Efficient online schedulability tests for real-time systems

Kuo, Tei-Wei; Chang, Li-Pin; Liu, Yuhua; Lin, Kwei-Jay

doi:10.1109/tse.2003.1223647

Cited by 24 publications

(3 citation statements)

References 28 publications

(140 reference statements)

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“…Refined sufficient tests have been developed (Han 1998;Baruah et al 1999b;wei Kuo et al 2003;Lu et al 2007) with less pessimism than the test using the utilization bound in (5). They generally also allow certain task sets of higher utilization than those passing the above test to be classified as schedulable.…”

Section: Schedulability Analysismentioning

confidence: 99%

Graph-based models for real-time workload: a survey

Stigge

2015

Real-Time Syst

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This paper provides a survey on task models to characterize real-time workloads at different levels of abstraction for the design and analysis of real-time systems. It covers the classic periodic and sporadic models by Liu and Layland et al., their extensions to describe recurring and branching structures as well as general graph-and automata-based models to allow modeling of complex structures such as mode switches, local loops and also global timing constraints. The focus is on the precise semantics of the various models and on the solutions and complexity results of the respective feasibilty and schedulability analysis problems for preemptable uniprocessors.

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Section: Schedulability Analysismentioning

confidence: 99%

Graph-based models for real-time workload: a survey

Stigge

2015

Real-Time Syst

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“…Feasibility tests for these models are based on demand-bound functions (Baruah et al 1999). Static priority schedulability tests generalize response time analysis (Zuhily and Burns 2009;Takada and Sakamura 1997) or utilization bounds (Mok and Chen 1997;Han 1998;wei Kuo et al 2003;Lu et al 2007). These tests however are either imprecise, i.e., over-approximate, or very slow because of exponential explosion in complexity.…”

Section: Prior Workmentioning

confidence: 99%

Combinatorial Abstraction Refinement for Feasibility Analysis

Stigge

2013

2013 IEEE 34th Real-Time Systems Symposium

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Combinatorial explosion is a challenge for many analysis problems in the theory of hard real-time systems. One of these problems is static priority schedulability of workload models which are more expressive than the traditional periodic task model. Different classes of directed graphs have been proposed in recent years to model structures like frames, branching and loops. In contrast to dynamic priority schedulers with pseudo-polynomial time analysis methods, static priority schedulability has been shown to be intractable since it is strongly coNP-hard already for the relatively simple class of cyclic digraphs. The core of this problem is the necessity to combine different behaviors of the participating tasks. We introduce a novel iterative approach to efficiently cope with this combinatorial explosion, called combinatorial abstraction refinement. In combination with other techniques it significantly reduces exponential growth of run-time for most inputs. We apply the method to static task priorities and demonstrate that a prototype implementation outperforms the state-of-the art pseudo-polynomial analysis for dynamic priority feasibility. It further shows better scaling behavior for randomly generated problem instances. We extend the approach to non-preemptive schedulers as well as static job-type priorities where jobs of different types in the same task may be assigned different static priorities. Finally, we provide a general, abstract formulation of the algorithm, since we believe that this method can be applicable to a variety of combinatorial problems in the theory of real-time systems with certain abstraction structures.

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“…Form system utilization perspective, preemptive scheduling is preferred over non-preemptive counterpart. Various scheduling techniques have been proposed for real-time system (Liu and Layland, 1973;Leung and Whitehead, 1982;George et al, 1996;Katcher et al, 1993;Lehoczky et al, 1989;Bini and Buttazzo, 2001;Han and Tyan, 1997;Kuo et al, 2003;Audsley et al, 1993;Sjodin and Hansson, 1998) that ensures the timing requirements are met by prioritizing task executions running on the system. For instance, Rate Monotonic Scheduling (RMS) (Liu and Layland, 1973) strategy assigns priority by task activation rate while Deadline Monotonic Scheduling (DMS) (Leung and Whitehead, 1982) algorithm assigns priorities based on tasks deadlines.…”

Section: Introductionmentioning

confidence: 99%

Integrating Lowest Priority Approach with Largest Point Scheme for Faster Feasibility Analysis

Min‐Allah¹

2019

Journal of Computer Science

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Recently many solutions have been proposed to lower the computational cost of feasibility analysis for real-time systems. The computational cost of feasibility tests can be lowered by strategies such as lowering the number of scheduling points needed during analysis, starting feasibility analysis from lowest priority, or starting schedulability tests for a task with larger scheduling point. All these techniques significantly reduce the computation time of feasibility analysis for fixed priority systems. The computation time of such tests can be further reduced by combining various solutions for efficient feasibility analysis of periodic task sets. In this work, we integrate both lowest priority first with largest points first solution to derive a faster feasibility analysis test for fixed priority system. Our experimental evaluations suggest that the proposed technique significantly lowers the computational cost of the test when system utilization is in the range of 80% or when the ratio between the task period of a lower priority task and the highest priority task is large.

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Efficient online schedulability tests for real-time systems

Cited by 24 publications

References 28 publications

Graph-based models for real-time workload: a survey

Graph-based models for real-time workload: a survey

Combinatorial Abstraction Refinement for Feasibility Analysis

Integrating Lowest Priority Approach with Largest Point Scheme for Faster Feasibility Analysis

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