Period Selection for Minimal Hyperperiod in Periodic Task Systems

Ripoll, Ismael; Ballester-Ripoll, Rafael

doi:10.1109/tc.2012.243

Cited by 38 publications

(17 citation statements)

References 17 publications

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“…Similarly, while computing periods for a given tasks' set, different approaches for reducing the hyper-period are presented in [29,40]. However, the underlying assumption for the validity of such research is that given tasks are executed over a uni-processor platform independently.…”

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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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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“…Harmonic task periods have some potential benefits, both for scheduling analysis and for control system design [8], [9]. The hyperperiod of a harmonic task set is finite and small [10], [11]. RM scheduling of harmonic tasks is feasible if the utilization is less than or equal to 1, e.g., [12].…”

Section: Related Workmentioning

confidence: 99%

Harmonic Scheduling and Control Co-design

Xu¹,

Cervin²,

Årzén³

2016

2016 IEEE 22nd International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA)

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Harmonic task scheduling has many attractive properties, including a utilization bound of 100% under ratemonotonic scheduling and reduced jitter. At the same time, it places a severe constraint on the task period assignment for any application. In this paper, we explore the use of harmonic task scheduling for applications with multiple feedback control tasks. We present algorithm for finding harmonic task periods: to minimizes the distance from an initial set of non-harmonic periods. We apply the algorithm in a scheduling and control co-design procedure, where the goal is to optimize the total performance of a number of control tasks that share a common computing platform. The procedure is evaluated in simulated randomized examples, where it is shown that, in general, harmonic scheduling combined with release offsets gives better control performance than standard, non-harmonic scheduling.

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“…The schedule of partitions repeats every major cycle. In the case that the complexity (size) of the schedule would be an issue, a technique to reduce MC, such as one proposed in[25], could be applied to reduce the complexity (size) of the schedule 7. Although there are a large number of symbols used, this is a consequence of the richness of the scheduling model (i.e., the number of relevant parameters for which we will derive values).…”

mentioning

confidence: 99%

Integrated Modular Avionics (IMA) Partition Scheduling with Conflict-Free I/O for Multicore Avionics Systems

Kim

Yoon

Bradford³

et al. 2014

2014 IEEE 38th Annual Computer Software and Applications Conference

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The trend in the semiconductor industry toward multicore processors poses a significant challenge to many suppliers of safety-critical real-time embedded software. Having certified their systems for use on single-core processors, these companies may be forced to migrate their installed base of software onto multicore processors as single-core processors become harder to obtain. These companies naturally want to minimize the potentially high costs of recertifying their software for multicore processors. In support of this goal, we propose an approach to solving a fundamental problem in migrating legacy software applications to multicore systems, namely that of preventing conflicts among I/O transactions from applications residing on different cores. We formalize the problem as a partition scheduling problem that serializes I/O partitions. Although this problem is strongly NP-complete, we formulate it as a Constraint Programming (CP) problem. Since the CP approach scales poorly, we propose a heuristic algorithm that outperforms the CP approach in scalability.

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Period Selection for Minimal Hyperperiod in Periodic Task Systems

Cited by 38 publications

References 17 publications

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

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

Harmonic Scheduling and Control Co-design

Integrated Modular Avionics (IMA) Partition Scheduling with Conflict-Free I/O for Multicore Avionics Systems

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