Preemption Point Selection in Limited Preemptive Scheduling using Probabilistic Preemption Costs Filip Marković, Jan Carlson, Radu Dobrin Mälardalen Real-Time Research Centre, Dept. of Computer Science and Software Engineering, Mälardalen University, Sweden
Limited Preemptive Scheduling
Limited Preemptive Scheduling ● An attractive scheduling paradigm instead of fully-preemptive and non-preemptive scheduling. ● Enables control of preemption related overheads , thus reducing their impact on schedulability.
Limited Preemptive Scheduling ● An attractive scheduling paradigm instead of fully-preemptive and non-preemptive scheduling. ● Enables control of preemption related overheads , thus reducing their impact on schedulability. ● Fixed Preemption Points ● Preemption is allowed only at predefined selected locations inside the code, called preemption points.
Limited Preemptive Scheduling ● An attractive scheduling paradigm instead of fully-preemptive and non-preemptive scheduling. ● Enables control of preemption related overheads , thus reducing their impact on schedulability. ● Fixed Preemption Points ● Preemption is allowed only at predefined selected locations inside the code, called preemption points. ℎ𝑗ℎ𝑓𝑠 𝑄 𝜐 " 𝜐 # 𝑚𝑝𝑥𝑓𝑠 𝑄 preemption point
Limited Preemptive Scheduling ● An attractive scheduling paradigm instead of fully-preemptive and non-preemptive scheduling. ● Enables control of preemption related overheads , thus reducing their impact on schedulability. ● Fixed Preemption Points ● Preemption is allowed only at predefined selected locations inside the code, called preemption points. ℎ𝑗ℎ𝑓𝑠 𝑄 𝜐 " 𝜐 # 𝑚𝑝𝑥𝑓𝑠 𝑄 preemption point
Limited Preemptive Scheduling ● An attractive scheduling paradigm instead of fully-preemptive and non-preemptive scheduling. ● Enables control of preemption related overheads , thus reducing their impact on schedulability. ● Fixed Preemption Points ● Preemption is allowed only at predefined selected locations inside the code, called preemption points. ℎ𝑗ℎ𝑓𝑠 𝑄 𝜐 " 𝜐 # 𝑚𝑝𝑥𝑓𝑠 𝑄 preemption point
Limited Preemptive Scheduling ● An attractive scheduling paradigm instead of fully-preemptive and non-preemptive scheduling. ● Enables control of preemption related overheads , thus reducing their impact on schedulability. ● Fixed Preemption Points ● Preemption is allowed only at predefined selected locations inside the code, called preemption points. ℎ𝑗ℎ𝑓𝑠 𝑄 𝜐 " 𝜐 # 𝑚𝑝𝑥𝑓𝑠 𝑄 preemption point
Limited Preemptive Scheduling ● An attractive scheduling paradigm instead of fully-preemptive and non-preemptive scheduling. ● Enables control of preemption related overheads , thus reducing their impact on schedulability. ● Fixed Preemption Points ● Preemption is allowed only at predefined selected locations inside the code, called preemption points. ℎ𝑗ℎ𝑓𝑠 𝑄 𝜐 " 𝜐 # 𝑚𝑝𝑥𝑓𝑠 𝑄 preemption point
Limited Preemptive Scheduling ● An attractive scheduling paradigm instead of fully-preemptive and non-preemptive scheduling. ● Enables control of preemption related overheads , thus reducing their impact on schedulability. ● Fixed Preemption Points ● Preemption is allowed only at predefined selected locations inside the code, called preemption points. ℎ𝑗ℎ𝑓𝑠 𝑄 𝜐 " 𝜐 # 𝑚𝑝𝑥𝑓𝑠 𝑄 preemption point
Motivation • The existing selection methods account for upper bounded preemption overheads , thus introducing a potentially high level of pessimism in the results.
