Hamza Gharsellaoui

New Optimal Solutions for Real-Time Reconfigurable Periodic Asynchronous Operating System Tasks with Minimizations of Response Time

Scheduling tasks is an essential requirement in most real-time and embedded systems, but leads to unwanted central processing unit (CPU) overheads. The authors present a real-time schedulability algorithm for preemptable, asynchronous and periodic reconfigurable task systems with arbitrary relative deadlines, scheduled on a uniprocessor by an optimal scheduling algorithm based on the earliest deadline first (EDF) principles and on the dynamic reconfiguration. A reconfiguration scenario is assumed to be a dynamic automatic operation allowing addition, removal or update of operating system’s (OS) functional asynchronous tasks. When such a scenario is applied to save the system at the occurrence of hardware-software faults, or to improve its performance, some real-time properties can be violated. The authors propose an intelligent agent-based architecture where a software agent is used to satisfy the user requirements and to respect time constraints. The agent dynamically provides precious technical solutions for users when these constraints are not verified, by removing tasks according to predefined heuristic, or by modifying the worst case execution times (WCETs), periods, and deadlines of tasks in order to meet deadlines and to minimize their response time. They implement the agent to support these services which are applied to a Blackberry Bold 9700 and to a Volvo system and present and discuss the results of experiments.

Real-Time Reconfigurable Scheduling of Aperiodic OS Tasks

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 systems 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 systems feasibility after any reconfiguration scenario. The effectiveness and the performance of the designed approach is evaluated through simulation studies.

Reconfiguration of Synchronous Real-Time Operating System

Real-time scheduling is the theoretical basis of real-time systems engineering. Earliest Deadline first EDF is an optimal scheduling algorithm for uniprocessor real-time systems. The paper deals with Reconfigurable Uniprocessor embedded Real-Time Systems classically implemented by different OS tasks that the authors suppose independent, synchronous and periodic to meet functional and temporal properties described in user requirements. They define two forms of automatic reconfigurations which are applied at run-time: Addition-Remove of tasks or just modifications of their temporal parameters: WCET and/or Periods. The authors define a new semantic of the reconfiguration where a crucial criterion to consider is the automatic improvement of the systems feasibility at run-time by using an Intelligent Agent that automatically checks the systems feasibility after any reconfiguration scenario to verify if all tasks meet the required deadlines. To handle all possible reconfiguration solutions, the authors propose an agent-based architecture that applies automatic reconfigurations to re-obtain the systems feasibility and satisfy user requirements. Therefore, they developed the tool RT-Reconfiguration to support these contributions that they apply on the running example system and the authors apply the Real-Time Simulator, Cheddar to check the whole system behavior and evaluate the performance of the algorithm. They present simulations of this architecture where the agent that implemented is evaluated.

New Optimal Preemptively Scheduling for Real-Time Reconfigurable Sporadic Tasks Based on Earliest Deadline First Algorithm

This paper examines the problem of scheduling the mixed workload of both sporadic (on-line) and periodic (off-line) tasks on uniprocessor in a hard real-time environment. The authors introduce an optimal earliest deadline scheduling algorithm to optimize response time while ensuring that all periodic tasks meet their deadlines and to accept as many sporadic tasks. A necessary and sufficient schedulability test is presented, and an efficient O(n+m) guarantee algorithm is proposed. This optimal algorithm results in dynamic scheduling solutions. They are presented by a proposed intelligent agent-based architecture where a software agent is used to evaluate the response time, to calculate the processor utilization factor and also to verify the satisfaction of real-time deadlines. The agent dynamically provides technical solutions for users where the system becomes unfeasible by sending sporadic tasks to idle times, by modifying the deadlines of tasks, the worst case execution times (WCETs), the activation time, by tolerating some non critical tasks according to the (m, n) firm and a reasonable cost, or in the worst case by removing some non hard (soft) tasks according to predefined heuristic. The authors implement the agent to support these services which are applied to extensive experiments with real-life design examples in order to demonstrate the effectiveness and the excellent performance of the new optimal algorithm in normal and overload conditions.

A Novel Approach for Real-time Extracting Data From NoSQL Embedded Data Bases

In nowadays industry, embedded databases which display a mixture of multimedia signals and big data with modal interfaces need to be highly reconfigurable to meet real-time constraints, data stores optimization problems and to solve requirements problem in order to achieve high scalability and availability. This paper deals with a multi-objective extracting, managing and interrogating problem for a reconfigurable real-time embedded databases. In this case, new methods are required and NoSQL database created for solving the mentioned sub-problems and to be able to store big data effectively, demand for high performance when extracting, managing and interrogating these embedded databases. We also discuss the advantages and disadvantages of a

Real-Time Reconfigurable Scheduling of Sporadic Tasks

This book chapter deals with the problem of scheduling multiprocessor real-time tasks by an optimal EDF-based scheduling algorithm. Two forms of automatic reconfigurations which are assumed to be applied at run-time: Addition-Removal of tasks or just modifications of their temporal parameters: WCET and/or deadlines. Nevertheless, when such a scenario is applied to save the system at the occurrence of hardware-software faults, or to improve its performance, some real-time properties can be violated at run-time. We define an Intelligent Agent that automatically checks the system’s feasibility after any reconfiguration scenario was applied on a multiprocessor embedded system. Indeed, if the system is unfeasible, then the Intelligent Agent dynamically provides precious technical solutions for users to send sporadic tasks to idle times, by modifying the deadlines of tasks, the worst case execution times (WCETs), the activation time, by tolerating some non critical tasks, by sending some tasks from their current processors to be scheduled in other processors, or in the worst case by removing some soft tasks according to predefined heuristic. We implement the agent to support these services.

On a Genetic-Tabu Search Based Algorithm for Two-Dimensional Guillotine Cutting Problems

The paper deals with the purpose of one hybrid approach for solving the constrained two-dimensional cutting (2DC) problem. The authors study this hybrid approach that combines the genetic algorithm and the Tabu search method. For this problem, they assume a packing of a whole number of rectangular pieces to cut, and that all cuts are of guillotine type in one sheet of a fixed width and an infinite height. Finally, they undertake an extensive experimental study with a large number of problem instances extracted from the literature by the Hoppers benchmarks in order to support and to prove their approach and to evaluate the performance.

New Optimal Solutions for Real-Time Scheduling of Operating System Tasks Based on Neural Networks.

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.

Distributed Intelligent Systems for Network Security Control

The great number of heterogeneous interconnected operating systems gives greater access to intruders and makes it easier for malicious users to break systems security policy. Also, a single security control agent is insufficient to monitor multiple interconnected hosts and to protect distributed operating systems from hostile uses. This paper shows the ability of distributed security controller’s agents to correlate data stream from heterogeneous hosts and to trace abnormal behavior in order to protect network security. An experimental study is done to improve our proposed approach.

A Novel Proposed Approach for Real-Time Scheduling Based on Neural Networks Approach with Minimization of Power Consumption

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 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.