Preemption Threshold Scheduling: Stack Optimality, Enhancements and Analysis

Ghattas, Rony; Dean, Amanda

doi:10.1109/rtas.2007.27

Cited by 28 publications

(28 citation statements)

References 16 publications

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“…With FPPS these run-time overheads cannot be resolved analytically. An important disadvantage of FPPS therefore remains that arbitrary pre-emptions during execution may lead to inefficient memory use and high run-time overheads (Gai et al 2001;Ghattas and Dean 2007).…”

Section: Background and Motivationmentioning

confidence: 99%

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Fixed priority scheduling with pre-emption thresholds and cache-related pre-emption delays: integrated analysis and evaluation

et al. 2017

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Commercial off-the-shelf programmable platforms for real-time systems typically contain a cache to bridge the gap between the processor speed and main memory speed. Because cache-related pre-emption delays (CRPD) can have a significant influence on the computation times of tasks, CRPD have been integrated in the response time analysis for fixed-priority pre-emptive scheduling (FPPS). This paper presents CRPD aware response-time analysis of sporadic tasks with arbitrary deadlines for fixed-priority pre-emption threshold scheduling (FPTS), generalizing earlier work. The analysis is complemented by an optimal (pre-emption) threshold assignment algorithm, assuming the priorities of tasks are given. We further improve upon these results by presenting an algorithm that searches for a layout of tasks in memory that makes a task set schedulable. The paper includes an extensive comparative evaluation of the schedulability ratios of FPPS and FPTS, taking CRPD into account. The practical relevance of our work stems from FPTS support in AUTOSAR, a standardized development model for the automotive industry. Keywords Fixed-priority pre-emptive scheduling · Fixed-priority scheduling with pre-emption thresholds · Cache-related pre-emption delay · Response-time analysis B Reinder J. Bril

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Section: Background and Motivationmentioning

confidence: 99%

“…This scheme of fixedpriority scheduling with pre-emptions thresholds (FPTS) has been shown to greatly reduce the memory footprint of concurrent task systems (Gai et al 2001) and reduce the average case response times of tasks (Ghattas and Dean 2007). Currently, FPTS is therefore already adopted by industry.…”

Section: Background and Motivationmentioning

confidence: 99%

Fixed priority scheduling with pre-emption thresholds and cache-related pre-emption delays: integrated analysis and evaluation

et al. 2017

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“…In [8] a unified framework for static and dynamic priority systems with the definition of preemption thresholds is presented. The authors demonstrate that the algorithm in [13] for the assignment of the highest possible preemption thresholds after priorities are assigned to tasks is also optimal with respect to stack usage (again, with the assumption of a given priority assignment).…”

Section: State Of the Artmentioning

confidence: 99%

Optimizing stack memory requirements for real-time embedded applications

Zeng

Natale

Zhu

2012

Proceedings of 2012 IEEE 17th International Conference on Emerging Technologies &Amp; Factory Automation (ETFA 2012)

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In the development of some real-time embedded applications, especially systems-on-chip, an efficient use of RAM memory is as important as the effective scheduling of the computation resources. The design problem is to find a schedulable solution that fits within the memory budget. In a real-time concurrent system, preemption plays an important role in the exploration of these tradeoffs. Several schemes, including preemption thresholds and non-preemption groups, have been developed to improve schedulability and saving stack memory space by selectively disabling preemption. However, the design synthesis problem for such systems and protocols is still an open problem. We target at the efficient assignment of the scheduling parameters for systems scheduled according to these policies in several cases of practical interest, including those that are compliant with automotive standards.

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“…Gai et al (2001) propose the stack resource policy with preemption thresholds (SRPT), a method to uniformly deal with the blocking due to tasks with higher preemption thresholds or to tasks accessing shared resources. Ghattas and Dean (2007) propose a framework that applies to both static-priority and dynamic-priority scheduling, optimizing the selection of preemption thresholds for systems with limited memory and reduced stack size. This paper differs from previous works on preemption thresholds in several ways.…”

Section: Relation To Prior Workmentioning

confidence: 99%

Resource holding times: computation and optimization

2008

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In scheduling hard-real-time systems, the primary objective is to meet all deadlines. We study the scheduling of such systems with the secondary objective of minimizing the duration of time for which the system locks each shared resource. We abstract out this objective into the resource hold time (RHT)-the largest length of time that may elapse between the instant that a system locks a resource and the instant that it subsequently releases the resource, and study properties of the RHT. We present an algorithm for computing resource hold times for every resource in a task system that is scheduled using Earliest Deadline First scheduling, with resource access arbitrated using the Stack Resource Policy. We also present and prove the correctness of algorithms for decreasing these RHT's without changing the semantics of the application or compromising application feasibility.

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Preemption Threshold Scheduling: Stack Optimality, Enhancements and Analysis

Abstract: Using preemption threshold scheduling (PTS)

Cited by 28 publications

References 16 publications

Fixed priority scheduling with pre-emption thresholds and cache-related pre-emption delays: integrated analysis and evaluation

Fixed priority scheduling with pre-emption thresholds and cache-related pre-emption delays: integrated analysis and evaluation

Optimizing stack memory requirements for real-time embedded applications

Resource holding times: computation and optimization

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