applied to explain the paper contribution.

This paper proposes an approach respectively Multi-Choice Goal Programming (MCGP) that integrated Analytical Hierarchy Process (AHP) for piloting a Quality Management System (QMS) designed for a real study case. This approach is used to determine the best solution verifies simultaneously the multi-objective functions and satisfying the different constraints of real company which presents a major problem for controlling the quality level of production.

Designing real-time system is hard and costly. Real-time application designer must care about functional design and real-time constraints. Aspect oriented programming (AOP) allows the separation of the functional mechanisms from the non-functional ones. The separation of concerns (SoC) in AOP enhances the productivity and reduces the applications cost. In this paper we propose a SoC approach for designing real-time applications. The main idea is to process standard applications into real-time ones. The processing is done by ldquoweavingrdquo real-time constraints on the standard applications model. We introduce a rule which defines how real-time constraints will be woven. Depending on the design language, this rule will have a set of instantiation values. We gess, in this paper, the case of UML designed standard applications which will be processed into a MARTE (i.e. UML profile for modeling and analyzing real-time and embedded systems) real-time application model.