Motivation • The existing selection methods account for upper bounded preemption overheads , thus introducing a potentially high level of pessimism in the results. 𝜐 " ℎ𝑗ℎ𝑓𝑠 𝑄 𝜐 # 𝑚𝑝𝑥𝑓𝑠 𝑄 Preemption overhead
Motivation • The existing selection methods account for upper bounded preemption overheads , thus introducing a potentially high level of pessimism in the results. 𝜐 " ℎ𝑗ℎ𝑓𝑠 𝑄 𝜐 # 𝑚𝑝𝑥𝑓𝑠 𝑄 Preemption overhead
Motivation • The existing selection methods account for upper bounded preemption overheads , thus introducing a potentially high level of pessimism in the results. 𝜐 " ℎ𝑗ℎ𝑓𝑠 𝑄 preemption overhead 𝜐 # 𝑚𝑝𝑥𝑓𝑠 𝑄 Preemption overhead
Motivation • The existing selection methods account for upper bounded preemption overheads , thus introducing a potentially high level of pessimism in the results. 𝜐 " ℎ𝑗ℎ𝑓𝑠 𝑄 preemption overhead deadline miss 𝜐 # 𝑚𝑝𝑥𝑓𝑠 𝑄 Preemption overhead
Motivation • The existing selection methods account for upper bounded preemption overheads , thus introducing a potentially high level of pessimism in the results. 𝜐 " ℎ𝑗ℎ𝑓𝑠 𝑄 preemption overhead deadline miss 𝜐 # 𝑚𝑝𝑥𝑓𝑠 𝑄 Preemption overhead • Can we reduce the pessimism by considering probabilistic information about overheads?
Contributions ● We propose a probabilistic distribution model of overheads and preemption point selection method which provides controllable probabilistic relaxations .
Contributions ● We propose a probabilistic distribution model of overheads and preemption point selection method which provides controllable probabilistic relaxations . 𝜐 #
Contributions ● We propose a probabilistic distribution model of overheads and preemption point selection method which provides controllable probabilistic relaxations . Preemption overhead 𝜐 #
Contributions ● We propose a probabilistic distribution model of overheads and preemption point selection method which provides controllable probabilistic relaxations . upper bound preemption overhead 𝜐 #
Contributions ● We propose a probabilistic distribution model of overheads and preemption point selection method which provides controllable probabilistic relaxations . upper bound empirical samples preemption overhead of preemption overheads 𝜐 #
Contributions ● We propose a probabilistic distribution model of overheads and preemption point selection method which provides controllable probabilistic relaxations . probability probability density function upper bound empirical samples preemption overhead of preemption overheads 𝜐 #
Preemption Point Selection Algorithm
Preemption Point Selection Algorithm ● Input ● Task set with potential preemption points ● Associated probabilistic overhead distributions
Preemption Point Selection Algorithm ● Input ● Task set with potential preemption points ● Associated probabilistic overhead distributions ● Output ● Selected preemption points
Preemption Point Selection Algorithm ● Input ● Task set with potential preemption points ● Associated probabilistic overhead distributions ● Output ● Selected preemption points ● Algorithm ● Gradually decreases probabilistic factor for preemption overheads in order to find preemption point selection
Preemption Point Selection Algorithm ● Input iteration focused overhead implies selection probability of a deadline miss of different points ( part of the future work ) ● Task set with potential preemption points 0 1 ● Associated probabilistic 1 overhead distributions. ● Output ● Selected preemption points 0 1 ● Algorithm 2 ● Gradually decreases probabilistic factor for preemption overheads in order to find preemption point 0 1 selection. 3 27
Preemption Point Selection Algorithm ● Input iteration focused overhead implies selection probability of a deadline miss of different points ( part of the future work ) ● Task set with potential preemption points 0 1 ● Associated probabilistic 1 overhead distributions. ● Output ● Selected preemption points 0 1 ● Algorithm 2 ● Gradually decreases probabilistic factor for preemption overheads in order to find preemption point 0 1 selection. 3 28
Preemption Point Selection Algorithm ● Input iteration focused overhead implies selection probability of a deadline miss of different points ( part of the future work ) ● Task set with potential preemption points 0 1 ● Associated probabilistic 1 overhead distributions. ● Output ● Selected preemption points 0 1 ● Algorithm 2 ● Gradually decreases probabilistic factor for preemption overheads in order to find preemption point 0 1 selection. 3 29
Preliminary results ● Goal of the experiment : To investigate to what extent the relaxation of the considered overheads facilitates finding solutions to the preemption point selection problem. 100 Task sets for which a selection is found (%) Upper bounds Quantile selection 80 60 40 20 0 0.8 0.82 0.84 0.86 0.88 0.9 0.92 0.94 0.96 0.98 1 Utilisation
Summary and Future work ● Contributions ● Probabilistic overhead model ● Preemption point selection based on probabilistic overhead distributions ● Future work ● Probabilistic schedulability analysis techniques for tasks with fixed preemption points and associated probabilistic overheads ● Novel preemption point selection strategies to maximize schedulability
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