Neural Feedback Scheduling of Real-Time Control Tasks

📝 Abstract

Many embedded real-time control systems suffer from resource constraints and dynamic workload variations. Although optimal feedback scheduling schemes are in principle capable of maximizing the overall control performance of multitasking control systems, most of them induce excessively large computational overheads associated with the mathematical optimization routines involved and hence are not directly applicable to practical systems. To optimize the overall control performance while minimizing the overhead of feedback scheduling, this paper proposes an efficient feedback scheduling scheme based on feedforward neural networks. Using the optimal solutions obtained offline by mathematical optimization methods, a back-propagation (BP) neural network is designed to adapt online the sampling periods of concurrent control tasks with respect to changes in computing resource availability. Numerical simulation results show that the proposed scheme can reduce the computational overhead significantly while delivering almost the same overall control performance as compared to optimal feedback scheduling.

💡 Analysis

Many embedded real-time control systems suffer from resource constraints and dynamic workload variations. Although optimal feedback scheduling schemes are in principle capable of maximizing the overall control performance of multitasking control systems, most of them induce excessively large computational overheads associated with the mathematical optimization routines involved and hence are not directly applicable to practical systems. To optimize the overall control performance while minimizing the overhead of feedback scheduling, this paper proposes an efficient feedback scheduling scheme based on feedforward neural networks. Using the optimal solutions obtained offline by mathematical optimization methods, a back-propagation (BP) neural network is designed to adapt online the sampling periods of concurrent control tasks with respect to changes in computing resource availability. Numerical simulation results show that the proposed scheme can reduce the computational overhead significantly while delivering almost the same overall control performance as compared to optimal feedback scheduling.

📄 Content

arXiv:0805.3062v1 [cs.OH] 20 May 2008 To appear in International Journal of Innovative Computing, Information and Control NEURAL FEEDBACK SCHEDULING OF REAL-TIME CONTROL TASKS Feng Xia1,3, Yu-Chu Tian1,∗, Youxian Sun2 and Jinxiang Dong3 1Faculty of Information Technology Queensland University of Technology GPO Box 2434, Brisbane QLD 4001, Australia {f.xia, y.tian}@qut.edu.au ∗Corresponding author 2State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou 310027, P. R. China 3College of Computer Science and Technology Zhejiang University Hangzhou 310027, P. R. China Received August 2007; revised December 2007 Abstract. Many embedded real-time control systems suffer from resource constraints and dynamic workload variations. Although optimal feedback scheduling schemes are in principle capable of maximizing the overall control performance of multitasking control systems, most of them induce excessively large computational overheads associated with the mathematical optimization routines involved and hence are not directly applicable to practical systems. To optimize the overall control performance while minimizing the over- head of feedback scheduling, this paper proposes an efficient feedback scheduling scheme based on feedforward neural networks. Using the optimal solutions obtained offline by mathematical optimization methods, a back-propagation (BP) neural network is designed to adapt online the sampling periods of concurrent control tasks with respect to changes in computing resource availability. Numerical simulation results show that the proposed scheme can reduce the computational overhead significantly while delivering almost the same overall control performance as compared to optimal feedback scheduling. Keywords: Feedback scheduling, Neural networks, Real-time scheduling, Computa- tional overhead, Embedded control systems

1. Introduction. Embedded control systems have been used in a wide variety of appli- cations. These systems are typically resource constrained due to various technical and economic reasons [1, 2, 3, 4, 5]. In particular, the computing speeds of most embedded processors are limited as compared to general-purpose computers. Also, it is common that multiple control tasks have to compete for the use of one processor. For a real-time em- bedded control system, such resource constraint may affect the system timing behaviour significantly and may even yield unsatisfactory control performance. This problem will be further pronounced when the system operates in dynamic environments where the CPU 1 2 F. XIA, Y.-C. TIAN, Y. SUN AND J. DONG workload varies over time. In the context of resource constraints, these dynamic variations in workload will possibly lead to low CPU utilization and/or system overloading. As a consequence, the performance of a multitasking control system will be jeopardized [4, 6]. Recently, feedback scheduling [1, 4, 7] has emerged as a promising technology for ad- dressing the above mentioned uncertainty in resource availability. The basic idea of feed- back scheduling is to allocate available resources dynamically among multiple real-time tasks based on feedback information about actual resource usage. In multitasking con- trol systems, a straightforward objective of feedback scheduling is to optimize the overall quality of control (QoC) characterized by some sort of performance indices. Accordingly, the problem of feedback scheduling can be formulated as a constrained optimization prob- lem, which is usually referred to as optimal feedback scheduling [1]. In this optimization problem, the total control cost is to be minimized through optimizing scheduling param- eters of control tasks under the constraint of system schedulability. The most popular solution for this optimization problem is based on mathematical optimization algorithms, e.g., [8, 9, 10, 11, 12]. Since feedback schedulers are usually executed at runtime, it is of paramount importance to take into account the computational overhead of the scheduling algorithm to be employed [10, 11]. If the feedback scheduler consumes too much comput- ing resources, the execution of control tasks will inevitably be impacted in the presence of resource constraint. This may then cause significant degradation of the overall QoC. In theory optimal feedback scheduling schemes are effective in optimizing the overall QoC, but optimization solutions typically involve complex computations, which induce large overheads. Therefore, they are not suitable for online use in most cases. To tackle the problem associated with the large computational overheads of optimal feedback scheduling algorithms, a neural feedback scheduling (NFS) scheme will be pro- posed in this paper. The goal is to optimize the overall QoC of multitasking control systems through feedback scheduling while minimizing the scheduling overhead. A feed- forward back-propagation (BP) neural network with a simple structure is adopted to build the feedback

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