A Cellular Automaton Based Approach for Real Time Embedded Systems Scheduling Problem Resolution

This paper proposes Makespan and Reliability based approach, a static sheduling strategy for distributed real time embedded systems that aims to optimize the Makespan and the reliability of an application. This scheduling problem is NP-hard and we rely on a heuristic algorithm to obtain efficiently approximate solutions. Two contributions have to be outlined: First, a hierarchical cooperation between heuristics ensuring to treat alternatively the objectives and second, an Adapatation Module allowing to improve solution exploration by extending the search space. It results a set of compromising solutions offering the designer the possibility to make choices in line with his (her) needs. The method was tested and experimental results are provided

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An evolutionary cellular automaton for minimizing energy consumption in network on chip-based soft real-time embedded systems

2015 3rd International Conference on Control, Engineering & Information Technology (CEIT) ◽

10.1109/ceit.2015.7233186 ◽

2015 ◽

Author(s):

Djalila Belkebir ◽

Fateh Boutekkouk

Keyword(s):

Energy Consumption ◽

Embedded Systems ◽

Cellular Automaton ◽

Real Time ◽

Network On Chip ◽

On Chip

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Real-Time Embedded Systems Scheduling Optimization

International Journal of Applied Evolutionary Computation ◽

10.4018/ijaec.2021010104 ◽

2021 ◽

Vol 12 (1) ◽

pp. 43-73

Author(s):

Fateh Boutekkouk

Keyword(s):

Embedded Systems ◽

Real Time ◽

Design Space ◽

Artificial Immune Systems ◽

Scheduling Problem ◽

Future Directions ◽

Real Time Scheduling ◽

Energy Consumption Reduction ◽

Consumption Reduction ◽

Time Scheduling

The embedded real-time scheduling problem is qualified as a hard multi-objective optimization problem under constraints since it should compromise between three key conflictual objectives that are tasks deadlines guarantee, energy consumption reduction, and reliability enhancement. On this fact, conventional approaches can easily fail to find a good tradeoff in particular when the design space is too vast. On the other side, bio-inspired meta-heuristics have proved their efficiency even if the design space is very large. In this framework, the authors review the most pertinent works of literature targeting the application of bio-inspired methods to resolve the real-time scheduling problem for embedded systems, notably artificial immune systems, machine learning, cellular automata, evolutionary algorithms, and swarm intelligence. A deep discussion is conducted putting the light on the main challenges of using bio-inspired methods in the context of embedded systems. At the end of this review, the authors highlight some of the future directions.

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A Novel Rail-Network Hardware Simulator for Embedded System Programming

Electronics ◽

10.3390/electronics10010013 ◽

2020 ◽

Vol 10 (1) ◽

pp. 13

Author(s):

Balaji M ◽

Chandrasekaran M ◽

Vaithiyanathan Dhandapani

Keyword(s):

Embedded Systems ◽

Embedded System ◽

Real Time ◽

Learning Outcomes ◽

Real World ◽

Time Constraints ◽

Network Simulator ◽

Practical Applications ◽

Rail Network ◽

Knowledge Enhancement

A Novel Rail-Network Hardware with simulation facilities is presented in this paper. The hardware is designed to facilitate the learning of application-oriented, logical, real-time programming in an embedded system environment. The platform enables the creation of multiple unique programming scenarios with variability in complexity without any hardware changes. Prior experimental hardware comes with static programming facilities that focus the students’ learning on hardware features and programming basics, leaving them ill-equipped to take up practical applications with more real-time constraints. This hardware complements and completes their learning to help them program real-world embedded systems. The hardware uses LEDs to simulate the movement of trains in a network. The network has train stations, intersections and parking slots where the train movements can be controlled by using a 16-bit Renesas RL78/G13 microcontroller. Additionally, simulating facilities are provided to enable the students to navigate the trains by manual controls using switches and indicators. This helps them get an easy understanding of train navigation functions before taking up programming. The students start with simple tasks and gradually progress to more complicated ones with real-time constraints, on their own. During training, students’ learning outcomes are evaluated by obtaining their feedback and conducting a test at the end to measure their knowledge acquisition during the training. Students’ Knowledge Enhancement Index is originated to measure the knowledge acquired by the students. It is observed that 87% of students have successfully enhanced their knowledge undergoing training with this rail-network simulator.

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