A Polynomial-Time Algorithm for Computing Response Time Bounds in Static Priority Scheduling Employing Multi-linear Workload Bounds

Stein, Steffen; Ivers, Matthias; Diemer, Jonas; Ernst, Rolf

doi:10.1109/ecrts.2010.27

Cited by 3 publications

(3 citation statements)

References 9 publications

(30 reference statements)

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“…Some other research (see, e.g., Davis and Burns 2008;Stein et al 2010) has also been directed at obtaining bounds on response time. Polynomial-time approximation schemes (PTAS's) for DM-schedulability analysis have been obtained-see, e.g., Fisher and Baruah (2005), Fisher (2007), Nguyen et al (2009).…”

Section: Introductionmentioning

confidence: 98%

Efficient computation of response time bounds for preemptive uniprocessor deadline monotonic scheduling

Baruah

2011

Real-Time Syst

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The deadline-monotonic (DM) scheduling of sporadic task systems upon a preemptive uniprocessor is considered. A technique is derived for determining upper bounds on the response time of the jobs of each task, when a constrained-deadline sporadic task system is scheduled. This technique yields a generalization to a loadbased sufficient schedulability condition for DM, the generalization being the added ability to account for blocking in the presence of non-preemptable serially re-usable resources.

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Section: Introductionmentioning

confidence: 98%

Efficient computation of response time bounds for preemptive uniprocessor deadline monotonic scheduling

Baruah

2011

Real-Time Syst

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“…In this work, we show how to address the over-allocation problem through a piecewise linear service guarantee. Piecewise linear bounds have been previously used for trafficshaping [6]- [8], as well as computing response time of arbiters [9]. However, these works focus on regulating unbounded requested services to make service guarantee analysis feasible.…”

Section: Introductionmentioning

confidence: 99%

“…A common similarity between these works is their use of traffic-shaping to enforce a certain request arrival curve. Piecewise linear bounds have also been employed for traffic-shaping/policing in [6]- [8], as well as for computing response time of real-time arbiters by bounding resource access requests [9]. However, these traffic shapers are applied to the requested service, whereas our model is a piecewise lower-bound on the provided service.…”

Section: Introductionmentioning

confidence: 99%

Resource-Efficient Real-Time Scheduling Using Credit-Controlled Static-Priority Arbitration

Siyoum

Åkesson

Stuijk

et al. 2011

2011 IEEE 17th International Conference on Embedded and Real-Time Computing Systems and Applications

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Abstract-A present-day System-on-Chip (SoC) runs a wide range of applications with diverse real-time requirements. Resources, such as processors, interconnects and memories, are shared between these applications to reduce cost. Resource sharing causes temporal interference, which must be bounded by a suitable resource arbiter. System-level analysis techniques use the service guarantee of the arbiter to ensure that realtime requirements of these applications are satisfied. A service guarantee that underestimates the minimum service provided by an arbiter results in more allocation of resources than needed to satisfy latency and throughput requirements. For instance, a linear service guarantee cannot accurately capture bursty service provision by many priority-based schedulers, such as Credit-Controlled Static Priority (CCSP) and Priority-Budget Scheduling (PBS). As a result, the timing analysis of these arbiters becomes too pessimistic. This leads to unnecessary cost penalties since some SoC resources, such as SDRAM bandwidth, are scarce and expensive.

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Incorporating data from a primary frequency standard into a time scale

Levine

1997

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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It is difficult to combine data from a primary frequency standard with the time-difference measurements that are usually used as input to most time scales because a primary standard usually operates only occasionally on an irregular schedule and because the fundamental output of a primary frequency standard is a frequency rather than a time, and there is often no natural way of inserting this kind of datum into the scale in a manner that is statistically robust. We will present a new timescale algorithm that seeks to address these problems. We call this frequency-based algorithm AF1, by analogy with the time-based algorithm AT1 that has been used at NIST for many years. Unlike AT1 in which frequency is simply a parameter that specifies how the time of a clock evolves between measurements, however, the frequency of each clock is a fundamental parameter in AF1. This change in focus provides a natural way for incorporating data from a primary frequency standard into the ensemble. We will present the details of the algorithm and results using data from our primary frequency standard, NIST-7.

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A Polynomial-Time Algorithm for Computing Response Time Bounds in Static Priority Scheduling Employing Multi-linear Workload Bounds

Cited by 3 publications

References 9 publications

Efficient computation of response time bounds for preemptive uniprocessor deadline monotonic scheduling

Efficient computation of response time bounds for preemptive uniprocessor deadline monotonic scheduling

Resource-Efficient Real-Time Scheduling Using Credit-Controlled Static-Priority Arbitration

Incorporating data from a primary frequency standard into a time scale

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