Control-Scheduling Codesign Exploiting Trade-Off between Task Periods and Deadlines

Cha, Hyun-Jun; Jeong, Woo-Hyuk; Kim, Jong-Chan

doi:10.1155/2016/3414816

Cited by 12 publications

(12 citation statements)

References 12 publications

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“…Bini et al proposed the branch-bound search based task scheduler with fixed priorities in order to maximize the system performance without violation of deadlines [18]. Likewise, Hyun-Jun Cha et al developed a deadline monotonic task scheduler for optimality of deadline and period constraints in automotive systems [19].…”

Section: Conventional Scheduling Algorithms On Real-time Embedded Iot Systemsmentioning

confidence: 99%

An Intelligent Task Scheduling Mechanism for Autonomous Vehicles via Deep Learning

2021

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With the rapid development of the Internet of Things (IoT) and artificial intelligence, autonomous vehicles have received much attention in recent years. Safe driving is one of the essential concerns of self-driving cars. The main problem in providing better safe driving requires an efficient inference system for real-time task management and autonomous control. Due to limited battery life and computing power, reducing execution time and resource consumption can be a daunting process. This paper addressed these challenges and developed an intelligent task management system for IoT-based autonomous vehicles. For each task processing, a supervised resource predictor is invoked for optimal hardware cluster selection. Tasks are executed based on the earliest hyper period first (EHF) scheduler to achieve optimal task error rate and schedule length performance. The single-layer feedforward neural network (SLFN) and lightweight learning approaches are designed to distribute each task to the appropriate processor based on their emergency and CPU utilization. We developed this intelligent task management module in python and experimentally tested it on multicore SoCs (Odroid Xu4 and NVIDIA Jetson embedded platforms).Connected Autonomous Vehicles (CAV) and Internet of Medical Things (IoMT) benchmarks are used for training and testing purposes. The proposed modules are validated by observing the task miss rate, resource utilization, and energy consumption metrics compared with state-of-art heuristics. SLFN-EHF task scheduler achieved better results in an average of 98% accuracy, and in an average of 20–27% reduced in execution time and 32–45% in task miss rate metric than conventional methods.

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Section: Conventional Scheduling Algorithms On Real-time Embedded Iot Systemsmentioning

confidence: 99%

An Intelligent Task Scheduling Mechanism for Autonomous Vehicles via Deep Learning

2021

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“…Cha et al [58] propose a method for scheduling of control tasks which determines the deadline and period of the tasks for achieving maximum QoC. The method optimizes the QoC of the control applications and resource utilization.…”

Section: Related Workmentioning

confidence: 99%

Performance Optimization of Control Applications on Fog Computing Platforms Using Scheduling and Isolation

2020

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“…The time complexity of their proposed system is deterministic, and thus the proposed method is appropriate for online applications. More recently, Cha et al [37] developed a searching-based heuristic scheme to compute near-optimal values for feasible task periods such that the overall performance of the system is maximised. The time complexity of their proposed algorithm is linear and hence suitable for online use.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Approach Based on Resource-Awareness Towards Power-Efficient Real-Time Periodic Task Modeling on Embedded IoT Devices

et al. 2018

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Embedded devices are gaining popularity day by day due to the expanded use of Internet of Things applications. However, these embedded devices have limited capabilities concerning power and memory. Thus, the applications need to be tailored in such a way to perform the specified tasks within the constrained resources with the same accuracy. In Real-Time task scheduling, one of the challenging factors is the intelligent modelling of input tasks in such a way that it produces not only logically correct output within the deadline but also consumes minimum CPU power. Algorithms like Rate Monotonic and Earliest Deadline First compute hyper-period of input tasks for periodic repetition of the same set of tasks on CPU. However, at times when the tasks are not adequately modelled, they lead to an enormously high value of hyper-period which result in more CPU cycles and power consumption. Many state-of-the-art solutions are presented in this regard, but the main problem is that they limit tasks from having all possible period values; however, with the vision of Industry 4.0, where most of the tasks will be doing some critical manufacturing activities, it is highly discouraged to prevent them of a certain period. In this paper, we present a resource-aware approach to minimise the hyper-period of input tasks based on device profiles and allows tasks of every possible period value to admit. The proposed work is compared with similar existing techniques, and results indicate significant improvements regarding power consumptions.

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Control-Scheduling Codesign Exploiting Trade-Off between Task Periods and Deadlines

Cited by 12 publications

References 12 publications

An Intelligent Task Scheduling Mechanism for Autonomous Vehicles via Deep Learning

An Intelligent Task Scheduling Mechanism for Autonomous Vehicles via Deep Learning

Performance Optimization of Control Applications on Fog Computing Platforms Using Scheduling and Isolation

An Adaptive Approach Based on Resource-Awareness Towards Power-Efficient Real-Time Periodic Task Modeling on Embedded IoT Devices

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