Optimal priority assignment for real-time systems: a coevolution-based approach

Lee, Jaekwon; Shin, Seung Yeob; Nejati, Shiva; Briand, Lionel C.

doi:10.1007/s10664-022-10170-1

Cited by 4 publications

(2 citation statements)

References 76 publications

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“…The scheduling problem is addressed in [7] where a new algorithm based on Reinforcement Learning (RL) is proposed for global fixed-priority task assignment in multi-processor real-time systems. In [8], the authors propose a new scheduling algorithm that maximizes the magnitude of safety margins while respecting the engineering constraints. In [9], new scheduling policies are proposed for heterogeneous platforms equipped with a hardware accelerator.…”

Section: Introductionmentioning

confidence: 99%

PSRL: A New Method for Real-Time Task Placement and Scheduling Using Reinforcement Learning

Haouari,

Mzid,

Mosbahi

2023

International Conferences on Software Engineering and Knowledge Engineering

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Modern real-time system development methodologies describe a stage in which application tasks are deployed onto an execution platform. The deployment process is divided into two steps: (i) task placement on processors and (ii) task scheduling to determine their execution order. The overall performance of the deployment model depends on the two steps, which are interdependent. In this paper, a new method based on reinforcement learning techniques, called PSRL, is proposed. PSRL explores all the feasible placements in the first step. In the second step, an optimal schedule is considered for each feasible placement. PSRL generates the optimal deployment, which corresponds to the placement and scheduling that minimize task response times. Application to case studies shows the applicability and quality of the obtained solutions when compared to related work.

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Section: Introductionmentioning

confidence: 99%

PSRL: A New Method for Real-Time Task Placement and Scheduling Using Reinforcement Learning

Haouari,

Mzid,

Mosbahi

2023

International Conferences on Software Engineering and Knowledge Engineering

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“…Real time tasks often require task priority changes and preemption of tasks for the successful execution i.e. meeting deadlines effectively [12]. So it is recommended that an operating system with preemptible kernel be used for any real time system.…”

Section: Introductionmentioning

confidence: 99%

Measurement of Kernel Preemption Time of Real Time Operating Systems

2023

MCET

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In a Real Time System, when a high priority task is scheduled, it preempts ongoing lower priority tasks and sometimes it preempts even the kernel too. In normal cases, the time needed for the preemption is too small and hence is not considered in task scheduling. But, in some mission critical hard real time systems, even a small fraction of a second should be taken into account as it could affect the deadline. This research work proposes two models -embedded model and software model, among which the latter has been used for the experiment that calculates the kernel preemption time. The hardware model consists of a triangular wave generator called TRIANG that takes timer values from real time tasks and sends outputs to a comparator module. Finally, the comparator compares the time differences, and hence, calculates the kernel preemption time. Whereas the software model uses the RT-Linux architecture in which device drivers are used instead of hardware components. A high precision timer clock is also used to measure the task release times.The results from the experiments show that the release time jitter due to the kernel preemption is found to be fluctuating in the range of -94 and +93 microseconds in 3% of the cases. So, the kernel preemption time for the hard and mission critical real time systems is taken to be less than 99 microseconds. Whereas 85% of the cases have release time jitters of single digit only. So, the kernel preemption time in average case is considered to be less than 9 microseconds.

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Optimal priority assignment for real-time systems: a coevolution-based approach

et al. 2022

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In real-time systems, priorities assigned to real-time tasks determine the order of task executions, by relying on an underlying task scheduling policy. Assigning optimal priority values to tasks is critical to allow the tasks to complete their executions while maximizing safety margins from their specified deadlines. This enables real-time systems to tolerate unexpected overheads in task executions and still meet their deadlines. In practice, priority assignments result from an interactive process between the development and testing teams. In this article, we propose an automated method that aims to identify the best possible priority assignments in real-time systems, accounting for multiple objectives regarding safety margins and engineering constraints. Our approach is based on a multi-objective, competitive coevolutionary algorithm mimicking the interactive priority assignment process between the development and testing teams. We evaluate our approach by applying it to six industrial systems from different domains and several synthetic systems. The results indicate that our approach significantly outperforms both our baselines, i.e., random search and sequential search, and solutions defined by practitioners. Our approach scales to complex industrial systems as an offline analysis method that attempts to find near-optimal solutions within acceptable time, i.e., less than 16 hours.

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Optimal priority assignment for real-time systems: a coevolution-based approach

Cited by 4 publications

References 76 publications

PSRL: A New Method for Real-Time Task Placement and Scheduling Using Reinforcement Learning

PSRL: A New Method for Real-Time Task Placement and Scheduling Using Reinforcement Learning

Measurement of Kernel Preemption Time of Real Time Operating Systems

Optimal priority assignment for real-time systems: a coevolution-based approach

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