scholarly journals Analysis Of Time Triggered Schedulers In Embedded System

Author(s):  
E. Anbarasi ◽  
N. karthik

In embedded software, scheduler is one of the essential component. It is essential to schedule the task in a suitable way to achieve the system optimization. Time triggered scheduling approaches render a reliable performance for real time systems. This paper sketches on time triggered schedulers which tends to results in highly predictable system behavior. Despite many scheduling approaches, time triggered cooperative and time triggered hybrid schedulers have more attention in resource constrained embedded system which works on single processor. Our target of this work is to analyze these time triggered schedulers’ performances. We evaluated and studied the performance of those time triggered schedulers with various parameters. The results have been incurred by implementation of schedulers in embedded c programming language with simulation tool.

2012 ◽  
Vol 468-471 ◽  
pp. 60-63
Author(s):  
Xiao Fan Wu ◽  
Jia Jun Bu ◽  
Chun Chen

Due to the rapid development of Internet of Things (IoT), kinds of sensor nodes have been introduced to the different applications. Because of the variety of MCUs, sensors and radio modules, it’s challenging to reuse the device drivers between different sensor node platforms. To address this issue, a reusable device driver framework is proposed in this paper. Comparing with existed work, our framework is flexible, efficient, and easy to learn. The flexibility is achieved by layered encapsulation, which decouples the device driver with the sensor node operating system kernel. Our framework gives the reusability at the source code level, so it’s efficient. At the end, our framework is implemented in C programming language, which is the most common tool adopted by embedded system developing. This framework has applied to SenSpire OS, a micro-kernel real-time operating system for IoT sensor nodes.


Author(s):  
Ibrahim Gharbi ◽  
Hamza Gharsellaoui ◽  
Sadok Bouamama

This journal article deals with the problem of real-time scheduling of operating systems (OS) tasks by a hybrid genetic-based scheduling algorithm. Indeed, most of real-time systems are framed with aid of priority-based scheduling algorithms. 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. In contrast, most of the applications of real-time systems are based on timing constraints, i.e. OS tasks should be scheduled properly to finish their execution within the time specified by the real-time systems. For this reason, the authors propose in their article, a hybrid genetic-based scheduling approach that automatically checks the systems feasibility after any reconfiguration scenario was applied to an embedded system. A benchmark example is given, and the experimental results demonstrate the effectiveness of the originally proposed genetic-based scheduling approach over other such classical genetic algorithmic approaches.


Author(s):  
Erfansyah Ali ◽  
Andriyan B Suksmono

One of methods in remote sensing is Synthetic Aperture Radar (SAR). When combined with Range DopplerAlgorithm (RDA) can produce smaller radar resolution only by using normal sized antenna placed atplatform. RDA is able to generate much wider aperture �synthetic� antenna, resulting very narrow beamwidthwhen reach earth's ground. By using already established 2D SAR methods in accuracy andprocessing speed this 3D SAR simulation was developed. Simulated on 15 x 15 pixels grayscale targets atdifferent heights, 3D SAR developed on this research can detect object's height accurately. Thissimulation was developed using JAVA as steppingstone in implementing SAR image processing in smallsystem like embedded system or micro computing which normally using C programming language.


Author(s):  
Hesham Hussien ◽  
Eman Shaaban ◽  
Said Ghoniemy

The complexity of embedded real-time systems has increased, and most applications have large diversity in execution times of their tasks. Therefore, most traditional scheduling techniques do not satisfy requirements of such applications. This article proposes an adaptive hierarchical scheduling framework for a set of independent concurrent applications composing of soft and hard real time tasks, that run on a single processor. It ensures temporal partitioning between independent applications with budget adaption feature, where CPU time of each application is periodically and dynamically assigned. Implemented in the kernel of TI-RTOS on a resource constrained platform, experiments show that proposed scheme provides good performance for multiple applications with dynamic tasks under overload conditions. Compared with traditional priority scheduler originally implemented in TI-RTOS and EDF scheduler, it achieves low miss ratio with minimal overhead while yielding temporal partitioning.


Author(s):  
Ibrahim Gharbi ◽  
Hamza Gharsellaoui ◽  
Sadok Bouamama

This journal article deals with the problem of real-time scheduling of operating systems (OS) tasks by a hybrid genetic-based scheduling algorithm. Indeed, most of real-time systems are framed with aid of priority-based scheduling algorithms. 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. In contrast, most of the applications of real-time systems are based on timing constraints, i.e. OS tasks should be scheduled properly to finish their execution within the time specified by the real-time systems. For this reason, the authors propose in their article, a hybrid genetic-based scheduling approach that automatically checks the systems feasibility after any reconfiguration scenario was applied to an embedded system. A benchmark example is given, and the experimental results demonstrate the effectiveness of the originally proposed genetic-based scheduling approach over other such classical genetic algorithmic approaches.


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